diff --git a/.git-blame-ignore-rev b/.git-blame-ignore-rev old mode 100644 new mode 100755 diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml old mode 100644 new mode 100755 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index 7f52cf983..9c1f177ee --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,6 @@ clinicadl/notebooks/ # Mask and other files clinicadl/clinicadl/resources/masks/*.nii clinicadl/clinicadl/resources/masks/*.nii.gz + +# Jean zay +core-rg* \ No newline at end of file diff --git a/.jenkins/Jenkinsfile b/.jenkins/Jenkinsfile old mode 100644 new mode 100755 diff --git a/.pep8speaks.yml b/.pep8speaks.yml old mode 100644 new mode 100755 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml old mode 100644 new mode 100755 diff --git a/.readthedocs.yaml b/.readthedocs.yaml old mode 100644 new mode 100755 diff --git a/CHANGELOG b/CHANGELOG old mode 100644 new mode 100755 index ebd783353..7b996a40e --- a/CHANGELOG +++ b/CHANGELOG @@ -5,6 +5,94 @@ Main changes to this code/ project are documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## ClinicaDL 1.4.0 + +### Fixed + +* Fix `--diagnoses` and `--merged_tsv` option bug in `clinicadl get-labels` +* Fix Pathlib bugs +* Fix `get_tsv_paths` function bug. +* Fix a bug for which it was impossible to use predict without specifying the splits and selection metrics. + +### Changed + +* Changed default batch size to 8 for `clinicadl predict` +* Changed VAEs main class + +### New + +* Add `--n_proc` option in `clinicadl generate` pipelines for parallelization. +* Add `--split` to `clinicadl predict` +* Add new VAE networks. +* Add pytorch function to summarize +* Add `--size_reduction` and `--size_reduction_factor` options to `clinicadl train`, `clinicadl predict` and `clinicadl interpret`. +* Add SSIM2D, SSIM3D metrics for VAE. +* Add `--save_latent_space` option to `clinicadl train` and `clinicadl predict`. +* Add `clinicadl generate trvial_motion` +* Add Data augmentation with torchio + + + +## ClinicaDL 1.3.1 + +### Fixed + +* Fix TypeError when running ClinicaDL. +* Fix `--extract_json` option bug in `clinicadl prepare-data`. +* Fix `clinicadl tsvtools get-labels` error finding `clinica iotools missing-modalities` output. + +### Changed + +* Changed `clinicadl tsvtools get-labels` output directory. + +### New + +* Add `--caps_directory` option in `clinicadl tsvtools get-labels`. +## ClinicaDL 1.3.0 + +### New + +* Add new command `quality-check pet-linear`. +* Add new command `generate hypometabolic`. +* Add new network architecture: `Resnet3D` and `SqueezeExcitationCNN`. +* Add `flair-linear` modality for `prepare-data` command. +* Add pytorch profiler. +* Add `--save_nifti` option for `interpret`command. +* Add `--output_dir` argument for `tsvtools get-labels` command + +### Changed + +** Core: ** + +* Transition from os to pathlib. +* Update data CI. +* Improve maps_manager. +* Change `--acq_label` option for `--tracer`. +* Update tutorial. + + +## ClinicaDL 1.2.0 + +### Changed + +** Core: ** + +* Add ClinicaDL installation with pipx. +* Improve logging. +* Add method argument to the interpret command to choose between the new Grad-CAM method and the gradient method. +* Change `extract` command to `prepare-data`. +* Change output of `get-labels`, `split` and `kfold` commands to one TSV per split instead of one per label. +* Change `tsvtool` command to `tsvtools`. +* Change `tsvtools getlabels` command to `tsvtools get-labels` and remove the progression column in the TSV output. +* Add new commands: `tsvtools get-progression`, `tsvtools get-metadata`, `tsvtools prepare-experiment` (split + kfold), and `tsvtools adapt`. +* Update data CI. +* Add a new model for quality check + +### Fixed + +- Fix `quality-check t1-linear` + + ## ClinicaDL 1.1.1 ### Changed @@ -18,7 +106,7 @@ greater than 3.8. sub-command (before `save_tensor` was also subcommand`). - Update some paper references in the documentation. - Add a description for the available models. -- Add the flag `--model_layers` to the `clinicadl train list_models` subcommad +- Add the flag `--model_layers` to the `clinicadl train list_models` subcommand to visualize the model layers. ### Fixed @@ -84,7 +172,7 @@ Print help if no argument is given. ### Fixed Fix label code generation -Remove all occurences of a group in a MAPS when choosing the `overwriting` option. +Remove all occurrences of a group in a MAPS when choosing the `overwriting` option. Fix `resume` when the training.tsv file is empty. ## ClinicaDL 1.0.3 (release bugfix) @@ -108,7 +196,7 @@ Fix `resume` when the training.tsv file is empty. - Use the GPU option on the command line interface. - Fix the train task when invoking the CLI. -- Remplace the `predict` arguments passed through the CLI. +- Replace the `predict` arguments passed through the CLI. - Fix issue when using multiple cohorts. ## ClinicaDL 1.0.1 @@ -135,12 +223,12 @@ introduction of [Click](https://click.palletsprojects.com/en/8.0.x/) library. Major changes in several pipelines interfaces have been implemented. For instance the preprocessing pipeline has been split. Train pipeline has also been refactored to reduce the number of options. The goal is to make ClinicaDL -more easy to use, to maintain, and adapt the command line to the MAPS. +easier to use, to maintain, and adapt the command line to the MAPS. ClinicaDL aim for more reproducibility: some configuration files are saved in the MAPS to reproduce experiments in the same condition (with same -environnement and same parameters). In addition we added some options to fix -the random processes seed and use Pytorch latest enhancement for a +environment and same parameters). In addition, we added some options to fix +the random processes seed and use Pytorch's latest enhancement for a deterministic behavior. Other improvement and small fix have also been implemented. diff --git a/LICENSE.txt b/LICENSE.txt old mode 100644 new mode 100755 diff --git a/MANIFEST.in b/MANIFEST.in old mode 100644 new mode 100755 diff --git a/Makefile b/Makefile old mode 100644 new mode 100755 diff --git a/clinicadl/__init__.py b/clinicadl/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/abnormality_map/__init__.py b/clinicadl/abnormality_map/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/clinicadl/abnormality_map/abnormality_map.py b/clinicadl/abnormality_map/abnormality_map.py new file mode 100644 index 000000000..a9a3f9114 --- /dev/null +++ b/clinicadl/abnormality_map/abnormality_map.py @@ -0,0 +1,163 @@ +import json +import numpy as np +import nibabel as nib +from pathlib import Path +from copy import deepcopy +from typing import Tuple, Callable, Union, List + +import torch +from torch.utils.data import DataLoader + +from clinicadl.utils.caps_dataset.data import load_data_test, return_dataset, get_transforms +from clinicadl.utils.maps_manager import MapsManager +from clinicadl.utils.maps_manager.maps_manager_utils import read_json + + +def residual_fn( + x: torch.Tensor, + x_hat: torch.Tensor, +) -> Tuple[torch.Tensor, str]: + return x - x_hat, "residual" + +# def residual_fn( +# x: nib.Nifti1Image, +# x_hat: nib.Nifti1Image, +# ) -> Tuple[nib.Nifti1Image, str]: +# affine = x.affine +# x_data = x.get_fdata().numpy() +# x_hat_data = x_hat.get_fdata().numpy() +# return nib.Nifti1Image(x_data - x_hat_data, eye(4)), "residual" + +abn_map_dict = { + "residual": residual_fn, +} + +def compute_abnormality_map( + maps_dir: Path, + preprocessing_json: Path, + data_group: str, + gpu: bool, + n_proc: int, + batch_size: int, + abn_map_fn: Union[str, Callable[[nib.Nifti1Image, nib.Nifti1Image], Tuple[nib.Nifti1Image, str]]] = residual_fn, + split_list: List[int] = None, + tsv_path: Path = None, + selection_metrics: str = None, + multi_cohort: bool = False, +): + """ + Computes the abnormality map for a given split and data group using the model selected with the selection metrics. + + Args: + maps_manager (MapsManager): MAPS manager object + split_list (list[int]): list of splits to compute the abnormality map on + data_group (str): data group to compute the abnormality map on + selection_metrics (str): selection metrics to use to select the best model + abn_map_fn (Callable[[nib.Nifti1Image, nib.Nifti1Image], Tuple[nib.Nifti1Image, str]], optional): + method to compute the abnormality map. Defaults to residual_fn. + """ + + maps_manager = MapsManager(maps_dir) + + if not split_list: + split_list = maps_manager._find_splits() + + for split in split_list: + + if tsv_path is not None: + group_df = load_data_test( + tsv_path, + "CN", + multi_cohort=multi_cohort, + ) + + group_df, group_parameters = maps_manager.get_group_info(data_group, split) + + if not selection_metrics: + split_selection_metrics = maps_manager._find_selection_metrics(split)[0] + else: + split_selection_metrics = selection_metrics[0] + + preprocessing_dict = read_json(preprocessing_json) + + input_preprocessing_dict = deepcopy(preprocessing_dict) + input_pattern = preprocessing_dict["file_type"]["pattern"].replace("_mood.pt", "_input.pt") + input_preprocessing_dict["file_type"]["pattern"] = input_pattern + input_preprocessing_dict["preprocessing"] = "custom" + + output_preprocessing_dict = deepcopy(preprocessing_dict) + output_pattern = preprocessing_dict["file_type"]["pattern"].replace("_mood.pt", "_output.pt") + output_preprocessing_dict["file_type"]["pattern"] = output_pattern + output_preprocessing_dict["preprocessing"] = "custom" + + # TO DO: code to retrieve transforms + # _, all_transforms = get_transforms( + # normalize=maps_manager.normalize, + # data_augmentation=maps_manager.data_augmentation, + # size_reduction=maps_manager.size_reduction, + # size_reduction_factor=maps_manager.size_reduction_factor, + # ) + + caps_output_dir = Path(maps_dir, f"split-{split}", f"best-{split_selection_metrics}", "CapsOutput") + + input_data = return_dataset( + caps_output_dir, + group_df, + input_preprocessing_dict, + all_transformations=None, # TO DO: change this + multi_cohort=group_parameters["multi_cohort"], + ) + + output_data = return_dataset( + caps_output_dir, + group_df, + output_preprocessing_dict, + all_transformations=None, # TO DO: change this + multi_cohort=group_parameters["multi_cohort"], + ) + + input_loader = DataLoader( + input_data, + batch_size=batch_size, + shuffle=False, + num_workers=n_proc, + ) + + output_loader = DataLoader( + output_data, + batch_size=batch_size, + shuffle=False, + num_workers=n_proc, + ) + + for i, (input_batch, output_batch) in enumerate(zip(input_loader, output_loader)): + + for idx in range(len(input_batch['participant_id'])): + + assert input_batch['participant_id'][idx] == output_batch['participant_id'][idx] + + participant_id = input_batch['participant_id'][idx] + session_id = input_batch['session_id'][idx] + + if isinstance(abn_map_fn, str): + abn_map_fn = abn_map_dict[abn_map_fn] + + abn_map_pt, abn_map_pattern = abn_map_fn(output_batch['data'][idx], input_batch['data'][idx]) + abn_map_nii = nib.Nifti1Image(abn_map_pt.numpy(), np.eye(4)) + + abn_map_pt_path = input_batch['image_path'][idx].replace("_input.pt", f"_{abn_map_pattern}.pt") + abn_map_nii_path = Path(caps_output_dir, "subjects", participant_id, session_id, "custom", f"{participant_id}_{session_id}_{abn_map_pattern}.nii.gz") + + torch.save(abn_map_pt, abn_map_pt_path) + nib.save(abn_map_nii, abn_map_nii_path) + + print("Saved abnormality map at", abn_map_pt_path) + print("Saved abnormality map at", abn_map_nii_path) + + abn_map_preprocessing_dict = deepcopy(preprocessing_dict) + abn_map_preprocessing_dict["file_type"]["pattern"] = abn_map_pattern + + abn_map_preprocessing_json = json.dumps(abn_map_preprocessing_dict, skipkeys=True, indent=4) + f = open(preprocessing_json.replace(".json", f"_{abn_map_pattern}.json"), "w") + f.write(abn_map_preprocessing_json) + f.close() \ No newline at end of file diff --git a/clinicadl/abnormality_map/abnormality_map_cli.py b/clinicadl/abnormality_map/abnormality_map_cli.py new file mode 100644 index 000000000..fd2c111e6 --- /dev/null +++ b/clinicadl/abnormality_map/abnormality_map_cli.py @@ -0,0 +1,66 @@ +import click +from pathlib import Path + +from clinicadl.utils import cli_param + +from clinicadl.abnormality_map.abnormality_map import compute_abnormality_map + +@click.command(name="abnormality_map", no_args_is_help=True) +@cli_param.argument.input_maps +@cli_param.argument.data_group +@cli_param.argument.preprocessing_json +@cli_param.option.participant_list +@cli_param.option.selection_metrics +@cli_param.option.split +@cli_param.option.use_gpu +@cli_param.option.n_proc +@cli_param.option.batch_size +@click.option( + "--multi_cohort", + type=bool, + default=False, + is_flag=True, + help="""Allow to use multiple CAPS directories. + In this case, CAPS_DIRECTORY and PARTICIPANTS_TSV must be paths to TSV files.""", +) +@click.option( + "--abn_map_type", + type=str, + default="residual", + help="""Type of abnormality map to compute. Default is residual.""", +) +def cli( + input_maps_directory, + data_group, + preprocessing_json, + participants_tsv, + split, + abn_map_type, + gpu, + n_proc, + batch_size, + selection_metrics, + multi_cohort, +): + """Infer the outputs of a trained model on a test set. + INPUT_MAPS_DIRECTORY is the MAPS folder from where the model used for prediction will be loaded. + DATA_GROUP is the name of the subjects and sessions list used for the interpretation. + """ + from clinicadl.utils.cmdline_utils import check_gpu + + if gpu: + check_gpu() + + compute_abnormality_map( + maps_dir=input_maps_directory, + abn_map_fn=abn_map_type, + data_group=data_group, + tsv_path=participants_tsv, + gpu=gpu, + n_proc=n_proc, + batch_size=batch_size, + split_list=split, + selection_metrics=selection_metrics, + multi_cohort=multi_cohort, + preprocessing_json=preprocessing_json, + ) \ No newline at end of file diff --git a/clinicadl/cmdline.py b/clinicadl/cmdline.py old mode 100644 new mode 100755 index 2bace163e..4808dd741 --- a/clinicadl/cmdline.py +++ b/clinicadl/cmdline.py @@ -11,6 +11,7 @@ from clinicadl.train.train_cli import cli as train_cli from clinicadl.tsvtools.cli import cli as tsvtools_cli from clinicadl.utils.maps_manager.logwriter import setup_logging +from clinicadl.abnormality_map.abnormality_map_cli import cli as abnormality_map_cli CONTEXT_SETTINGS = dict( # Extend content width to avoid shortening of pipeline help. @@ -44,6 +45,7 @@ def cli(verbosity): cli.add_command(interpret_cli) cli.add_command(qc_cli) cli.add_command(random_search_cli) +cli.add_command(abnormality_map_cli) if __name__ == "__main__": cli() diff --git a/clinicadl/extract/__init__.py b/clinicadl/extract/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/extract/extract.py b/clinicadl/extract/extract.py old mode 100644 new mode 100755 index 9460ff894..71c45319b --- a/clinicadl/extract/extract.py +++ b/clinicadl/extract/extract.py @@ -1,16 +1,25 @@ +import os from logging import getLogger +from pathlib import Path -def DeepLearningPrepareData(caps_directory, tsv_file, n_proc, parameters): - import os - from os import path - - from clinica.utils.inputs import check_caps_folder, clinica_file_reader - from clinica.utils.nipype import container_from_filename - from clinica.utils.participant import get_subject_session_list +def DeepLearningPrepareData( + caps_directory: Path, + tsv_file: Path, + n_proc, + parameters, + tmp_dir: str = None, + mood_abdom: bool = False, +): from joblib import Parallel, delayed from torch import save as save_tensor + from clinicadl.utils.clinica_utils import ( + check_caps_folder, + clinicadl_file_reader, + container_from_filename, + get_subject_session_list, + ) from clinicadl.utils.exceptions import ClinicaDLArgumentError from clinicadl.utils.preprocessing import write_preprocessing @@ -22,7 +31,7 @@ def DeepLearningPrepareData(caps_directory, tsv_file, n_proc, parameters): check_caps_folder(caps_directory) logger.debug(f"CAPS directory : {caps_directory}.") is_bids_dir = False - sessions, subjects = get_subject_session_list( + subjects, sessions = get_subject_session_list( caps_directory, tsv_file, is_bids_dir, False, None ) if parameters["prepare_dl"]: @@ -49,23 +58,37 @@ def DeepLearningPrepareData(caps_directory, tsv_file, n_proc, parameters): parameters["file_type"] = file_type # Input file: - input_files = clinica_file_reader(subjects, sessions, caps_directory, file_type)[0] + input_files = clinicadl_file_reader(subjects, sessions, caps_directory, file_type)[ + 0 + ] + logger.debug(f"Selected image file name list: {input_files}.") - def write_output_imgs(output_mode, container, subfolder): + def write_output_imgs(output_mode, container, subfolder, mood_abdom: bool = False): # Write the extracted tensor on a .pt file for filename, tensor in output_mode: - output_file_dir = path.join( + output_file_dir = Path( caps_directory, container, "deeplearning_prepare_data", subfolder, mod_subfolder, ) - if not path.exists(output_file_dir): + if not Path(output_file_dir).is_dir(): os.makedirs(output_file_dir) - output_file = path.join(output_file_dir, filename) + + output_file = Path(output_file_dir, filename) + + if mood_abdom: + # import torch + # from skimage.transform import resize + + tensor = tensor.resize_(256, 256, 256) + # output_img = resize(tensor.numpy(), output_shape=(256, 256, 256)) + # output_img = torch.tensor(output_img) + save_tensor(tensor, output_file) - logger.debug(f" Output tensor saved at {output_file}") + + logger.debug(f"Output tensor saved at {output_file}") if parameters["mode"] == "image" or not parameters["prepare_dl"]: @@ -76,10 +99,12 @@ def prepare_image(file): container = container_from_filename(file) subfolder = "image_based" output_mode = extract_images(file) - logger.debug(f" Image extracted.") - write_output_imgs(output_mode, container, subfolder) + logger.debug("Image extracted.") + write_output_imgs(output_mode, container, subfolder, mood_abdom=mood_abdom) - Parallel(n_jobs=n_proc)(delayed(prepare_image)(file) for file in input_files) + Parallel(n_jobs=n_proc, temp_folder=tmp_dir)( + delayed(prepare_image)(file) for file in input_files + ) elif parameters["prepare_dl"] and parameters["mode"] == "slice": @@ -141,7 +166,7 @@ def prepare_roi(file): parameters["preprocessing"] ] - parameters["masks_location"] = path.join( + parameters["masks_location"] = Path( caps_directory, "masks", f"tpl-{parameters['roi_template']}" ) if len(parameters["roi_list"]) == 0: @@ -153,17 +178,21 @@ def prepare_roi(file): parameters["masks_location"], parameters["roi_list"], parameters["roi_mask_pattern"], - None - if parameters["use_uncropped_image"] is None - else not parameters["use_uncropped_image"], + ( + None + if parameters["use_uncropped_image"] is None + else not parameters["use_uncropped_image"] + ), ) output_mode = extract_roi( file, masks_location=parameters["masks_location"], mask_pattern=parameters["roi_mask_pattern"], - cropped_input=None - if parameters["use_uncropped_image"] is None - else not parameters["use_uncropped_image"], + cropped_input=( + None + if parameters["use_uncropped_image"] is None + else not parameters["use_uncropped_image"] + ), roi_names=parameters["roi_list"], uncrop_output=parameters["uncropped_roi"], ) diff --git a/clinicadl/extract/extract_cli.py b/clinicadl/extract/extract_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/extract/extract_utils.py b/clinicadl/extract/extract_utils.py old mode 100644 new mode 100755 index 2efb6dcca..81a321068 --- a/clinicadl/extract/extract_utils.py +++ b/clinicadl/extract/extract_utils.py @@ -1,7 +1,7 @@ # coding: utf8 from os import path from time import time -from typing import Any, Dict, List, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch @@ -14,8 +14,8 @@ def get_parameters_dict( extract_json: str, use_uncropped_image: bool, custom_suffix: str, - acq_label: str, - suvr_reference_region: str, + acq_label: Optional[str] = None, + suvr_reference_region: Optional[str] = None, ) -> Dict[str, Any]: """ Args: @@ -63,14 +63,16 @@ def compute_extract_json(extract_json: str) -> str: def compute_folder_and_file_type( parameters: Dict[str, Any] ) -> Tuple[str, Dict[str, str]]: - from clinica.utils.input_files import T1W_LINEAR, T1W_LINEAR_CROPPED, pet_linear_nii + from clinicadl.generate.generate_utils import linear_nii, pet_linear_nii if parameters["preprocessing"] == "t1-linear": mod_subfolder = "t1_linear" - if parameters["use_uncropped_image"]: - file_type = T1W_LINEAR - else: - file_type = T1W_LINEAR_CROPPED + file_type = linear_nii("T1w", parameters["use_uncropped_image"]) + + elif parameters["preprocessing"] == "flair-linear": + mod_subfolder = "flair_linear" + file_type = linear_nii("flair", parameters["use_uncropped_image"]) + elif parameters["preprocessing"] == "pet-linear": mod_subfolder = "pet_linear" file_type = pet_linear_nii( @@ -80,10 +82,13 @@ def compute_folder_and_file_type( ) elif parameters["preprocessing"] == "custom": mod_subfolder = "custom" - file_type = { - "pattern": f"*{parameters['custom_suffix']}", - "description": "Custom suffix", - } + if "file_type" in parameters: + file_type = parameters["file_type"] + else: + file_type = { + "pattern": f"*{parameters['custom_suffix']}", + "description": "Custom suffix", + } parameters["use_uncropped_image"] = None else: raise NotImplementedError( diff --git a/clinicadl/generate/__init__.py b/clinicadl/generate/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/generate/generate.py b/clinicadl/generate/generate.py old mode 100644 new mode 100755 index 6a33507ce..11eea9df0 --- a/clinicadl/generate/generate.py +++ b/clinicadl/generate/generate.py @@ -12,10 +12,18 @@ import numpy as np import pandas as pd import torch -from clinica.utils.inputs import RemoteFileStructure, clinica_file_reader, fetch_file +import torchio as tio +from joblib import Parallel, delayed +from nilearn.image import resample_to_img from clinicadl.extract.extract_utils import compute_extract_json from clinicadl.utils.caps_dataset.data import CapsDataset +from clinicadl.utils.clinica_utils import ( + RemoteFileStructure, + clinicadl_file_reader, + fetch_file, +) +from clinicadl.utils.exceptions import DownloadError from clinicadl.utils.maps_manager.iotools import check_and_clean, commandline_to_json from clinicadl.utils.preprocessing import write_preprocessing from clinicadl.utils.tsvtools_utils import extract_baseline @@ -98,11 +106,13 @@ def generate_random_dataset( preprocessing, uncropped_image, acq_label, suvr_reference_region ) - image_paths = clinica_file_reader( - [participant_id], [session_id], caps_dict[cohort], file_type - )[0] - image_nii = nib.load(image_paths[0]) - image = image_nii.get_data() + image_path = Path( + clinicadl_file_reader( + [participant_id], [session_id], caps_dict[cohort], file_type + )[0][0] + ) + image_nii = nib.load(image_path) + image = image_nii.get_fdata() # Create output tsv file participant_id_list = [f"sub-RAND{i}" for i in range(2 * n_subjects)] @@ -256,11 +266,13 @@ def generate_trivial_dataset( participant_id = data_df.loc[data_idx, "participant_id"] session_id = data_df.loc[data_idx, "session_id"] cohort = data_df.loc[data_idx, "cohort"] - image_paths = clinica_file_reader( - [participant_id], [session_id], caps_dict[cohort], file_type - )[0] - image_nii = nib.load(image_paths[0]) - image = image_nii.get_data() + image_path = Path( + clinicadl_file_reader( + [participant_id], [session_id], caps_dict[cohort], file_type + )[0][0] + ) + image_nii = nib.load(image_path) + image = image_nii.get_fdata() input_filename = basename(image_paths[0]) filename_pattern = "_".join(input_filename.split("_")[2::]) @@ -272,7 +284,13 @@ def generate_trivial_dataset( makedirs(trivial_image_nii_dir, exist_ok=True) - atlas_to_mask = nib.load(join(mask_path, f"mask-{label + 1}.nii")).get_data() + trivial_image_nii_dir.mkdir(parents=True, exist_ok=True) + + path_to_mask = mask_path / f"mask-{label + 1}.nii" + if path_to_mask.is_file(): + atlas_to_mask = nib.load(path_to_mask).get_fdata() + else: + raise ValueError("masks need to be named mask-1.nii and mask-2.nii") # Create atrophied image trivial_image = im_loss_roi_gaussian_distribution( @@ -395,3 +413,355 @@ def generate_shepplogan_dataset( } write_preprocessing(preprocessing_dict, output_dir) write_missing_mods(output_dir, data_df) + + logger.info(f"Shepplogan dataset was generated at {output_dir}") + + +def generate_hypometabolic_dataset( + caps_directory: Path, + output_dir: Path, + n_subjects: int, + n_proc: int, + tsv_path: Optional[Path] = None, + preprocessing: str = "pet-linear", + pathology: str = "ad", + anomaly_degree: float = 30, + sigma: int = 5, + uncropped_image: bool = False, +): + """ + Generates a dataset, based on the images of the CAPS directory, where all + the images are processed using a mask to generate a specific pathology. + + Parameters + ---------- + caps_directory: Path + Path to the CAPS directory. + output_dir: Path + Folder containing the synthetic dataset in CAPS format. + n_subjects: int + Number of subjects in each class of the synthetic dataset. + n_proc: int + Number of cores used during the task. + tsv_path: Path + Path to tsv file of list of subjects/sessions. + preprocessing: str + Preprocessing performed. For now it must be 'pet-linear'. + pathology: str + Name of the pathology to generate. + anomaly_degree: float + Percentage of pathology applied. + sigma: int + It is the parameter of the gaussian filter used for smoothing. + uncropped_image: bool + If True the uncropped image of `t1-linear` or `pet-linear` will be used. + + Returns + ------- + Folder structure where images are stored in CAPS format. + + + Raises + ------ + IndexError: if `n_subjects` is higher than the length of the TSV file at `tsv_path`. + """ + + commandline_to_json( + { + "output_dir": output_dir, + "caps_dir": caps_directory, + "preprocessing": preprocessing, + "n_subjects": n_subjects, + "n_proc": n_proc, + "pathology": pathology, + "anomaly_degree": anomaly_degree, + } + ) + + # Transform caps_directory in dict + caps_dict = CapsDataset.create_caps_dict(caps_directory, multi_cohort=False) + # Read DataFrame + data_df = load_and_check_tsv(tsv_path, caps_dict, output_dir) + data_df = extract_baseline(data_df) + + if n_subjects > len(data_df): + raise IndexError( + f"The number of subjects {n_subjects} cannot be higher " + f"than the number of subjects in the baseline dataset of size {len(data_df)}" + f"Please add the '--n_subjects' option and re-run the command." + ) + checksum_dir = { + "ad": "2100d514a3fabab49fe30702700085a09cdad449bdf1aa04b8f804e238e4dfc2", + "bvftd": "5a0ad28dff649c84761aa64f6e99da882141a56caa46675b8bf538a09fce4f81", + "lvppa": "1099f5051c79d5b4fdae25226d97b0e92f958006f6545f498d4b600f3f8a422e", + "nfvppa": "9512a4d4dc0003003c4c7526bf2d0ddbee65f1c79357f5819898453ef7271033", + "pca": "ace36356b57f4db73e17c421a7cfd7ae056a1b258b8126534cf65d8d0be9527a", + "svppa": "44f2e00bf2d2d09b532cb53e3ba61d6087b4114768cc8ae3330ea84c4b7e0e6a", + } + home = Path.home() + cache_clinicadl = home / ".cache" / "clinicadl" / "ressources" / "masks_hypo" + url_aramis = "https://aramislab.paris.inria.fr/files/data/masks/hypo/" + FILE1 = RemoteFileStructure( + filename=f"mask_hypo_{pathology}.nii", + url=url_aramis, + checksum=checksum_dir[pathology], + ) + cache_clinicadl.mkdir(parents=True, exist_ok=True) + if not (cache_clinicadl / f"mask_hypo_{pathology}.nii").is_file(): + logger.info(f"Downloading {pathology} masks...") + # mask_path = fetch_file(FILE1, cache_clinicadl) + try: + mask_path = fetch_file(FILE1, cache_clinicadl) + except: + DownloadError( + """Unable to download masks, please download them + manually at https://aramislab.paris.inria.fr/files/data/masks/ + and provide a valid path.""" + ) + + else: + mask_path = cache_clinicadl / f"mask_hypo_{pathology}.nii" + + mask_nii = nib.load(mask_path) + + # Find appropriate preprocessing file type + file_type = find_file_type( + preprocessing, uncropped_image, "18FFDG", "cerebellumPons2" + ) + + # Output tsv file + columns = ["participant_id", "session_id", "pathology", "percentage"] + output_df = pd.DataFrame(columns=columns) + participants = [data_df.loc[i, "participant_id"] for i in range(n_subjects)] + sessions = [data_df.loc[i, "session_id"] for i in range(n_subjects)] + cohort = caps_directory + + images_paths = clinicadl_file_reader(participants, sessions, cohort, file_type)[0] + image_nii = nib.load(images_paths[0]) + + mask_resample_nii = resample_to_img(mask_nii, image_nii, interpolation="nearest") + mask = mask_resample_nii.get_fdata() + + mask = mask_processing(mask, anomaly_degree, sigma) + + # Create subjects dir + (output_dir / "subjects").mkdir(parents=True, exist_ok=True) + + def generate_hypometabolic_image(subject_id, output_df): + image_path = Path(images_paths[subject_id]) + image_nii = nib.load(image_path) + image = image_nii.get_fdata() + if image_path.suffix == ".gz": + input_filename = Path(image_path.stem).stem + else: + input_filename = image_path.stem + input_filename = input_filename.strip("pet") + hypo_image_nii_dir = ( + output_dir + / "subjects" + / participants[subject_id] + / sessions[subject_id] + / preprocessing + ) + hypo_image_nii_filename = ( + f"{input_filename}pat-{pathology}_deg-{int(anomaly_degree)}_pet.nii.gz" + ) + hypo_image_nii_dir.mkdir(parents=True, exist_ok=True) + + # Create atrophied image + hypo_image = image * mask + hypo_image_nii = nib.Nifti1Image(hypo_image, affine=image_nii.affine) + hypo_image_nii.to_filename(hypo_image_nii_dir / hypo_image_nii_filename) + + # Append row to output tsv + row = [ + participants[subject_id], + sessions[subject_id], + pathology, + anomaly_degree, + ] + row_df = pd.DataFrame([row], columns=columns) + output_df = pd.concat([output_df, row_df]) + return output_df + + results_list = Parallel(n_jobs=n_proc)( + delayed(generate_hypometabolic_image)(subject_id, output_df) + for subject_id in range(n_subjects) + ) + + output_df = pd.DataFrame() + for result_df in results_list: + output_df = pd.concat([result_df, output_df]) + + output_df.to_csv(output_dir / "data.tsv", sep="\t", index=False) + + write_missing_mods(output_dir, output_df) + + logger.info( + f"Hypometabolic dataset was generated, with {anomaly_degree} % of dementia {pathology} at {output_dir}." + ) + + +def generate_artifacts_dataset( + caps_directory: Path, + output_dir: Path, + n_proc: int, + tsv_path: Optional[str] = None, + preprocessing: str = "t1-linear", + multi_cohort: bool = False, + uncropped_image: bool = False, + tracer: str = "fdg", + suvr_reference_region: str = "pons", + contrast: bool = False, + gamma: List = [-0.2, -0.05], + motion: bool = False, + translation: List = [2, 4], + rotation: List = [2, 4], + num_transforms: int = 2, + noise: bool = False, + noise_std: List = [5, 15], +): + """ + Generates a dataset, based on the images of the CAPS directory, where + all the images are corrupted with a combination of motion, contrast and + noise artefacts using torchio simulations. + Args: + caps_directory: Path + Path to the CAPS directory. + output_dir: Path + Folder containing the synthetic dataset in CAPS format. + n_proc: int + Number of cores used during the task. + tsv_path: Path + Path to tsv file of list of subjects/sessions. + preprocessing: str + Preprocessing performed. Must be in ['linear', 'extensive']. + multi_cohort: bool + If True caps_directory is the path to a TSV file linking cohort names and paths. + uncropped_image: bool + If True the uncropped image of `t1-linear` or `pet-linear` will be used. + tracer: str + Name of the tracer when using `pet-linear` preprocessing. + suvr_reference_region: str + Name of the reference region when using `pet-linear` preprocessing. + translation: List + Translation range in mm of simulated movements. + rotation : List + Rotation range in degree of simulated movement. + num_transformes: int + Number of simulated movements. + gamma: List + Gamma range of simulated contrast. + noise_std: List + Stadndard deviation of simulated noise. + Returns: + Folder structure where images are stored in CAPS format. + """ + + commandline_to_json( + { + "output_dir": output_dir, + "caps_dir": caps_directory, + "preprocessing": preprocessing, + } + ) + + # Transform caps_directory in dict + caps_dict = CapsDataset.create_caps_dict(caps_directory, multi_cohort=multi_cohort) + # Read DataFrame + data_df = load_and_check_tsv(tsv_path, caps_dict, output_dir) + # Create subjects dir + (output_dir / "subjects").mkdir(parents=True, exist_ok=True) + + # Output tsv file + columns = ["participant_id", "session_id", "diagnosis"] + output_df = pd.DataFrame(columns=columns) + + # Find appropriate preprocessing file type + file_type = find_file_type( + preprocessing, uncropped_image, tracer, suvr_reference_region + ) + artifacts_list = [] + if motion: + artifacts_list.append("motion") + if contrast: + artifacts_list.append("contrast") + if noise: + artifacts_list.append("noise") + + def create_artifacts_image(data_idx, output_df): + participant_id = data_df.loc[data_idx, "participant_id"] + session_id = data_df.loc[data_idx, "session_id"] + cohort = data_df.loc[data_idx, "cohort"] + image_path = Path( + clinicadl_file_reader( + [participant_id], [session_id], caps_dict[cohort], file_type + )[0][0] + ) + input_filename = image_path.name + filename_pattern = "_".join(input_filename.split("_")[2::]) + subject_name = input_filename.split("_")[:1][0] + session_name = input_filename.split("_")[1:2][0] + + artif_image_nii_dir = ( + output_dir / "subjects" / subject_name / session_name / preprocessing + ) + artif_image_nii_dir.mkdir(parents=True, exist_ok=True) + + artifacts_tio = [] + arti_ext = "" + for artif in artifacts_list: + if artif == "motion": + artifacts_tio.append( + tio.RandomMotion( + degrees=(rotation[0], rotation[1]), + translation=(translation[0], translation[1]), + num_transforms=num_transforms, + ) + ) + arti_ext += "mot-" + elif artif == "noise": + artifacts_tio.append( + tio.RandomNoise( + std=(noise_std[0], noise_std[1]), + ) + ) + arti_ext += "noi-" + elif artif == "contrast": + artifacts_tio.append(tio.RandomGamma(log_gamma=(gamma[0], gamma[1]))) + arti_ext += "con-" + + if filename_pattern.endswith(".nii.gz"): + file_suffix = ".nii.gz" + filename_pattern = Path(Path(filename_pattern).stem).stem + elif filename_pattern.endswith(".nii"): + file_suffix = ".nii" + filename_pattern = Path(filename_pattern).stem + + artif_image_nii_filename = f"{subject_name}_{session_name}_{filename_pattern}_art-{arti_ext[:-1]}{file_suffix}" + + artifacts = tio.transforms.Compose(artifacts_tio) + + artif_image = artifacts(tio.ScalarImage(image_path)) + artif_image.save(artif_image_nii_dir / artif_image_nii_filename) + + # Append row to output tsv + row = [subject_name, session_name, artifacts_list] + row_df = pd.DataFrame([row], columns=columns) + output_df = pd.concat([output_df, row_df]) + + return output_df + + results_df = Parallel(n_jobs=n_proc)( + delayed(create_artifacts_image)(data_idx, output_df) + for data_idx in range(len(data_df)) + ) + output_df = pd.DataFrame() + for result in results_df: + output_df = pd.concat([result, output_df]) + + output_df.to_csv(output_dir / "data.tsv", sep="\t", index=False) + + write_missing_mods(output_dir, output_df) + + logger.info(f"Images corrupted with artefacts were generated at {output_dir}") diff --git a/clinicadl/generate/generate_cli.py b/clinicadl/generate/generate_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/generate/generate_random_cli.py b/clinicadl/generate/generate_random_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/generate/generate_shepplogan_cli.py b/clinicadl/generate/generate_shepplogan_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/generate/generate_trivial_cli.py b/clinicadl/generate/generate_trivial_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/generate/generate_utils.py b/clinicadl/generate/generate_utils.py index 89ced5b67..8e1d1423e 100755 --- a/clinicadl/generate/generate_utils.py +++ b/clinicadl/generate/generate_utils.py @@ -5,10 +5,15 @@ import numpy as np import pandas as pd -from clinica.utils.input_files import T1W_LINEAR, T1W_LINEAR_CROPPED, pet_linear_nii from scipy.ndimage import gaussian_filter from skimage.draw import ellipse +from clinicadl.utils.caps_dataset.data import check_multi_cohort_tsv +from clinicadl.utils.clinica_utils import ( + create_subs_sess_list, + linear_nii, + pet_linear_nii, +) from clinicadl.utils.exceptions import ClinicaDLArgumentError @@ -19,10 +24,7 @@ def find_file_type( suvr_reference_region: str, ) -> Dict[str, str]: if preprocessing == "t1-linear": - if uncropped_image: - file_type = T1W_LINEAR - else: - file_type = T1W_LINEAR_CROPPED + file_type = linear_nii("T1w", uncropped_image) elif preprocessing == "pet-linear": if acq_label is None or suvr_reference_region is None: raise ClinicaDLArgumentError( @@ -59,11 +61,6 @@ def write_missing_mods(output_dir: str, output_df: pd.DataFrame): def load_and_check_tsv( tsv_path: str, caps_dict: Dict[str, str], output_path: str ) -> pd.DataFrame: - from os.path import join - - from clinica.iotools.utils.data_handling import create_subs_sess_list - - from clinicadl.utils.caps_dataset.data import check_multi_cohort_tsv if tsv_path is not None: if len(caps_dict) == 1: diff --git a/clinicadl/interpret/__init__.py b/clinicadl/interpret/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/interpret/gradients.py b/clinicadl/interpret/gradients.py old mode 100644 new mode 100755 diff --git a/clinicadl/interpret/interpret.py b/clinicadl/interpret/interpret.py old mode 100644 new mode 100755 diff --git a/clinicadl/interpret/interpret_cli.py b/clinicadl/interpret/interpret_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/predict/__init__.py b/clinicadl/predict/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/predict/predict.py b/clinicadl/predict/predict.py old mode 100644 new mode 100755 index 2f1dc9864..56935337a --- a/clinicadl/predict/predict.py +++ b/clinicadl/predict/predict.py @@ -1,4 +1,5 @@ # coding: utf8 +from pathlib import Path from typing import List from clinicadl import MapsManager @@ -9,23 +10,27 @@ def predict( maps_dir: str, data_group: str, caps_directory: str, - tsv_path: str, + tsv_path: Path, use_labels: bool = True, label: str = None, gpu: bool = True, n_proc: int = 0, - batch_size: int = 1, + batch_size: int = 8, + split_list: List[int] = None, selection_metrics: List[str] = None, diagnoses: List[str] = None, multi_cohort: bool = False, overwrite: bool = False, save_tensor: bool = False, save_nifti: bool = False, + save_latent_tensor: bool = False, + sample_latent: int = 0, + save_caps: bool = False, + skip_leak_check: bool = False, ): """ This function loads a MAPS and predicts the global metrics and individual values for all the models selected using a metric in selection_metrics. - Args: maps_dir: path to the MAPS. data_group: name of the data group tested. @@ -43,6 +48,8 @@ def predict( overwrite: If True former definition of data group is erased save_tensor: For reconstruction task only, if True it will save the reconstruction as .pt file in the MAPS. save_nifti: For reconstruction task only, if True it will save the reconstruction as NIfTI file in the MAPS. + sample_latent: For reconstruction task only, will sample the latent space multiple times to generate multiple + reconstructions for a single input. """ verbose_list = ["warning", "info", "debug"] @@ -56,10 +63,19 @@ def predict( raise ClinicaDLArgumentError( "Cannot save nifti if the network task is not reconstruction. Please remove --save_nifti option." ) + if sample_latent and maps_manager.network_task != "reconstruction": + raise ClinicaDLArgumentError( + "Cannot sample multiple times the latent space if the network task is not reconstruction. \ + Please remove --sample_latent option." + ) + # Check if it is a pythae model + pythae = "pythae" in maps_manager.parameters["architecture"] + maps_manager.predict( data_group, caps_directory=caps_directory, tsv_path=tsv_path, + split_list=split_list, selection_metrics=selection_metrics, multi_cohort=multi_cohort, diagnoses=diagnoses, @@ -71,4 +87,9 @@ def predict( overwrite=overwrite, save_tensor=save_tensor, save_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + pythae=pythae, + sample_latent=sample_latent, + save_caps=save_caps, + skip_leak_check=skip_leak_check, ) diff --git a/clinicadl/predict/predict_cli.py b/clinicadl/predict/predict_cli.py old mode 100644 new mode 100755 index 2a8c131ac..462a6105f --- a/clinicadl/predict/predict_cli.py +++ b/clinicadl/predict/predict_cli.py @@ -2,6 +2,8 @@ from clinicadl.utils import cli_param +from pathlib import Path + @click.command(name="predict", no_args_is_help=True) @cli_param.argument.input_maps @@ -15,7 +17,7 @@ @click.option( "--participants_tsv", default=None, - type=click.Path(), + type=click.Path(exists=True, path_type=Path), help="""Path to the file with subjects/sessions to process, if different from the one used during network training. If it includes the filename will load the TSV file directly. Else will load the baseline TSV files of wanted diagnoses produced by `tsvtool split`.""", @@ -69,6 +71,29 @@ is_flag=True, help="Save the reconstruction output in the MAPS in NIfTI format.", ) +@click.option( + "--save_latent_tensor", + type=bool, + default=False, + is_flag=True, + help="""Save the latent representation of the image.""", +) +@click.option( + "--sample_latent", + type=int, + default=0, + help="""For reconstruction task only, will sample the latent space multiple times to generate + multiple reconstructions for a single input.""", +) +@click.option( + "--save_caps", + type=bool, + default=False, + is_flag=True, + help="Save the reconstruction input and output in the MAPS in Pytorch tensor format in a CAPS.", +) +@cli_param.option.split +@cli_param.option.selection_metrics @cli_param.option.use_gpu @cli_param.option.n_proc @cli_param.option.batch_size @@ -78,6 +103,7 @@ def cli( data_group, caps_directory, participants_tsv, + split, gpu, n_proc, batch_size, @@ -89,11 +115,12 @@ def cli( overwrite, save_tensor, save_nifti, + save_latent_tensor, + sample_latent, + save_caps, ): """Infer the outputs of a trained model on a test set. - INPUT_MAPS_DIRECTORY is the MAPS folder from where the model used for prediction will be loaded. - DATA_GROUP is the name of the subjects and sessions list used for the interpretation. """ from clinicadl.utils.cmdline_utils import check_gpu @@ -113,10 +140,14 @@ def cli( gpu=gpu, n_proc=n_proc, batch_size=batch_size, + split_list=split, selection_metrics=selection_metrics, diagnoses=diagnoses, multi_cohort=multi_cohort, overwrite=overwrite, save_tensor=save_tensor, save_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + sample_latent=sample_latent, + save_caps=save_caps, ) diff --git a/clinicadl/quality_check/__init__.py b/clinicadl/quality_check/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/pet_linear/quality_check.py b/clinicadl/quality_check/pet_linear/quality_check.py new file mode 100644 index 000000000..aa59a04fa --- /dev/null +++ b/clinicadl/quality_check/pet_linear/quality_check.py @@ -0,0 +1,148 @@ +""" +Automatically reject images incorrectly preprocessed pet-linear (Unified Segmentation) with ? criterion + + +""" + +from logging import getLogger +from pathlib import Path + +import nibabel as nib +import numpy as np +import pandas as pd +from joblib import Parallel, delayed + +from clinicadl.utils.clinica_utils import ( + RemoteFileStructure, + clinicadl_file_reader, + fetch_file, + get_subject_session_list, + pet_linear_nii, +) + +from .utils import get_metric + + +def quality_check( + caps_dir: Path, + output_tsv: Path, + tracer: str, + ref_region: str, + use_uncropped_image: bool, + participants_tsv: Path = None, + threshold: float = 0.8, + n_proc: int = 0, + gpu: bool = False, +): + """ + Performs quality check on pet-linear pipeline. + + Parameters + ---------- + + caps_directory: str (Path) + The CAPS folder where pet-linear outputs are stored. + output_tsv: str (Path) + The path to TSV output file. + tracer: str + The label given to the PET acquisition, specifying the tracer used (trc-). + ref_region: str + The reference region used to perform intensity normalization {pons|cerebellumPons|pons2|cerebellumPons2}. + use_uncropped_image: bool + Use the uncropped image instead of the cropped image generated by t1-linear or pet-linear. + participants_tsv: str (Path) + Path to a TSV file including a list of participants/sessions on which the quality-check will be performed. + threshold: float + The threshold on the output probability to decide if the image passed or failed. + Default is 0.8 + n_proc: int + Number of cores used during the task. + """ + # caps_dir= Path(caps_dir) + logger = getLogger("clinicadl.quality_check") + + if Path(output_tsv).is_file(): + raise NameError("this file already exists please chose another name") + + # load the contour mask + home = Path.home() + cache_clinicadl = home / ".cache" / "clinicadl" / "mask" + if not cache_clinicadl.is_dir(): + cache_clinicadl.mkdir(parents=True) + + mask_contour_file = cache_clinicadl / "qc_pet_mask_contour.nii.gz" + + if not (mask_contour_file).is_file(): + try: + url_aramis = "https://aramislab.paris.inria.fr/files/data/masks/" + FILE1 = RemoteFileStructure( + filename="qc_pet_mask_contour.nii.gz", + url=url_aramis, + checksum="0c561ce7de343219e42861b87a359420f9d485da37a8f64d1366ee9bb5460ee6", + ) + mask_contour_file = fetch_file(FILE1, cache_clinicadl) + except IOError as err: + raise IOError("Unable to download required MNI file for QC: ", err) + + mask_contour_nii = nib.load(mask_contour_file) + mask_contour = mask_contour_nii.get_fdata() + mask_contour.astype(int) + + nb_one_inside = np.sum(mask_contour) # 1605780 + + columns = [ + "participant_id", + "session_id", + "pass_probability", + "pass", + ] + + results_df = pd.DataFrame(columns=columns) + subjects, sessions = get_subject_session_list( + caps_dir, participants_tsv, False, False, None + ) + file_type = pet_linear_nii( + tracer, + ref_region, + use_uncropped_image, + ) + input_files = clinicadl_file_reader(subjects, sessions, caps_dir, file_type)[0] + + def write_output_data(file): + file = Path(file) + + if file.is_file(): + image_nii = nib.load(file) + image_np = image_nii.get_fdata() + else: + raise FileNotFoundError(f"Clinical data not found ({file})") + + sum_contour = get_metric(mask_contour, image_np, nb_one_inside) + + session = file.parent.parent + subject = session.parent + row = [ + [ + subject.name, + session.name, + sum_contour, + sum_contour > threshold, + ] + ] + row_df = pd.DataFrame(row, columns=columns) + + return row_df + + results_df = Parallel(n_jobs=n_proc)( + delayed(write_output_data)(file) for file in input_files + ) + + all_df = pd.DataFrame(columns=columns) + for subject_df in results_df: + all_df = pd.concat([all_df, subject_df]) + all_df.sort_values("pass_probability", inplace=True) + all_df.to_csv(output_tsv, sep="\t", index=False) + + logger.info( + f"Quality check metrics extracted at {Path(output_tsv) / 'QC_metrics.tsv'}." + ) diff --git a/clinicadl/quality_check/qc_cli.py b/clinicadl/quality_check/qc_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/t1_linear/__init__.py b/clinicadl/quality_check/t1_linear/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/t1_linear/cli.py b/clinicadl/quality_check/t1_linear/cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/t1_linear/quality_check.py b/clinicadl/quality_check/t1_linear/quality_check.py index fbc0a7a21..97105e4d7 100755 --- a/clinicadl/quality_check/t1_linear/quality_check.py +++ b/clinicadl/quality_check/t1_linear/quality_check.py @@ -1,6 +1,7 @@ """ This file contains all methods needed to perform the quality check procedure after t1-linear preprocessing. """ + from logging import getLogger from os import makedirs from os.path import abspath, dirname, exists, join @@ -8,11 +9,12 @@ import pandas as pd import torch -from clinica.utils.inputs import RemoteFileStructure, fetch_file +from torch.cuda.amp import autocast from torch.utils.data import DataLoader from clinicadl.generate.generate_utils import load_and_check_tsv from clinicadl.utils.caps_dataset.data import CapsDataset +from clinicadl.utils.clinica_utils import RemoteFileStructure, fetch_file from clinicadl.utils.exceptions import ClinicaDLArgumentError from .utils import QCDataset, resnet_qc_18 @@ -82,7 +84,7 @@ def quality_check( for data in dataloader: logger.debug(f"Processing subject {data['participant_id']}.") - inputs = data["image"] + inputs = data["data"] if gpu: inputs = inputs.cuda() outputs = softmax.forward(model(inputs)) diff --git a/clinicadl/quality_check/t1_linear/utils.py b/clinicadl/quality_check/t1_linear/utils.py old mode 100644 new mode 100755 index d6c32ce48..2fb294b3e --- a/clinicadl/quality_check/t1_linear/utils.py +++ b/clinicadl/quality_check/t1_linear/utils.py @@ -6,6 +6,12 @@ import nibabel as nib import torch + +from torch.utils.data import Dataset + +from clinicadl.prepare_data.prepare_data_utils import compute_folder_and_file_type +from clinicadl.utils.clinica_utils import clinicadl_file_reader, linear_nii + import torch.nn as nn from clinica.utils.input_files import T1W_LINEAR_CROPPED from clinica.utils.inputs import clinica_file_reader @@ -191,8 +197,9 @@ def __init__(self, img_dir, data_df, use_extracted_tensors=False): preprocessing_dict = { "preprocessing": "t1-linear", "mode": "image", - "use_uncropped_image": False, - "file_type": T1W_LINEAR_CROPPED, + "use_uncropped_image": use_uncropped_image, + "file_type": linear_nii("T1w", use_uncropped_image), + "use_tensor": use_extracted_tensors, } self.tensor_dataset = CapsDatasetImage( img_dir, @@ -207,13 +214,33 @@ def __len__(self): def __getitem__(self, idx): if self.use_extracted_tensors: - image = self.tensor_dataset[idx] + file_type = self.preprocessing_dict["file_type"] + file_type["pattern"] = file_type["pattern"].replace(".nii.gz", ".pt") + image_output = clinicadl_file_reader( + [subject], [session], self.img_dir, file_type + )[0] + image_path = Path(image_output[0]) + image_filename = image_path.name + folder, _ = compute_folder_and_file_type(self.preprocessing_dict) + image_dir = ( + self.img_dir + / "subjects" + / subject + / session + / "deeplearning_prepare_data" + / "image_based" + / folder + ) + + image_path = image_dir / image_filename + image = torch.load(image_path) image = self.pt_transform(image) else: - subject = self.df.loc[idx, "participant_id"] - session = self.df.loc[idx, "session_id"] - image_path = clinica_file_reader( - [subject], [session], self.img_dir, T1W_LINEAR_CROPPED + image_path = clinicadl_file_reader( + [subject], + [session], + self.img_dir, + linear_nii("T1w", self.use_uncropped_image), )[0] image = nib.load(image_path[0]) image = self.nii_transform(image) @@ -228,7 +255,7 @@ def nii_transform(image): import torch from skimage import transform - sample = np.array(image.get_data()) + sample = np.array(image.get_fdata()) # normalize input _min = np.min(sample) @@ -290,7 +317,8 @@ def nii_transform(image): ).unsqueeze_(0) def pt_transform(self, image): - from torch.nn.functional import interpolate, pad + import numpy as np + from torch.nn.functional import interpolate image = self.normalization(image) - 0.5 image = image[0, :, :, :] diff --git a/clinicadl/quality_check/t1_volume/__init__.py b/clinicadl/quality_check/t1_volume/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/t1_volume/cli.py b/clinicadl/quality_check/t1_volume/cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/quality_check/t1_volume/quality_check.py b/clinicadl/quality_check/t1_volume/quality_check.py old mode 100644 new mode 100755 index 0cde166c9..488f818bb --- a/clinicadl/quality_check/t1_volume/quality_check.py +++ b/clinicadl/quality_check/t1_volume/quality_check.py @@ -4,6 +4,7 @@ 2) percentage of non zero values < 15 % or > 50 % 3) frontal similarity of T1 volume with the template < 0.40 """ + from logging import getLogger from os import path diff --git a/clinicadl/quality_check/t1_volume/utils.py b/clinicadl/quality_check/t1_volume/utils.py old mode 100644 new mode 100755 index 4576f3be8..c0adbd1e8 --- a/clinicadl/quality_check/t1_volume/utils.py +++ b/clinicadl/quality_check/t1_volume/utils.py @@ -8,7 +8,8 @@ import nibabel as nib import numpy as np import pandas as pd -from clinica.utils.inputs import RemoteFileStructure, fetch_file + +from clinicadl.utils.clinica_utils import RemoteFileStructure, fetch_file def extract_metrics(caps_dir, output_dir, group_label): diff --git a/clinicadl/random_search/__init__.py b/clinicadl/random_search/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/random_search/random_search.py b/clinicadl/random_search/random_search.py index cfaddbf0e..5cbfb31c4 100755 --- a/clinicadl/random_search/random_search.py +++ b/clinicadl/random_search/random_search.py @@ -3,24 +3,41 @@ """ from os import path +from random import sample -import toml -from clinicadl.random_search.random_search_utils import get_space_dict, random_sampling -from clinicadl.train import train +def launch_search(launch_directory, job_name, toml_name): + from clinicadl.random_search.random_search_classification_utils import get_classification_space_dict, classification_random_sampling + from clinicadl.train import train - -def launch_search(launch_directory, job_name): - - if not path.exists(path.join(launch_directory, "random_search.toml")): + if not path.exists(path.join(launch_directory, toml_name)): raise FileNotFoundError( - f"TOML file 'random_search.toml' must be written in directory {launch_directory}." + f"TOML file {toml_name} must be written in directory {launch_directory}." ) - space_options = get_space_dict(launch_directory) - options = random_sampling(space_options) + space_options = get_classification_space_dict(launch_directory, toml_name) + options = classification_random_sampling(space_options) maps_directory = path.join(launch_directory, job_name) split = options.pop("split") options["architecture"] = "RandomArchitecture" train(maps_directory, options, split) + + +def launch_vae_search(launch_directory, job_name, toml_name): + from clinicadl.utils.maps_manager import MapsManager + from clinicadl.random_search.random_search_vae_utils import get_vae_space_dict, vae_random_sampling + + space_options = get_vae_space_dict(launch_directory, toml_name) + parameters = vae_random_sampling(space_options) + print("Parameters:", parameters) + + # Select 3 splits randomly + split_list = sample(range(6), 3) + print("Split list:", split_list) + + # initialise maps + maps_dir = path.join(launch_directory, job_name) + maps_manager = MapsManager(maps_dir, parameters, verbose="info") + # launch training procedure for Pythae + maps_manager.train_pythae(split_list=split_list) diff --git a/clinicadl/random_search/random_search_utils.py b/clinicadl/random_search/random_search_classification_utils.py old mode 100644 new mode 100755 similarity index 93% rename from clinicadl/random_search/random_search_utils.py rename to clinicadl/random_search/random_search_classification_utils.py index 94abbf777..3092c110a --- a/clinicadl/random_search/random_search_utils.py +++ b/clinicadl/random_search/random_search_classification_utils.py @@ -9,7 +9,7 @@ from clinicadl.utils.preprocessing import read_preprocessing -def get_space_dict(launch_directory: str) -> Dict[str, Any]: +def get_classification_space_dict(launch_directory: str) -> Dict[str, Any]: """Transforms the TOML dictionary in one dimension dictionary.""" toml_path = path.join(launch_directory, "random_search.toml") toml_options = toml.load(toml_path) @@ -74,29 +74,29 @@ def get_space_dict(launch_directory: str) -> Dict[str, Any]: def sampling_fn(value, sampling_type: str): if isinstance(value, (tuple, list)): - if sampling_type is "fixed": + if sampling_type == "fixed": return value - elif sampling_type is "choice": + elif sampling_type == "choice": return random.choice(value) - elif sampling_type is "exponent": + elif sampling_type == "exponent": exponent = random.uniform(*value) return 10**-exponent - elif sampling_type is "randint": + elif sampling_type == "randint": return random.randint(*value) - elif sampling_type is "uniform": + elif sampling_type == "uniform": return random.uniform(*value) else: raise NotImplementedError( f"Sampling type {sampling_type} is not implemented" ) else: - if sampling_type is "exponent": + if sampling_type == "exponent": return 10**-value else: return value -def random_sampling(rs_options: Dict[str, Any]) -> Dict[str, Any]: +def classification_random_sampling(rs_options: Dict[str, Any]) -> Dict[str, Any]: """ Samples all the hyperparameters of the model. Args: diff --git a/clinicadl/random_search/random_search_cli.py b/clinicadl/random_search/random_search_cli.py old mode 100644 new mode 100755 index 085af716a..3588aa7e6 --- a/clinicadl/random_search/random_search_cli.py +++ b/clinicadl/random_search/random_search_cli.py @@ -2,14 +2,24 @@ @click.command("random-search", no_args_is_help=True) +@click.argument( + "task", + type=click.Choice(["classification", "vae-architecture"]) +) @click.argument( "launch_directory", type=str, ) +@click.argument( + "toml_name", + type=str, +) @click.argument("name", type=str) def cli( + task, launch_directory, name, + toml_name, ): """Hyperparameter exploration using random search. @@ -17,9 +27,13 @@ def cli( NAME is the name of the output folder containing the experiment. """ - from .random_search import launch_search - launch_search(launch_directory, name) + if task == "classification": + from .random_search import launch_search + launch_search(task, launch_directory, name, toml_name) + elif task == "vae-architecture": + from.random_search import launch_vae_search + launch_vae_search(launch_directory, name, toml_name) if __name__ == "__main__": diff --git a/clinicadl/random_search/random_search_pythae_parameters.py b/clinicadl/random_search/random_search_pythae_parameters.py new file mode 100644 index 000000000..fc25d2fac --- /dev/null +++ b/clinicadl/random_search/random_search_pythae_parameters.py @@ -0,0 +1,115 @@ +RS_PYTHAE_DICT = { + "pythae_BetaVAE": { + "beta": "choice", + }, + "pythae_VAE_LinNF": { + "flows": "choice", + }, + "pythae_VAE_IAF": { + "n_made_blocks": "choice", + "n_hidden_in_made": "choice", + "hidden_size": "choice", + }, + "pythae_BetaTCVAE": { + "beta": "choice", + "alpha": "choice", + "gamma": "choice", + }, + "pythae_MSSSIM_VAE": { + # MS SSIM VAE + "beta": "choice", + "window_size": "choice", + }, + "pythae_VQVAE": { + # VQ VAE + "commitment_loss_factor": "choice", + "quantization_loss_factor": "choice", + "num_embeddings": "choice", + "use_ema": "choice", + "decay": "choice", + }, + "pythae_RAE_L2": { + # Regularized AE with L2 decoder param + "embedding_weight": "choice", + "reg_weight": "choice", + }, + "pythae_RAE_GP": { + # Regularized AE with gradient penalty + "embedding_weight": "choice", + "reg_weight": "choice", + }, + "pythae_Adversarial_AE": { + # Adversarial AE + "adversarial_loss_scale": "choice", + }, + "pythae_VAEGAN": { + # VAE GAN + "adversarial_loss_scale": "choice", + "reconstruction_layer": "choice", + "margin": "choice", + "equilibrium": "choice", + }, + "pythae_WAE_MMD": { + # Wasserstein Autoencoder + "kernel_choice": "choice", + "reg_weight": "choice", + "kernel_bandwidth": "choice", + }, + "pythae_INFOVAE_MMD": { + # Info VAE + "kernel_choice": "choice", + "alpha": "choice", + "lbd": "choice", + "kernel_bandwidth": "choice", + }, + "pythae_CIWAE": { + "beta": "choice", + "number_samples": "randint", + }, + "pythae_PIWAE": { + "number_gradient_estimates": "randint", + "number_samples": "randint", + }, + "pythae_MIWAE": { + "number_gradient_estimates": "randint", + "number_samples": "randint", + }, + "pythae_IWAE": { + "number_samples": "choice", + }, + "pythae_DisentangledBetaVAE": { + "beta": "choice", + "C": "choice", + "warmup_epoch": "choice", + }, + "pythae_FactorVAE": { + "gamma": "choice", + }, + "pythae_SVAE": { + }, + "pythae_PoincareVAE": { + "prior_distribution": "choice", + "posterior_distribution": "choice", + "curvature": "choice", + }, + "pythae_HVAE": { + "n_lf": "choice", + "eps_lf": "choice", + "beta_zero": "choice", + }, + "pythae_RHVAE": { + "n_lf": "choice", + "eps_lf": "choice", + "beta_zero": "choice", + "temperature": "choice", + "regularization": "choice", + }, + "pythae_VAMP": { + "number_components": "choice", + "linear_scheduling_steps": "choice", + }, + "pythae_MSSSIM_VAE": { + "beta": "choice", + "window_size": "choice", + } +} diff --git a/clinicadl/random_search/random_search_vae_utils.py b/clinicadl/random_search/random_search_vae_utils.py new file mode 100644 index 000000000..55d093c39 --- /dev/null +++ b/clinicadl/random_search/random_search_vae_utils.py @@ -0,0 +1,138 @@ +import random +from os import path +from typing import Any, Dict, Tuple + +import toml + +from clinicadl.random_search.random_search_classification_utils import sampling_fn +from clinicadl.random_search.random_search_pythae_parameters import RS_PYTHAE_DICT +from clinicadl.train.train_utils import build_train_dict +from clinicadl.utils.exceptions import ClinicaDLConfigurationError +from clinicadl.utils.preprocessing import read_preprocessing + + +def get_vae_space_dict(launch_directory, toml_name): + """ + Takes a launch directory with a "random_search.toml" file with all the parameters to explore. + Return a parameters dictionnary randomly sampled + """ + + toml_path = path.join(launch_directory, toml_name) + if not path.exists(toml_path): + raise FileNotFoundError( + f"TOML file {toml} must be written in directory {launch_directory}." + ) + + # load TOML file and create space dict + toml_options = toml.load(toml_path) + space_dict = dict() + + # check and read TOML + if "Random_Search" not in toml_options: + raise ClinicaDLConfigurationError( + "Category 'Random_Search' must be defined in the random_search.toml file. " + "All random search arguments AND options must be defined in this category." + ) + + for key in toml_options["Random_Search"]: + space_dict[key] = toml_options["Random_Search"][key] + + # Check presence of mandatory arguments + mandatory_arguments = [ + "network_task", + "tsv_path", + "caps_directory", + "preprocessing_json", + ] + + for argument in mandatory_arguments: + if argument not in space_dict: + raise ClinicaDLConfigurationError( + f"The argument {argument} must be specified in the random_search.toml file (Random_Search category)." + ) + + # Make training parameter dict + train_default = build_train_dict(toml_path, space_dict["network_task"]) + + # Mode and preprocessing + preprocessing_json = path.join( + space_dict["caps_directory"], + "tensor_extraction", + space_dict.pop("preprocessing_json"), + ) + + preprocessing_dict = read_preprocessing(preprocessing_json) + train_default["preprocessing_dict"] = preprocessing_dict + train_default["mode"] = preprocessing_dict["mode"] + + # Add the other default parameters to the dictionnary + # space_dict.update(train_default) + for k, v in train_default.items(): + if k not in space_dict: + space_dict[k] = v + return space_dict + + +def vae_random_sampling(space_dict): + # Create parameters dict + parameters = dict() + + sampling_vae_dict = { + "accumulation_steps": "fixed", + "baseline": "fixed", + "batch_size": "fixed", + "caps_directory": "fixed", + "channels_limit": "fixed", + "compensation": "fixed", + "data_augmentation": "fixed", + "deterministic": "fixed", + "diagnoses": "fixed", + "epochs": "fixed", + "evaluation_steps": "fixed", + "gpu": "fixed", + "label": "fixed", + "learning_rate": "fixed", + "mode": "fixed", + "multi_cohort": "fixed", + "n_splits": "fixed", + "n_proc": "fixed", + "network_task": "fixed", + "normalize": "fixed", + "optimizer": "choice", + "patience": "fixed", + "preprocessing_dict": "fixed", + "sampler": "fixed", + "seed": "fixed", + "selection_metrics": "fixed", + "size_reduction": "fixed", + "size_reduction_factor": "fixed", + "split": "fixed", + "tolerance": "fixed", + "transfer_path": "fixed", + "transfer_selection_metric": "fixed", + "tsv_path": "fixed", + "wd_bool": "fixed", + "weight_decay": "fixed", + "learning_rate": "choice", + # VAE architecture + "architecture": "choice", + "first_layer_channels": "choice", + "n_block_encoder": "randint", + "feature_size": "choice", + "latent_space_size": "choice", + "n_block_decoder": "randint", + "last_layer_channels": "choice", + "last_layer_conv": "choice", + "n_layer_per_block_encoder": "randint", + "n_layer_per_block_decoder": "randint", + "block_type": "choice", + } + if space_dict["architecture"] in RS_PYTHAE_DICT.keys(): + sampling_vae_dict.update(RS_PYTHAE_DICT[space_dict["architecture"]]) + + for name, sampling_type in sampling_vae_dict.items(): + if name in space_dict: + sampled_value = sampling_fn(space_dict[name], sampling_type) + parameters[name] = sampled_value + + return parameters diff --git a/clinicadl/resources/config/train_config.toml b/clinicadl/resources/config/train_config.toml old mode 100644 new mode 100755 index 4d6f4bce2..62eaf4f7c --- a/clinicadl/resources/config/train_config.toml +++ b/clinicadl/resources/config/train_config.toml @@ -10,12 +10,19 @@ multi_network = false dropout = 0.0 # between 0 and 1 # architecture.n_layers = 4 # VAE +first_layer_channels = 32 +n_block_encoder = 4 +feature_size = 0 latent_space_size = 128 -feature_size = 1024 -n_conv = 4 -io_layer_channels = 8 +n_block_decoder = 4 +last_layer_channels = 32 +last_layer_conv = false recons_weight = 1 -KL_weight = 1 +kl_weight = 1 +normalization = "batch" +n_layer_per_block_encoder = 1 +n_layer_per_block_decoder = 1 +block_type = "conv" [Classification] selection_metrics = ["loss"] @@ -33,6 +40,88 @@ loss = "MSELoss" selection_metrics = ["loss"] loss = "MSELoss" +[Pythae] +# Beta VAE +beta=5 +# Linear Normalizing Flow VAE +flows=['Planar', 'Radial', 'Planar'] +# Inverse Autoregressive Flows +n_made_blocks=2 +n_hidden_in_made=3 +hidden_size=128 +# Disentangled Beta VAE +#beta=5 +C=30.0 +warmup_epoch=25 +# Factor VAE +gamma=10 +# Beta TC VAE +#beta=2. +alpha=1 +#gamma=1 +# MS SSIM VAE +#beta=1e-2 +window_size=3 +# Info VAE +kernel_choice='imq' +#alpha=-2 +lbd=10 +kernel_bandwidth=1 +# Wasserstein Autoencoder +#kernel_choice='imq' +reg_weight=100 +#kernel_bandwidth=2 +# Hyperspherical VAE +# Poincare VAE +reconstruction_loss="bce" +prior_distribution="riemannian_normal" +posterior_distribution="wrapped_normal" +curvature=0.7 +# Adversarial AE +adversarial_loss_scale=0.9 +# VAE GAN +#adversarial_loss_scale=0.8 +reconstruction_layer=3 +margin=0.4 +equilibrium= 0.68 +# VQ VAE +commitment_loss_factor=0.25 +quantization_loss_factor=1.0 +num_embeddings=128 +use_ema=true +decay=0.99 +# Hamiltonian VAE +n_lf=1 +eps_lf=0.001 +beta_zero=0.3 +# Riemannian Hamiltonian VAE +#n_lf=1 +#eps_lf=0.001 +#beta_zero=0.3 +temperature=1.5 +regularization=0.001 +# Importance Weighted Autoencoder +number_samples=3 +# Multiply Importance Weighted Autoencoder +number_gradient_estimates=4 +#number_samples=4 +# Partially Importance Weighted Autoencoder +#number_gradient_estimates=4 +#number_samples=4 +# Combination Importance Weighted Autoencoder +#beta=0.05 +#number_samples=4 +# VAMP Autoencoder +number_components=50 +linear_scheduling_steps=0 +# Regularized AE with L2 decoder param +embedding_weight=1e-2 +#reg_weight=1e-4 +# Regularized AE with gradient penalty +#embedding_weight=1e-2 +#reg_weight=1e-4 +sample_latent = 0 + [Computational] gpu = true n_proc = 2 @@ -59,6 +148,8 @@ baseline = false normalize = true data_augmentation = false sampler = "random" +size_reduction=false +size_reduction_factor=2 [Cross_validation] n_splits = 0 diff --git a/clinicadl/resources/masks/README.md b/clinicadl/resources/masks/README.md old mode 100644 new mode 100755 diff --git a/clinicadl/resources/models/README.md b/clinicadl/resources/models/README.md old mode 100644 new mode 100755 diff --git a/clinicadl/train/from_json_cli.py b/clinicadl/train/from_json_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/train/list_models_cli.py b/clinicadl/train/list_models_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/train/resume.py b/clinicadl/train/resume.py old mode 100644 new mode 100755 index 0dab8e8d4..f520614d8 --- a/clinicadl/train/resume.py +++ b/clinicadl/train/resume.py @@ -62,4 +62,4 @@ def automatic_resume(model_path, user_split_list=None, verbose=0): if len(stopped_splits) > 0: maps_manager.resume(stopped_splits) if len(absent_splits) > 0: - maps_manager.train(absent_splits, overwrite=True) + maps_manager.train_pythae(absent_splits) diff --git a/clinicadl/train/resume_cli.py b/clinicadl/train/resume_cli.py old mode 100644 new mode 100755 index 377ce7452..4f59f7238 --- a/clinicadl/train/resume_cli.py +++ b/clinicadl/train/resume_cli.py @@ -1,7 +1,7 @@ import click -from clinicadl.train import train_option from clinicadl.utils import cli_param +from clinicadl.utils.cli_param import train_option @click.command(name="resume", no_args_is_help=True) diff --git a/clinicadl/train/tasks/__init__.py b/clinicadl/train/tasks/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/train/tasks/classification_cli.py b/clinicadl/train/tasks/classification_cli.py old mode 100644 new mode 100755 index 0b90a265f..075c8d67f --- a/clinicadl/train/tasks/classification_cli.py +++ b/clinicadl/train/tasks/classification_cli.py @@ -1,6 +1,7 @@ import click -from .. import train_option +from clinicadl.utils.cli_param import train_option + from .task_utils import task_launcher diff --git a/clinicadl/train/tasks/pythae_cli.py b/clinicadl/train/tasks/pythae_cli.py new file mode 100644 index 000000000..29783b6b3 --- /dev/null +++ b/clinicadl/train/tasks/pythae_cli.py @@ -0,0 +1,59 @@ +import click + +import os + +from clinicadl.utils.cli_param import train_option + +from clinicadl.train.train_utils import build_train_dict +from clinicadl.utils.preprocessing import read_preprocessing +from clinicadl import MapsManager + + +@click.command(name="pythae", no_args_is_help=True) +# Mandatory arguments +@train_option.caps_directory +@train_option.preprocessing_json +@train_option.tsv_directory +@train_option.output_maps +# Options +@train_option.config_file +@train_option.split +def cli(caps_directory, preprocessing_json, tsv_directory, output_maps_directory, config_file, split): + """ + Train a deep learning model to learn a vae variant using Pythae on neuroimaging data. + + CAPS_DIRECTORY is the CAPS folder from where tensors will be loaded. + + PREPROCESSING_JSON is the name of the JSON file in CAPS_DIRECTORY/tensor_extraction folder where + all information about extraction are stored in order to read the wanted tensors. + + TSV_DIRECTORY is a folder were TSV files defining train and validation sets are stored. + + OUTPUT_MAPS is the path to the MAPS folder where outputs and results will be saved. + + Options for this command can be input by providing a configuration file in TOML format. + For more details, please visit the documentation: + https://clinicadl.readthedocs.io/en/stable/Train/Introduction/#configuration-file + """ + + parameters = build_train_dict(config_file, "reconstruction") + + parameters["network_task"] = "reconstruction" + parameters["caps_directory"] = caps_directory + parameters["tsv_path"] = tsv_directory + parameters["mode"] = "image" + + preprocessing_json_path = os.path.join( + caps_directory, + "tensor_extraction", + preprocessing_json, + ) + parameters["preprocessing_dict"] = read_preprocessing(preprocessing_json_path) + + if split is not None: + parameters["split"] = split + + maps_manager = MapsManager(output_maps_directory, parameters, verbose="info") + # launch training procedure for Pythae + maps_manager.train_pythae(split_list=parameters["split"]) + \ No newline at end of file diff --git a/clinicadl/train/tasks/reconstruction_cli.py b/clinicadl/train/tasks/reconstruction_cli.py old mode 100644 new mode 100755 index 1c40a12cb..81c5b68b0 --- a/clinicadl/train/tasks/reconstruction_cli.py +++ b/clinicadl/train/tasks/reconstruction_cli.py @@ -1,6 +1,7 @@ import click -from .. import train_option +from clinicadl.utils.cli_param import train_option + from .task_utils import task_launcher diff --git a/clinicadl/train/tasks/regression_cli.py b/clinicadl/train/tasks/regression_cli.py old mode 100644 new mode 100755 index 0dada54db..7b40500a1 --- a/clinicadl/train/tasks/regression_cli.py +++ b/clinicadl/train/tasks/regression_cli.py @@ -1,6 +1,7 @@ import click -from .. import train_option +from clinicadl.utils.cli_param import train_option + from .task_utils import task_launcher diff --git a/clinicadl/train/tasks/task_utils.py b/clinicadl/train/tasks/task_utils.py old mode 100644 new mode 100755 index 8de19335b..23532470b --- a/clinicadl/train/tasks/task_utils.py +++ b/clinicadl/train/tasks/task_utils.py @@ -22,7 +22,7 @@ def task_launcher(network_task: str, task_options_list: List[str], **kwargs): config_file_name = None if kwargs["config_file"]: - config_file_name = kwargs["config_file"].name + config_file_name = kwargs["config_file"] train_dict = build_train_dict(config_file_name, network_task) # Add arguments diff --git a/clinicadl/train/train.py b/clinicadl/train/train.py old mode 100644 new mode 100755 diff --git a/clinicadl/train/train_cli.py b/clinicadl/train/train_cli.py old mode 100644 new mode 100755 index a256f09bc..94ccb594d --- a/clinicadl/train/train_cli.py +++ b/clinicadl/train/train_cli.py @@ -6,6 +6,7 @@ from .tasks.classification_cli import cli as classification_cli from .tasks.reconstruction_cli import cli as reconstruction_cli from .tasks.regression_cli import cli as regression_cli +from .tasks.pythae_cli import cli as pythae_cli @click.group(name="train", no_args_is_help=True) @@ -20,6 +21,7 @@ def cli(): cli.add_command(from_json_cli) cli.add_command(resume_cli) cli.add_command(list_models_cli) +cli.add_command(pythae_cli) if __name__ == "__main__": diff --git a/clinicadl/train/train_utils.py b/clinicadl/train/train_utils.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/__init__.py b/clinicadl/tsvtools/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/analysis/__init__.py b/clinicadl/tsvtools/analysis/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/analysis/analysis.py b/clinicadl/tsvtools/analysis/analysis.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/analysis/analysis_cli.py b/clinicadl/tsvtools/analysis/analysis_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/cli.py b/clinicadl/tsvtools/cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/get_labels/get_labels.py b/clinicadl/tsvtools/get_labels/get_labels.py new file mode 100644 index 000000000..96861ba6d --- /dev/null +++ b/clinicadl/tsvtools/get_labels/get_labels.py @@ -0,0 +1,416 @@ +# coding: utf8 + +""" +Source files can be obtained by running the following commands on a BIDS folder: + - clinica iotools merge-tsv + - clinica iotools check-missing-modalities +To download Clinica follow the instructions at http://www.clinica.run/doc/#installation + +NB: Other preprocessing may be needed on the merged file obtained: for example the selection of subjects older than 62 +in the OASIS dataset is not done in this script. Moreover a quality check may be needed at the end of preprocessing +pipelines, leading to the removal of some subjects. +""" +from copy import copy +from logging import getLogger +from pathlib import Path +from typing import Dict, List + +import numpy as np +import pandas as pd + +from clinicadl.utils.exceptions import ClinicaDLArgumentError, ClinicaDLTSVError +from clinicadl.utils.maps_manager.iotools import commandline_to_json +from clinicadl.utils.tsvtools_utils import ( + cleaning_nan_diagnoses, + find_label, + first_session, + last_session, + neighbour_session, +) + +logger = getLogger("clinicadl.tsvtools") + + +def infer_or_drop_diagnosis(bids_df: pd.DataFrame) -> pd.DataFrame: + """ + Deduce the diagnosis when missing from previous and following sessions of the subject. If not identical, the session + is dropped. Sessions with no diagnosis are also dropped when there are the last sessions of the follow-up. + + Parameters + ---------- + bids_df: DataFrame + Columns including ['participant_id', 'session_id', 'diagnosis']. + + Returns + ------- + bids_copy_df: DataFrame + Cleaned copy of the input bids_df. + """ + bids_copy_df = copy(bids_df) + found_diag_interpol = 0 + nb_drop = 0 + + for subject, subject_df in bids_df.groupby(level=0): + session_list = [] + for _, session in subject_df.index.values: + x = session[5::] + if not x.isdigit(): + subject_df.drop((subject, session), axis=0, inplace=True) + bids_copy_df.drop((subject, session), axis=0, inplace=True) + nb_drop += 1 + + session_list = [session for _, session in subject_df.index.values] + + for _, session in subject_df.index.values: + diagnosis = subject_df.loc[(subject, session), "diagnosis"] + session_nb = session + + if isinstance(diagnosis, float): + if session == last_session(session_list): + bids_copy_df.drop(index=(_, session), axis=0, inplace=True) + nb_drop += 1 + else: + prev_session = neighbour_session(session_nb, session_list, -1) + prev_diagnosis = bids_df.loc[(subject, prev_session), "diagnosis"] + while isinstance( + prev_diagnosis, float + ) and prev_session != first_session(subject_df): + prev_session = neighbour_session(prev_session, session_list, -1) + prev_diagnosis = bids_df.loc[ + (subject, prev_session), "diagnosis" + ] + post_session = neighbour_session(session_nb, session_list, +1) + post_diagnosis = bids_df.loc[(subject, post_session), "diagnosis"] + while isinstance( + post_diagnosis, float + ) and post_session != last_session(session_list): + post_session = neighbour_session(post_session, session_list, +1) + post_diagnosis = bids_df.loc[ + (subject, post_session), "diagnosis" + ] + if prev_diagnosis == post_diagnosis: + found_diag_interpol += 1 + bids_copy_df.loc[(subject, session), "diagnosis"] = ( + prev_diagnosis + ) + else: + bids_copy_df.drop((subject, session), inplace=True) + nb_drop += 1 + + logger.info(f"Inferred diagnosis: {found_diag_interpol}") + logger.info(f"Dropped subjects (inferred diagnosis): {nb_drop}") + + return bids_copy_df + + +def mod_selection( + bids_df: pd.DataFrame, missing_mods_dict: Dict[str, pd.DataFrame], mod: str = "t1w" +) -> pd.DataFrame: + """ + Select only sessions for which the modality is present + + Parameters + ---------- + bids_df: DataFrame + Columns include ['participant_id', 'session_id', 'diagnosis'] + missing_mods_dict: dictionary of str and DataFrame + DataFrames of missing modalities + mod: str + the modality used for selection + + Returns + ------- + copy_bids_df: DataFrame + Cleaned copy of the input bids_df + """ + bids_copy_df = copy(bids_df) + nb_subjects = 0 + if mod is not None: + for subject, session in bids_df.index.values: + try: + mod_present = missing_mods_dict[session].loc[subject, mod] + if not mod_present: + bids_copy_df.drop((subject, session), inplace=True) + nb_subjects += 1 + except KeyError: + bids_copy_df.drop((subject, session), inplace=True) + nb_subjects += 1 + logger.info(f"Dropped sessions (mod selection): {nb_subjects}") + return bids_copy_df + + +def remove_unique_session(bids_df: pd.DataFrame) -> pd.DataFrame: + """ + A method to get the subgroup for each sessions depending on their stability on the time horizon + + Parameters + ---------- + bids_df: DataFrame + Columns include ['participant_id', 'session_id', 'diagnosis'] + + Returns + ------- + bids_copy_df: DataFrame + Cleaned copy of the input bids_df + """ + bids_copy_df = copy(bids_df) + nb_unique = 0 + + for subject, subject_df in bids_df.groupby(level=0): + session_list = [session for _, session in subject_df.index.values] + session_list.sort() + nb_session = len(session_list) + if nb_session == 1: + bids_copy_df.drop((subject, session_list[0]), inplace=True) + subject_df.drop((subject, session_list[0]), inplace=True) + nb_unique += 1 + logger.info(f"Dropped subjects (unique session): {nb_unique}") + + return bids_copy_df + + +def diagnosis_removal(bids_df: pd.DataFrame, diagnosis_list: List[str]) -> pd.DataFrame: + """ + Removes sessions for which the diagnosis is not in the list provided + + Parameters + ---------- + bids_df: DataFrame + Columns must includes ['participant_id', 'session_id', 'diagnosis'] + diagnosis_list: list of str + List of diagnoses that will be removed + + Returns + ------- + output_df: DataFrame + Cleaned copy of the input bids_df + + """ + + output_df = copy(bids_df) + nb_subjects = 0 + for subject, subject_df in bids_df.groupby(level=0): + for _, session in subject_df.index.values: + group = subject_df.loc[(subject, session), "diagnosis"] + if group not in diagnosis_list: + output_df.drop((subject, session), inplace=True) + nb_subjects += 1 + + logger.info(f"Dropped subjects (diagnoses): {nb_subjects}") + return output_df + + +def apply_restriction(bids_df: pd.DataFrame, restriction_path: Path) -> pd.DataFrame: + """ + Application of a restriction (for example after the removal of some subjects after a preprocessing pipeline) + + Parameters + ---------- + bids_df: DataFrame + Columns must include ['participant_id', 'session_id', 'diagnosis'] + restriction_path: str (path) + Path to a tsv file with columns including ['participant_id', 'session_id', 'diagnosis'] including + all the sessions that can be included + + Returns + ------- + bids_copy_df: DataFrame + Cleaned copy of the input bids_df + """ + bids_copy_df = copy(bids_df) + nb_subjects = 0 + if restriction_path is not None: + restriction_df = pd.read_csv(restriction_path, sep="\t") + + for subject, session in bids_df.index.values: + subject_qc_df = restriction_df[ + (restriction_df.participant_id == subject) + & (restriction_df.session_id == session) + ] + if len(subject_qc_df) != 1: + bids_copy_df.drop((subject, session), inplace=True) + nb_subjects += 1 + logger.info(f"Dropped subjects (apply restriction): {nb_subjects}") + return bids_copy_df + + +def get_labels( + bids_directory: Path, + diagnoses: List[str], + modality: str = "t1w", + restriction_path: Path = None, + variables_of_interest: List[str] = None, + remove_smc: bool = True, + merged_tsv: Path = None, + missing_mods: Path = None, + remove_unique_session_: bool = False, + output_dir: Path = None, + caps_directory: Path = None, +): + """ + Writes one TSV file based on merged_tsv and missing_mods. + + + Parameters + ---------- + bids_directory: str (path) + Path to the folder containing the dataset in a BIDS hierarchy. + diagnoses: List of str + Labels that must be extracted from merged_tsv. + modality: str + Modality to select sessions. Sessions which do not include the modality will be excluded. + restriction_path: str (path) + Path to a tsv containing the sessions that can be included. + variables_of_interest: List of str + Columns that should be kept in the output tsv files. + remove_smc: bool + If True SMC participants are removed from the lists. + caps_directory: str (path) + Path to a folder of a older of a CAPS compliant dataset + merged_tsv: str (path) + Path to the output of clinica iotools merge-tsv if already exists + missing_mods: str (path) + Path to the output directory of clinica iotools check-missing-modalities if already exists + remove_unique_session: bool + If True, subjects with only one session are removed. + output_dir: str (path) + Path to the directory where the output labels.tsv will be stored. + """ + + if not output_dir.is_dir(): + output_dir.mkdir(parents=True, exist_ok=True) + + output_tsv = output_dir / "labels.tsv" + + commandline_to_json( + { + "bids_directory": bids_directory, + "output_dir": output_dir, + "diagnoses": diagnoses, + "modality": modality, + "restriction_path": restriction_path, + "variables_of_interest": variables_of_interest, + "remove_smc": remove_smc, + "missing_mods": missing_mods, + "merged_tsv": merged_tsv, + "remove_unique_session": remove_unique_session_, + "caps_directory": caps_directory, + }, + filename="labels.json", + ) + + # Generating the output of `clinica iotools check-missing-modalities`` + missing_mods_directory = output_dir / "missing_mods" + if missing_mods is not None: + missing_mods_directory = missing_mods + + if not missing_mods_directory.is_dir(): + raise ValueError( + f"The missing_mods directory doesn't exist: {missing_mods}, please give another directory." + ) + + logger.info( + f"output of clinica iotools check-missing-modalities: {missing_mods_directory}" + ) + + # Generating the output of `clinica iotools merge-tsv ` + if not merged_tsv: + merged_tsv = output_dir / "merged.tsv" + if merged_tsv.is_file(): + logger.warning( + f"A merged_tsv file already exists at {merged_tsv}. It will be used to run the command." + ) + else: + raise ValueError( + "We can't find any merged tsv files, please give another path." + ) + + logger.info(f"output of clinica iotools merge-tsv: {merged_tsv}") + + # Reading files + if not merged_tsv.is_file(): + raise ClinicaDLTSVError(f"{merged_tsv} file was not found. ") + bids_df = pd.read_csv(merged_tsv, sep="\t", low_memory=False) + bids_df.set_index(["participant_id", "session_id"], inplace=True) + variables_list = [] + + if "dx1" in bids_df.columns: + bids_df.rename(columns={"dx1": "diagnosis"}, inplace=True) + + try: + variables_list.append(find_label(bids_df.columns.values, "age")) + variables_list.append(find_label(bids_df.columns.values, "sex")) + variables_list.append(find_label(bids_df.columns.values, "diagnosis")) + except ValueError: + logger.warning( + "The age, sex or diagnosis values were not found in the dataset." + ) + + # Cleaning NaN diagnosis + logger.debug("Cleaning NaN diagnosis") + bids_df = cleaning_nan_diagnoses(bids_df) + + # Checking the variables of interest + if variables_of_interest is not None: + variables_set = set(variables_of_interest) | set(variables_list) + variables_list = list(variables_set) + if not set(variables_list).issubset(set(bids_df.columns.values)): + raise ClinicaDLArgumentError( + f"The variables asked by the user {variables_of_interest} do not " + f"exist in the data set." + ) + + # Loading missing modalities files + list_files = list(missing_mods_directory.iterdir()) + missing_mods_dict = {} + for file in list_files: + fileext = file.suffix + filename = file.stem + if fileext == ".tsv": + session = filename.split("_")[-1] + missing_mods_df = pd.read_csv(file, sep="\t") + if len(missing_mods_df) == 0: + raise ClinicaDLTSVError( + f"Given TSV file at {file} loads an empty DataFrame." + ) + + missing_mods_df.set_index("participant_id", drop=True, inplace=True) + missing_mods_dict[session] = missing_mods_df + + # Remove SMC patients + if remove_smc: + if "diagnosis_bl" in bids_df.columns.values: # Retro-compatibility + bids_df = bids_df[~(bids_df.diagnosis_bl == "SMC")] + if "diagnosis_sc" in bids_df.columns.values: + bids_df = bids_df[~(bids_df.diagnosis_sc == "SMC")] + + # Adding the field baseline_diagnosis + bids_copy_df = copy(bids_df) + bids_copy_df["baseline_diagnosis"] = pd.Series( + np.zeros(len(bids_df)), index=bids_df.index + ) + for subject, subject_df in bids_df.groupby(level=0): + baseline_diagnosis = subject_df.loc[ + (subject, first_session(subject_df)), "diagnosis" + ] + bids_copy_df.loc[subject, "baseline_diagnosis"] = baseline_diagnosis + + bids_df = copy(bids_copy_df) + variables_list.append("baseline_diagnosis") + + bids_df = bids_df[variables_list] + if remove_unique_session_: + bids_df = remove_unique_session(bids_df) + + variables_list.remove("baseline_diagnosis") + output_df = bids_df[variables_list] + output_df = infer_or_drop_diagnosis(output_df) + output_df = diagnosis_removal(output_df, diagnoses) + output_df = mod_selection(output_df, missing_mods_dict, modality) + output_df = apply_restriction(output_df, restriction_path) + + output_df.reset_index() + output_df.sort_values(by=["participant_id", "session_id"], inplace=True) + output_df.to_csv(output_tsv, sep="\t") + + logger.info(f"Results are stored in {output_dir}.") diff --git a/clinicadl/tsvtools/getlabels/__init__.py b/clinicadl/tsvtools/getlabels/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/getlabels/getlabels.py b/clinicadl/tsvtools/getlabels/getlabels.py old mode 100644 new mode 100755 index 6c6eeb412..d774a68b2 --- a/clinicadl/tsvtools/getlabels/getlabels.py +++ b/clinicadl/tsvtools/getlabels/getlabels.py @@ -137,9 +137,9 @@ def infer_or_drop_diagnosis(bids_df: pd.DataFrame) -> pd.DataFrame: ] if prev_diagnosis == post_diagnosis: found_diag_interpol += 1 - bids_copy_df.loc[ - (subject, session), "diagnosis" - ] = prev_diagnosis + bids_copy_df.loc[(subject, session), "diagnosis"] = ( + prev_diagnosis + ) else: bids_copy_df.drop((subject, session), inplace=True) @@ -309,10 +309,9 @@ def mci_stability(bids_df: pd.DataFrame, horizon_time: int = 36) -> pd.DataFrame update_diagnosis = stability_dict[last_diagnosis] + "MCI" else: update_diagnosis = "uMCI" - # print(update_diagnosis) - bids_copy_df.loc[ - (subject, session), "diagnosis" - ] = update_diagnosis + bids_copy_df.loc[(subject, session), "diagnosis"] = ( + update_diagnosis + ) else: prev_session = neighbour_session( @@ -336,10 +335,9 @@ def mci_stability(bids_df: pd.DataFrame, horizon_time: int = 36) -> pd.DataFrame update_diagnosis = "uMCI" else: update_diagnosis = "sMCI" - # print(update_diagnosis) - bids_copy_df.loc[ - (subject, session), "diagnosis" - ] = update_diagnosis + bids_copy_df.loc[(subject, session), "diagnosis"] = ( + update_diagnosis + ) return bids_copy_df diff --git a/clinicadl/tsvtools/getlabels/getlabels_cli.py b/clinicadl/tsvtools/getlabels/getlabels_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/kfold/__init__.py b/clinicadl/tsvtools/kfold/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/kfold/kfold.py b/clinicadl/tsvtools/kfold/kfold.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/kfold/kfold_cli.py b/clinicadl/tsvtools/kfold/kfold_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/restrict/__init__.py b/clinicadl/tsvtools/restrict/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/restrict/restrict.py b/clinicadl/tsvtools/restrict/restrict.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/restrict/restrict_cli.py b/clinicadl/tsvtools/restrict/restrict_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/split/__init__.py b/clinicadl/tsvtools/split/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/split/split.py b/clinicadl/tsvtools/split/split.py old mode 100644 new mode 100755 diff --git a/clinicadl/tsvtools/split/split_cli.py b/clinicadl/tsvtools/split/split_cli.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/__init__.py b/clinicadl/utils/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/caps_dataset/__init__.py b/clinicadl/utils/caps_dataset/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/caps_dataset/caps_dataset_refactoring/caps_dataset.py b/clinicadl/utils/caps_dataset/caps_dataset_refactoring/caps_dataset.py new file mode 100644 index 000000000..611a5d0e9 --- /dev/null +++ b/clinicadl/utils/caps_dataset/caps_dataset_refactoring/caps_dataset.py @@ -0,0 +1,1225 @@ +# coding: utf8 + +import abc +from logging import getLogger +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import pandas as pd +import torch +import torchio as tio +import torchvision.transforms as transforms +from torch.utils.data import Dataset + +from clinicadl.extract.extract_utils import ( + PATTERN_DICT, + TEMPLATE_DICT, + compute_discarded_slices, + compute_folder_and_file_type, + extract_patch_path, + extract_patch_tensor, + extract_roi_path, + extract_roi_tensor, + extract_slice_path, + extract_slice_tensor, + find_mask_path, +) +from clinicadl.utils.exceptions import ( + ClinicaDLArgumentError, + ClinicaDLCAPSError, + ClinicaDLConfigurationError, + ClinicaDLTSVError, +) + +logger = getLogger("clinicadl.caps_dataset") + + +################################# +# Datasets loaders +################################# +class CapsDataset(Dataset): + """Abstract class for all derived CapsDatasets.""" + + def __init__( + self, + caps_directory: Path, + tsv_label: Path, + preprocessing_dict: Dict[str, Any], + transformations: Optional[Callable], + label_presence: bool, + label: str = None, + label_code: Dict[Any, int] = None, + augmentation_transformations: Optional[Callable] = None, + ): + from clinicadl.utils.preprocessing import read_preprocessing + + self.caps_directory = caps_directory + self.caps_dict = self.create_caps_dict(caps_directory) + self.transformations = transformations + self.augmentation_transformations = augmentation_transformations + self.eval_mode = False + self.label_presence = label_presence + self.label = label + self.label_code = label_code + self.preprocessing_dict = read_preprocessing(preprocessing_dict) + + if not hasattr(self, "elem_index"): + raise AttributeError( + "Child class of CapsDataset must set elem_index attribute." + ) + if not hasattr(self, "mode"): + raise AttributeError("Child class of CapsDataset, must set mode attribute.") + + self.df = pd.read_csv(tsv_label, sep="\t") + mandatory_col = { + "participant_id", + "session_id", + } + + # check label presence and label to include it in the mandatory col + if self.label_presence and self.label is not None: + mandatory_col.add(self.label) + + if not mandatory_col.issubset(set(self.df.columns.values)): + raise Exception( + f"the tsv file is not in the correct format." + f"Columns should include {mandatory_col}" + ) + self.elem_per_image = self.num_elem_per_image() + self.size = self.size_elem() + + @property + @abc.abstractmethod + def elem_index(self): + pass + + def label_fn(self, target: Union[str, float, int]) -> Optional[Union[float, int]]: + """ + Returns the label value usable in criterion. + + Args: + target: value of the target. + Returns: + label: value of the label usable in criterion. + """ + + # Reconstruction case (no label) + if self.label is None: + return None + # Regression case (no label code) + elif self.label_code is None: + return np.float32([target]) + # Classification case (label + label_code dict) + else: + return self.label_code[str(target)] + + def domain_fn(self, target: Union[str, float, int]) -> Union[float, int]: + """ + Returns the label value usable in criterion. + + Args: + target: value of the target. + Returns: + label: value of the label usable in criterion. + """ + domain_code = {"t1": 0, "flair": 1} + return domain_code[str(target)] + + def __len__(self) -> int: + return len(self.df) * self.elem_per_image + + @staticmethod + def create_caps_dict( + caps_directory: Path, + ) -> Dict[str, Path]: + from clinicadl.utils.clinica_utils import check_caps_folder + + check_caps_folder(caps_directory) + caps_dict = {"single": caps_directory} + return caps_dict + + def _get_image_path(self, participant: str, session: str) -> Path: + """ + Gets the path to the tensor image (*.pt) + + Args: + participant: ID of the participant. + session: ID of the session. + Returns: + image_path: path to the tensor containing the whole image. + """ + from clinicadl.utils.clinica_utils import clinicadl_file_reader + + # Try to find .nii.gz file + try: + file_type = self.preprocessing_dict["file_type"] + results = clinicadl_file_reader( + subjects=[participant], + sessions=[session], + input_directory=self.caps_directory, + information=file_type, + ) + logger.debug(f"clinica_file_reader output: {results}") + filepath = Path(results[0][0]) + image_filename = filepath.name.replace(".nii.gz", ".pt") + + folder, _ = compute_folder_and_file_type(self.preprocessing_dict) + image_dir = ( + self.caps_directory + / "subjects" + / participant + / session + / "deeplearning_prepare_data" + / "image_based" + / folder + ) + image_path = image_dir / image_filename + # Try to find .pt file + except ClinicaDLCAPSError: + file_type = self.preprocessing_dict["file_type"] + file_type["pattern"] = file_type["pattern"].replace(".nii.gz", ".pt") + results = clinicadl_file_reader( + [participant], [session], self.caps_directory, file_type + ) + filepath = results[0] + image_path = Path(filepath[0]) + + return image_path + + def _get_meta_data(self, idx: int) -> Tuple[str, str, str, int, int]: + """ + Gets all meta data necessary to compute the path with _get_image_path + + Args: + idx (int): row number of the meta-data contained in self.df + Returns: + participant (str): ID of the participant. + session (str): ID of the session. + elem_index (int): Index of the part of the image. + label (str or float or int): value of the label to be used in criterion. + """ + image_idx = idx // self.elem_per_image + participant = self.df.loc[image_idx, "participant_id"] + session = self.df.loc[image_idx, "session_id"] + + if self.elem_index is None: + elem_idx = idx % self.elem_per_image + else: + elem_idx = self.elem_index + if self.label_presence and self.label is not None: + target = self.df.loc[image_idx, self.label] + label = self.label_fn(target) + else: + label = -1 + + if "domain" in self.df.columns: + domain = self.df.loc[image_idx, "domain"] + domain = self.domain_fn(domain) + else: + domain = "" # TO MODIFY + return participant, session, elem_idx, label, domain + + def _get_full_image(self) -> torch.Tensor: + """ + Allows to get the an example of the image mode corresponding to the dataset. + Useful to compute the number of elements if mode != image. + + Returns: + image tensor of the full image first image. + """ + import nibabel as nib + + from clinicadl.utils.clinica_utils import clinicadl_file_reader + + participant_id = self.df.loc[0, "participant_id"] + session_id = self.df.loc[0, "session_id"] + + try: + image_path = self._get_image_path(participant_id, session_id) + image = torch.load(image_path) + except IndexError: + file_type = self.preprocessing_dict["file_type"] + results = clinicadl_file_reader( + [participant_id], [session_id], self.caps_directory, file_type + ) + image_nii = nib.load(results[0]) + image_np = image_nii.get_fdata() + image = ToTensor()(image_np) + + return image + + @abc.abstractmethod + def __getitem__(self, idx: int) -> Dict[str, Any]: + """ + Gets the sample containing all the information needed for training and testing tasks. + + Args: + idx: row number of the meta-data contained in self.df + Returns: + dictionary with following items: + - "image" (torch.Tensor): the input given to the model, + - "label" (int or float): the label used in criterion, + - "participant_id" (str): ID of the participant, + - "session_id" (str): ID of the session, + - f"{self.mode}_id" (int): number of the element, + - "image_path": path to the image loaded in CAPS. + + """ + pass + + @abc.abstractmethod + def num_elem_per_image(self) -> int: + """Computes the number of elements per image based on the full image.""" + pass + + def size_elem(self) -> int: + """Computes the number of elements per image based on the full image.""" + return self[0]["image"].size() + + def eval(self): + """Put the dataset on evaluation mode (data augmentation is not performed).""" + self.eval_mode = True + return self + + def train(self): + """Put the dataset on training mode (data augmentation is performed).""" + self.eval_mode = False + return self + + +class CapsDatasetImage(CapsDataset): + """Dataset of MRI organized in a CAPS folder.""" + + def __init__( + self, + caps_directory: Path, + tsv_label: Path, + preprocessing_dict: Dict[str, Any], + train_transformations: Optional[Callable] = None, + label_presence: bool = True, + label: str = None, + label_code: Dict[str, int] = None, + all_transformations: Optional[Callable] = None, + ): + """ + Args: + caps_directory: Directory of all the images. + data_file: Path to the tsv file or DataFrame containing the subject/session list. + preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data. + train_transformations: Optional transform to be applied only on training mode. + label_presence: If True the diagnosis will be extracted from the given DataFrame. + label: Name of the column in data_df containing the label. + label_code: label code that links the output node number to label value. + all_transformations: Optional transform to be applied during training and evaluation. + multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths. + + """ + + self.mode = "image" + super().__init__( + caps_directory, + tsv_label, + preprocessing_dict, + augmentation_transformations=train_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + transformations=all_transformations, + ) + + self.prepare_dl = self.preprocessing_dict["prepare_dl"] + + @property + def elem_index(self): + return None + + def __getitem__(self, idx): + participant, session, _, label, domain = self._get_meta_data(idx) + + image_path = self._get_image_path(participant, session) + image = torch.load(image_path) + + if self.transformations: + image = self.transformations(image) + + if self.augmentation_transformations and not self.eval_mode: + image = self.augmentation_transformations(image) + + sample = { + "image": image, + "label": label, + "participant_id": participant, + "session_id": session, + "image_id": 0, + "image_path": image_path.as_posix(), + "domain": domain, + } + + return sample + + def num_elem_per_image(self): + return 1 + + +class CapsDatasetPatch(CapsDataset): + def __init__( + self, + caps_directory: Path, + tsv_label: Path, + preprocessing_dict: Dict[str, Any], + train_transformations: Optional[Callable] = None, + patch_index: Optional[int] = None, + label_presence: bool = True, + label: str = None, + label_code: Dict[str, int] = None, + all_transformations: Optional[Callable] = None, + multi_cohort: bool = False, + ): + """ + Args: + caps_directory: Directory of all the images. + data_file: Path to the tsv file or DataFrame containing the subject/session list. + preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data. + train_transformations: Optional transform to be applied only on training mode. + patch_index: If a value is given the same patch location will be extracted for each image. + else the dataset will load all the patches possible for one image. + label_presence: If True the diagnosis will be extracted from the given DataFrame. + label: Name of the column in data_df containing the label. + label_code: label code that links the output node number to label value. + all_transformations: Optional transform to be applied during training and evaluation. + multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths. + + """ + super().__init__( + caps_directory, + tsv_label, + preprocessing_dict, + augmentation_transformations=train_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + transformations=all_transformations, + multi_cohort=multi_cohort, + ) + + self.patch_size = self.preprocessing_dict["patch_size"] + self.stride_size = self.preprocessing_dict["stride_size"] + self.patch_index = patch_index + self.mode = "patch" + self.prepare_dl = self.preprocessing_dict["prepare_dl"] + + @property + def elem_index(self): + return self.patch_index + + def __getitem__(self, idx): + participant, session, cohort, patch_idx, label, domain = self._get_meta_data( + idx + ) + image_path = self._get_image_path(participant, session, cohort) + + if self.prepare_dl: + patch_dir = image_path.parent.as_posix().replace( + "image_based", f"{self.mode}_based" + ) + patch_filename = extract_patch_path( + image_path, self.patch_size, self.stride_size, patch_idx + ) + patch_tensor = torch.load(Path(patch_dir).resolve() / patch_filename) + + else: + image = torch.load(image_path) + patch_tensor = extract_patch_tensor( + image, self.patch_size, self.stride_size, patch_idx + ) + + if self.transformations: + patch_tensor = self.transformations(patch_tensor) + + if self.augmentation_transformations and not self.eval_mode: + patch_tensor = self.augmentation_transformations(patch_tensor) + + sample = { + "image": patch_tensor, + "label": label, + "participant_id": participant, + "session_id": session, + "patch_id": patch_idx, + } + + return sample + + def num_elem_per_image(self) -> int: + if self.elem_index is not None: + return 1 + + image = self._get_full_image() + + patches_tensor = ( + image.unfold(1, self.patch_size, self.stride_size) + .unfold(2, self.patch_size, self.stride_size) + .unfold(3, self.patch_size, self.stride_size) + .contiguous() + ) + patches_tensor = patches_tensor.view( + -1, self.patch_size, self.patch_size, self.patch_size + ) + num_patches = patches_tensor.shape[0] + return num_patches + + +class CapsDatasetRoi(CapsDataset): + def __init__( + self, + caps_directory: Path, + tsv_label: Path, + preprocessing_dict: Dict[str, Any], + roi_index: Optional[int] = None, + train_transformations: Optional[Callable] = None, + label_presence: bool = True, + label: str = None, + label_code: Dict[str, int] = None, + all_transformations: Optional[Callable] = None, + multi_cohort: bool = False, + ): + """ + Args: + caps_directory: Directory of all the images. + data_file: Path to the tsv file or DataFrame containing the subject/session list. + preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data. + roi_index: If a value is given the same region will be extracted for each image. + else the dataset will load all the regions possible for one image. + train_transformations: Optional transform to be applied only on training mode. + label_presence: If True the diagnosis will be extracted from the given DataFrame. + label: Name of the column in data_df containing the label. + label_code: label code that links the output node number to label value. + all_transformations: Optional transform to be applied during training and evaluation. + multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths. + + """ + super().__init__( + caps_directory, + tsv_label, + preprocessing_dict, + augmentation_transformations=train_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + transformations=all_transformations, + multi_cohort=multi_cohort, + ) + self.roi_index = roi_index + self.mode = "roi" + self.roi_list = self.preprocessing_dict["roi_list"] + self.uncropped_roi = self.preprocessing_dict["uncropped_roi"] + self.prepare_dl = self.preprocessing_dict["prepare_dl"] + self.mask_paths, self.mask_arrays = self._get_mask_paths_and_tensors( + caps_directory, multi_cohort, self.preprocessing_dict + ) + + @property + def elem_index(self): + return self.roi_index + + def __getitem__(self, idx): + participant, session, cohort, roi_idx, label, domain = self._get_meta_data(idx) + image_path = self._get_image_path(participant, session, cohort) + + if self.roi_list is None: + raise NotImplementedError( + "Default regions are not available anymore in ClinicaDL. " + "Please define appropriate masks and give a roi_list." + ) + + if self.prepare_dl: + mask_path = self.mask_paths[roi_idx] + roi_dir = image_path.parent.as_posix().replace( + "image_based", f"{self.mode}_based" + ) + roi_filename = extract_roi_path(image_path, mask_path, self.uncropped_roi) + roi_tensor = torch.load(Path(roi_dir) / roi_filename) + + else: + image = torch.load(image_path) + mask_array = self.mask_arrays[roi_idx] + roi_tensor = extract_roi_tensor(image, mask_array, self.uncropped_roi) + + if self.transformations: + roi_tensor = self.transformations(roi_tensor) + + if self.augmentation_transformations and not self.eval_mode: + roi_tensor = self.augmentation_transformations(roi_tensor) + + sample = { + "image": roi_tensor, + "label": label, + "participant_id": participant, + "session_id": session, + "roi_id": roi_idx, + } + + return sample + + def num_elem_per_image(self): + if self.elem_index is not None: + return 1 + if self.roi_list is None: + return 2 + else: + return len(self.roi_list) + + def _get_mask_paths_and_tensors( + self, + caps_directory: Path, + multi_cohort: bool, + preprocessing_dict: Dict[str, Any], + ) -> Tuple[List[str], List]: + """Loads the masks necessary to regions extraction""" + import nibabel as nib + + caps_dict = self.create_caps_dict(caps_directory, multi_cohort) + + if len(caps_dict) > 1: + caps_directory = caps_dict[next(iter(caps_dict))] + logger.warning( + f"The equality of masks is not assessed for multi-cohort training. " + f"The masks stored in {caps_directory} will be used." + ) + # Find template name + if preprocessing_dict["preprocessing"] == "custom": + template_name = preprocessing_dict["roi_custom_template"] + if template_name is None: + raise ValueError( + f"Please provide a name for the template when preprocessing is `custom`." + ) + elif preprocessing_dict["preprocessing"] in TEMPLATE_DICT: + template_name = TEMPLATE_DICT[preprocessing_dict["preprocessing"]] + else: + raise NotImplementedError( + f"Template of preprocessing {preprocessing_dict['preprocessing']} " + f"is not defined." + ) + + # Find mask pattern + if preprocessing_dict["preprocessing"] == "custom": + pattern = preprocessing_dict["roi_custom_mask_pattern"] + if pattern is None: + raise ValueError( + f"Please provide a pattern for the masks when preprocessing is `custom`." + ) + elif preprocessing_dict["preprocessing"] in PATTERN_DICT: + pattern = PATTERN_DICT[preprocessing_dict["preprocessing"]] + else: + raise NotImplementedError( + f"Pattern of mask for preprocessing {preprocessing_dict['preprocessing']} " + f"is not defined." + ) + + mask_location = caps_directory / "masks" / f"tpl-{template_name}" + + mask_paths, mask_arrays = list(), list() + for roi in self.roi_list: + logger.info(f"Find mask for roi {roi}.") + mask_path, desc = find_mask_path(mask_location, roi, pattern, True) + if mask_path is None: + raise FileNotFoundError(desc) + mask_nii = nib.load(mask_path) + mask_paths.append(Path(mask_path)) + mask_arrays.append(mask_nii.get_fdata()) + + return mask_paths, mask_arrays + + +class CapsDatasetSlice(CapsDataset): + def __init__( + self, + caps_directory: Path, + tsv_label: Path, + preprocessing_dict: Dict[str, Any], + slice_index: Optional[int] = None, + train_transformations: Optional[Callable] = None, + label_presence: bool = True, + label: str = None, + label_code: Dict[str, int] = None, + all_transformations: Optional[Callable] = None, + multi_cohort: bool = False, + ): + """ + Args: + caps_directory: Directory of all the images. + data_file: Path to the tsv file or DataFrame containing the subject/session list. + preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data. + slice_index: If a value is given the same slice will be extracted for each image. + else the dataset will load all the slices possible for one image. + train_transformations: Optional transform to be applied only on training mode. + label_presence: If True the diagnosis will be extracted from the given DataFrame. + label: Name of the column in data_df containing the label. + label_code: label code that links the output node number to label value. + all_transformations: Optional transform to be applied during training and evaluation. + multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths. + """ + super().__init__( + caps_directory, + tsv_label, + preprocessing_dict, + augmentation_transformations=train_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + transformations=all_transformations, + multi_cohort=multi_cohort, + ) + self.slice_index = slice_index + self.slice_direction = self.preprocessing_dict["slice_direction"] + self.slice_mode = self.preprocessing_dict["slice_mode"] + self.discarded_slices = compute_discarded_slices( + self.preprocessing_dict["discarded_slices"] + ) + self.num_slices = None + if "num_slices" in preprocessing_dict: + self.num_slices = self.preprocessing_dict["num_slices"] + + self.mode = "slice" + self.prepare_dl = self.preprocessing_dict["prepare_dl"] + + @property + def elem_index(self): + return self.slice_index + + def __getitem__(self, idx): + participant, session, cohort, slice_idx, label, domain = self._get_meta_data( + idx + ) + slice_idx = slice_idx + self.discarded_slices[0] + image_path = self._get_image_path(participant, session, cohort) + + if self.prepare_dl: + slice_dir = image_path.parent.as_posix().replace( + "image_based", f"{self.mode}_based" + ) + slice_filename = extract_slice_path( + image_path, self.slice_direction, self.slice_mode, slice_idx + ) + slice_tensor = torch.load(Path(slice_dir) / slice_filename) + + else: + image_path = self._get_image_path(participant, session, cohort) + image = torch.load(image_path) + slice_tensor = extract_slice_tensor( + image, self.slice_direction, self.slice_mode, slice_idx + ) + + if self.transformations: + slice_tensor = self.transformations(slice_tensor) + + if self.augmentation_transformations and not self.eval_mode: + slice_tensor = self.augmentation_transformations(slice_tensor) + + sample = { + "image": slice_tensor, + "label": label, + "participant_id": participant, + "session_id": session, + "slice_id": slice_idx, + } + + return sample + + def num_elem_per_image(self): + if self.elem_index is not None: + return 1 + + if self.num_slices is not None: + return self.num_slices + + image = self._get_full_image() + return ( + image.size(self.slice_direction + 1) + - self.discarded_slices[0] + - self.discarded_slices[1] + ) + + +def return_dataset( + input_dir: Path, + data_df: pd.DataFrame, + preprocessing_dict: Dict[str, Any], + all_transformations: Optional[Callable], + label: str = None, + label_code: Dict[str, int] = None, + train_transformations: Optional[Callable] = None, + cnn_index: int = None, + label_presence: bool = True, + multi_cohort: bool = False, +) -> CapsDataset: + """ + Return appropriate Dataset according to given options. + Args: + input_dir: path to a directory containing a CAPS structure. + data_df: List subjects, sessions and diagnoses. + preprocessing_dict: preprocessing dict contained in the JSON file of prepare_data. + train_transformations: Optional transform to be applied during training only. + all_transformations: Optional transform to be applied during training and evaluation. + label: Name of the column in data_df containing the label. + label_code: label code that links the output node number to label value. + cnn_index: Index of the CNN in a multi-CNN paradigm (optional). + label_presence: If True the diagnosis will be extracted from the given DataFrame. + multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths. + + Returns: + the corresponding dataset. + """ + if cnn_index is not None and preprocessing_dict["mode"] == "image": + raise NotImplementedError( + f"Multi-CNN is not implemented for {preprocessing_dict['mode']} mode." + ) + + if preprocessing_dict["mode"] == "image": + return CapsDatasetImage( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "patch": + return CapsDatasetPatch( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + patch_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "roi": + return CapsDatasetRoi( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + roi_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "slice": + return CapsDatasetSlice( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + slice_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + else: + raise NotImplementedError( + f"Mode {preprocessing_dict['mode']} is not implemented." + ) + + +################################## +# Transformations +################################## + + +class RandomNoising(object): + """Applies a random zoom to a tensor""" + + def __init__(self, sigma=0.1): + self.sigma = sigma + + def __call__(self, image): + import random + + sigma = random.uniform(0, self.sigma) + dist = torch.distributions.normal.Normal(0, sigma) + return image + dist.sample(image.shape) + + +class RandomSmoothing(object): + """Applies a random zoom to a tensor""" + + def __init__(self, sigma=1): + self.sigma = sigma + + def __call__(self, image): + import random + + from scipy.ndimage import gaussian_filter + + sigma = random.uniform(0, self.sigma) + image = gaussian_filter(image, sigma) # smoothing of data + image = torch.from_numpy(image).float() + return image + + +class RandomCropPad(object): + def __init__(self, length): + self.length = length + + def __call__(self, image): + dimensions = len(image.shape) - 1 + crop = np.random.randint(-self.length, self.length, dimensions) + if dimensions == 2: + output = torch.nn.functional.pad( + image, (-crop[0], crop[0], -crop[1], crop[1]) + ) + elif dimensions == 3: + output = torch.nn.functional.pad( + image, (-crop[0], crop[0], -crop[1], crop[1], -crop[2], crop[2]) + ) + else: + raise ValueError( + f"RandomCropPad is only available for 2D or 3D data. Image is {dimensions}D" + ) + return output + + +class GaussianSmoothing(object): + def __init__(self, sigma): + self.sigma = sigma + + def __call__(self, sample): + from scipy.ndimage.filters import gaussian_filter + + image = sample["image"] + np.nan_to_num(image, copy=False) + smoothed_image = gaussian_filter(image, sigma=self.sigma) + sample["image"] = smoothed_image + + return sample + + +class RandomMotion(object): + """Applies a Random Motion""" + + def __init__(self, translation, rotation, num_transforms): + self.rotation = rotation + self.translation = translation + self.num_transforms = num_transforms + + def __call__(self, image): + motion = tio.RandomMotion( + degrees=self.rotation, + translation=self.translation, + num_transforms=self.num_transforms, + ) + image = motion(image) + + return image + + +class RandomGhosting(object): + """Applies a Random Ghosting""" + + def __init__(self, num_ghosts): + self.num_ghosts = num_ghosts + + def __call__(self, image): + ghost = tio.RandomGhosting(num_ghosts=self.num_ghosts) + image = ghost(image) + + return image + + +class RandomSpike(object): + """Applies a Random Spike""" + + def __init__(self, num_spikes, intensity): + self.num_spikes = num_spikes + self.intensity = intensity + + def __call__(self, image): + spike = tio.RandomSpike( + num_spikes=self.num_spikes, + intensity=self.intensity, + ) + image = spike(image) + + return image + + +class RandomBiasField(object): + """Applies a Random Bias Field""" + + def __init__(self, coefficients): + self.coefficients = coefficients + + def __call__(self, image): + bias_field = tio.RandomBiasField(coefficients=self.coefficients) + image = bias_field(image) + + return image + + +class RandomBlur(object): + """Applies a Random Blur""" + + def __init__(self, std): + self.std = std + + def __call__(self, image): + blur = tio.RandomBlur(std=self.std) + image = blur(image) + + return image + + +class RandomSwap(object): + """Applies a Random Swap""" + + def __init__(self, patch_size, num_iterations): + self.patch_size = patch_size + self.num_iterations = num_iterations + + def __call__(self, image): + swap = tio.RandomSwap( + patch_size=self.patch_size, num_iterations=self.num_iterations + ) + image = swap(image) + + return image + + +class ToTensor(object): + """Convert image type to Tensor and diagnosis to diagnosis code""" + + def __call__(self, image): + np.nan_to_num(image, copy=False) + image = image.astype(float) + + return torch.from_numpy(image[np.newaxis, :]).float() + + +class MinMaxNormalization(object): + """Normalizes a tensor between 0 and 1""" + + def __call__(self, image): + return (image - image.min()) / (image.max() - image.min()) + + +class NanRemoval(object): + def __init__(self): + self.nan_detected = False # Avoid warning each time new data is seen + + def __call__(self, image): + if torch.isnan(image).any().item(): + if not self.nan_detected: + logger.warning( + "NaN values were found in your images and will be removed." + ) + self.nan_detected = True + return torch.nan_to_num(image) + else: + return image + + +class SizeReduction(object): + """Reshape the input tensor to be of size [80, 96, 80]""" + + def __init__(self, size_reduction_factor=2) -> None: + self.size_reduction_factor = size_reduction_factor + + def __call__(self, image): + if self.size_reduction_factor == 2: + return image[:, 4:164:2, 8:200:2, 8:168:2] + elif self.size_reduction_factor == 3: + return image[:, 0:168:3, 8:200:3, 4:172:3] + elif self.size_reduction_factor == 4: + return image[:, 4:164:4, 8:200:4, 8:168:4] + elif self.size_reduction_factor == 5: + return image[:, 4:164:5, 0:200:5, 8:168:5] + else: + raise ClinicaDLConfigurationError( + "size_reduction_factor must be 2, 3, 4 or 5." + ) + + +def get_transforms( + normalize: bool = True, + data_augmentation: List[str] = None, + size_reduction: bool = False, + size_reduction_factor: int = 2, +) -> Tuple[transforms.Compose, transforms.Compose]: + """ + Outputs the transformations that will be applied to the dataset + + Args: + normalize: if True will perform MinMaxNormalization. + data_augmentation: list of data augmentation performed on the training set. + + Returns: + transforms to apply in train and evaluation mode / transforms to apply in evaluation mode only. + """ + augmentation_dict = { + "Noise": RandomNoising(sigma=0.1), + "Erasing": transforms.RandomErasing(), + "CropPad": RandomCropPad(10), + "Smoothing": RandomSmoothing(), + "Motion": RandomMotion((2, 4), (2, 4), 2), + "Ghosting": RandomGhosting((4, 10)), + "Spike": RandomSpike(1, (1, 3)), + "BiasField": RandomBiasField(0.5), + "RandomBlur": RandomBlur((0, 2)), + "RandomSwap": RandomSwap(15, 100), + "None": None, + } + + augmentation_list = [] + transformations_list = [] + + if data_augmentation: + augmentation_list.extend( + [augmentation_dict[augmentation] for augmentation in data_augmentation] + ) + + transformations_list.append(NanRemoval()) + if normalize: + transformations_list.append(MinMaxNormalization()) + if size_reduction: + transformations_list.append(SizeReduction(size_reduction_factor)) + + all_transformations = transforms.Compose(transformations_list) + train_transformations = transforms.Compose(augmentation_list) + + return train_transformations, all_transformations + + +################################ +# TSV files loaders +################################ +def load_data_test(test_path: Path, diagnoses_list, baseline=True, multi_cohort=False): + """ + Load data not managed by split_manager. + + Args: + test_path (str): path to the test TSV files / split directory / TSV file for multi-cohort + diagnoses_list (List[str]): list of the diagnoses wanted in case of split_dir or multi-cohort + baseline (bool): If True baseline sessions only used (split_dir handling only). + multi_cohort (bool): If True considers multi-cohort setting. + """ + # TODO: computes baseline sessions on-the-fly to manager TSV file case + + if multi_cohort: + if not test_path.suffix == ".tsv": + raise ClinicaDLArgumentError( + "If multi_cohort is given, the TSV_DIRECTORY argument should be a path to a TSV file." + ) + else: + tsv_df = pd.read_csv(test_path, sep="\t") + check_multi_cohort_tsv(tsv_df, "labels") + test_df = pd.DataFrame() + found_diagnoses = set() + for idx in range(len(tsv_df)): + cohort_name = tsv_df.loc[idx, "cohort"] + cohort_path = Path(tsv_df.loc[idx, "path"]) + cohort_diagnoses = ( + tsv_df.loc[idx, "diagnoses"].replace(" ", "").split(",") + ) + if bool(set(cohort_diagnoses) & set(diagnoses_list)): + target_diagnoses = list(set(cohort_diagnoses) & set(diagnoses_list)) + cohort_test_df = load_data_test_single( + cohort_path, target_diagnoses, baseline=baseline + ) + cohort_test_df["cohort"] = cohort_name + test_df = pd.concat([test_df, cohort_test_df]) + found_diagnoses = found_diagnoses | ( + set(cohort_diagnoses) & set(diagnoses_list) + ) + + if found_diagnoses != set(diagnoses_list): + raise ValueError( + f"The diagnoses found in the multi cohort dataset {found_diagnoses} " + f"do not correspond to the diagnoses wanted {set(diagnoses_list)}." + ) + test_df.reset_index(inplace=True, drop=True) + else: + if test_path.suffix == ".tsv": + tsv_df = pd.read_csv(test_path, sep="\t") + multi_col = {"cohort", "path"} + if multi_col.issubset(tsv_df.columns.values): + raise ClinicaDLConfigurationError( + "To use multi-cohort framework, please add 'multi_cohort=true' in your configuration file or '--multi_cohort' flag to the command line." + ) + test_df = load_data_test_single(test_path, diagnoses_list, baseline=baseline) + test_df["cohort"] = "single" + + return test_df + + +def load_data_test_single(test_path: Path, diagnoses_list, baseline=True): + if test_path.suffix == ".tsv": + test_df = pd.read_csv(test_path, sep="\t") + if "diagnosis" not in test_df.columns.values: + raise ClinicaDLTSVError( + f"'diagnosis' column must be present in TSV file {test_path}." + ) + test_df = test_df[test_df.diagnosis.isin(diagnoses_list)] + if len(test_df) == 0: + raise ClinicaDLTSVError( + f"Diagnoses wanted {diagnoses_list} were not found in TSV file {test_path}." + ) + return test_df + + test_df = pd.DataFrame() + + if baseline: + if not (test_path.parent / "train_baseline.tsv").is_file(): + if not (test_path.parent / "labels_baseline.tsv").is_file(): + raise ClinicaDLTSVError( + f"There is no train_baseline.tsv nor labels_baseline.tsv in your folder {test_path.parents[0]} " + ) + else: + test_path = test_path.parent / "labels_baseline.tsv" + else: + test_path = test_path.parent / "train_baseline.tsv" + else: + if not (test_path.parent / "train.tsv").is_file(): + if not (test_path.parent / "labels.tsv").is_file(): + raise ClinicaDLTSVError( + f"There is no train.tsv or labels.tsv in your folder {test_path.parent} " + ) + else: + test_path = test_path.parent / "labels.tsv" + else: + test_path = test_path.parent / "train.tsv" + + test_df = pd.read_csv(test_path, sep="\t") + test_df = test_df[test_df.diagnosis.isin(diagnoses_list)] + test_df.reset_index(inplace=True, drop=True) + + return test_df + + +def check_multi_cohort_tsv(tsv_df: pd.DataFrame, purpose: str) -> None: + """ + Checks that a multi-cohort TSV file is valid. + + Args: + tsv_df (pd.DataFrame): DataFrame of multi-cohort definition. + purpose (str): what the TSV file describes (CAPS or TSV). + Raises: + ValueError: if the TSV file is badly formatted. + """ + mandatory_col = ("cohort", "diagnoses", "path") + if purpose.upper() == "CAPS": + mandatory_col = ("cohort", "path") + if not set(mandatory_col).issubset(tsv_df.columns.values): + raise ClinicaDLTSVError( + f"Columns of the TSV file used for {purpose} location must include {mandatory_col}" + ) diff --git a/clinicadl/utils/caps_dataset/caps_dataset_refactoring/concat_dataset.py b/clinicadl/utils/caps_dataset/caps_dataset_refactoring/concat_dataset.py new file mode 100644 index 000000000..d5895bcf3 --- /dev/null +++ b/clinicadl/utils/caps_dataset/caps_dataset_refactoring/concat_dataset.py @@ -0,0 +1,375 @@ +from pathlib import Path +from typing import Any, Iterable, List, Optional, Union + +from clinicadl.utils.caps_dataset.caps_dataset_refactoring import ( + caps_dataset, # type: ignore +) +import pandas as pd +from torch.utils.data import ConcatDataset, DataLoader, Dataset, Sampler, StackDataset + + +class CapsConcatDataset(ConcatDataset): + """Concatenation of CapsDataset""" + + def __init__(self, datasets: Iterable[Dataset]) -> None: + super().__init__(datasets=datasets) + + mode = [d.mode for d in self.datasets] + if all( + i == mode[0] for i in mode + ): # check that all the CaspDataset have the same mode + self.mode = mode[0] + else: + raise AttributeError( + "All the CapsDataset must have the same mode: 'image','patch','roi','slice', etc." + ) + + +class CapsPairedDataset(StackDataset): + def __init__(self, datasets: Union[tuple, dict]) -> None: + self.paired = True + self.datasets = list(datasets) # CapsDatasets list + self.n_datasets = len(self.datasets) # number of CapsDatasets + self.mode = [d.mode for d in self.datasets] # modes of each CapsDatasets + super().__init__(*datasets) # * unpack the tuples + + +class CapsUnpairedDataset(Dataset): + r"""Dataset as a stacking of multiple datasets that can have different lengths. + + This class is useful to assemble different parts of complex input data, given as datasets. + + Args: + datasets (list[Dataset]): Iterable of Datasets. + """ + + def __init__(self, datasets: Union[tuple, list]) -> None: + self.datasets = list(datasets) # list of CapsDatasets + self.n_datasets = len(self.datasets) # number of CapsDatasets + self.mode = [d.mode for d in self.datasets] # modes of each CapsDatasets + self.len_dataset = [ + d.__len__() for d in self.datasets + ] # length of each datasets + self.paired = False + + def __getitem__(self, indexes: list): + # checks that the number of datasets is equal to the number of index + if len(indexes) == self.n_datasets: + # checks that the indexes < to the length of each datasets + if all(indexes[i] < self.len_dataset[i] for i in range(len(indexes))): + return tuple( + [ + self.datasets[i].__getitem__(indexes[i]) + for i in range(len(indexes)) + ] + ) + else: + raise ValueError( + "The indexes must be inferior than the length of each dataset" + ) + else: + raise ValueError(" The number of indexes must match the number of datasets") + + +def display_controlfile(control_file: pd.DataFrame) -> None: + import matplotlib.pyplot # must be imported to display overlay + + def codes_labels(labels_columns: pd.Series): + categorical_label = pd.CategoricalIndex(labels_columns) + codes = categorical_label.codes + return codes + + def make_pretty(styler, gmap=codes_labels(control_file["label_tsv_path"])): + styler.set_caption("Control File") + styler.background_gradient(axis=0, cmap="YlGnBu", subset="assembly_id") + styler.background_gradient(subset="label_tsv_path", gmap=gmap) + return styler + + return control_file.style.pipe(make_pretty) + + +def hyper_dataset(control_file_path: Path, paired: bool = False) -> Dataset: + mandatory_col = [ + "caps_directory", + "label_tsv_path", + "preprocessing_json_path", + "assembly_id", + ] + + control_file = pd.read_csv(control_file_path, sep="\t") + + # check that the mandatory columns exist + check_mandatory = True + for col in mandatory_col: + check_mandatory = check_mandatory & any(control_file.columns.str.contains(col)) + if not check_mandatory: + raise ValueError("Mandatory colmuns not recognized") + + # count the number of dataset to stack + control_file.set_index("assembly_id", inplace=True) + assembly_id = control_file.index.unique().sort_values() # sort it before + number_multicohort = control_file.index.value_counts() + number_stack = len(assembly_id) + + if not paired: # unpaired dataset + DatasetsToStackList = [] + for i in range(number_stack): + if number_multicohort.loc[assembly_id[i]] > 1: + DatasetsToConcatList = [] + + sub_control_file_i = ( + control_file.copy().loc[assembly_id[i]].reset_index(drop=True) + ) + + for j in range(len(sub_control_file_i)): + caps_arguments_i = sub_control_file_i.loc[j] + CapsDataset_i = caps_dataset.CapsDatasetImage( + caps_directory=Path(caps_arguments_i["caps_directory"]), + tsv_label=Path(caps_arguments_i["label_tsv_path"]), + preprocessing_dict=Path( + caps_arguments_i["preprocessing_json_path"] + ), + train_transformations=None, + label_presence=False, + label=None, + label_code=None, + all_transformations=None, + ) + + DatasetsToConcatList.append(CapsDataset_i) + + CapsConcat_i = CapsConcatDataset(DatasetsToConcatList) + else: + sub_control_file_i = control_file.loc[assembly_id[i]] + CapsConcat_i = caps_dataset.CapsDatasetImage( + caps_directory=Path(sub_control_file_i["caps_directory"]), + tsv_label=Path(sub_control_file_i["label_tsv_path"]), + preprocessing_dict=Path( + sub_control_file_i["preprocessing_json_path"] + ), + train_transformations=None, + label_presence=False, + label=None, + label_code=None, + all_transformations=None, + ) + + DatasetsToStackList.append(CapsConcat_i) + + return CapsUnpairedDataset(DatasetsToStackList) + else: # paired dataset + ########################### + # check # + ########################### + + # check 1, same number of cohort in each columns + check1 = all(i == number_multicohort[0] for i in number_multicohort) + if not check1: + raise ValueError( + "For the paired dataset, you should have the same number of multicohort number" + ) + + # check 2, each labels_tsv same number than multi modalities + controlcheck2 = control_file.reset_index().set_index(["label_tsv_path"]) + controlcheck2 = controlcheck2.index.value_counts() + check2 = all(i == controlcheck2[0] for i in controlcheck2) + + if not check2: + raise ValueError( + "Each label file should appears the same number of time for each assembly ID" + ) + + # check 3, equal number of combination assembly_id, labels + controlcheck3 = control_file.reset_index().set_index(["label_tsv_path"]) + index = controlcheck3.index.unique() + check3 = all( + ( + controlcheck3.loc[index[0], "assembly_id"].values + == controlcheck3.loc[index[i], "assembly_id"] + ).all() + for i in range(len(index)) + ) + + if not check3: + raise ValueError( + "Each combination of assembly ID, label_tsv_path should be equal" + ) + + ########################### + # compute paired # + ########################### + + DatasetsToStackList = [] + for i in range(number_stack): + if number_multicohort.loc[assembly_id[i]] > 1: + DatasetsToConcatList = [] + + sub_control_file_i = ( + control_file.copy() + .loc[assembly_id[i]] + .reset_index(drop=True) + .set_index("label_tsv_path") + ) + + for j in range(len(index)): + # we use the index by unicity, so they will be called in the same order at for each assembly ID which means + # that we ensure the same construction for the concatenation + caps_arguments_i = sub_control_file_i.loc[index[j]] + CapsDataset_i = caps_dataset.CapsDatasetImage( + caps_directory=Path(caps_arguments_i["caps_directory"]), + tsv_label=Path(index[j]), + preprocessing_dict=Path( + caps_arguments_i["preprocessing_json_path"] + ), + train_transformations=None, + label_presence=False, + label=None, + label_code=None, + all_transformations=None, + ) + + DatasetsToConcatList.append(CapsDataset_i) + + CapsConcat_i = CapsConcatDataset(DatasetsToConcatList) + else: + sub_control_file_i = control_file.loc[assembly_id[i]] + CapsConcat_i = caps_dataset.CapsDatasetImage( + caps_directory=Path(sub_control_file_i["caps_directory"]), + tsv_label=Path(sub_control_file_i["label_tsv_path"]), + preprocessing_dict=Path( + sub_control_file_i["preprocessing_json_path"] + ), + train_transformations=None, + label_presence=False, + label=None, + label_code=None, + all_transformations=None, + ) + + DatasetsToStackList.append(CapsConcat_i) + return CapsPairedDataset(DatasetsToStackList) + + +# Dataloader + +# import dataclasses + +# @dataclasses +# class CapsDataLoaderConfig: + + +class DataloaderCaps(DataLoader): + def __init__( + self, + dataset: Dataset, + batch_size: List[int] = 1, + shuffle: Optional[bool] = None, + sampler: Union[Sampler, Iterable, None] = None, + batch_sampler: Union[Sampler[List], Iterable[List], None] = None, + num_workers: int = 0, + collate_fn: Optional[str] = None, + pin_memory: bool = False, + drop_last: bool = False, + timeout: float = 0, + worker_init_fn: Optional[str] = None, + multiprocessing_context=None, + generator=None, + *, + prefetch_factor: Optional[int] = None, + persistent_workers: bool = False, + pin_memory_device: str = "", + ): + self.batch_size = batch_size + self.shuffle = shuffle + self.sampler = sampler + self.batch_sampler = batch_sampler + self.collate_fn = collate_fn + self.num_workers = num_workers + self.pin_memory = pin_memory + self.drop_last = drop_last + self.timeout = timeout + self.worker_init_fn = worker_init_fn + self.multiprocessing_context = multiprocessing_context + self.generator = generator + self.prefetch_factor = prefetch_factor + self.persistent_workers = persistent_workers + self.pin_memory_device = pin_memory_device + + if hasattr(dataset, "paired"): + self.paired = dataset.paired + else: + self.paired = True + + if self.paired: + dataloaders = DataLoader( + dataset=dataset, + batch_size=self.batch_size, + shuffle=self.shuffle, + sampler=self.sampler, + batch_sampler=self.batch_sampler, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + drop_last=self.drop_last, + timeout=self.timeout, + multiprocessing_context=self.multiprocessing_context, + generator=self.generator, + prefetch_factor=self.prefetch_factor, + persistent_workers=self.persistent_workers, + pin_memory_device=self.pin_memory_device, + ) + + else: + dataloaders = [] + for i in range(dataset.n_datasets): + if self.sampler is not None: + sampler_i = self.sampler[i] + else: + sampler_i = None + + dataloader_i = DataLoader( + dataset=dataset.datasets[i], + batch_size=self.batch_size, + shuffle=self.shuffle, + sampler=sampler_i, + batch_sampler=self.batch_sampler, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + drop_last=self.drop_last, + timeout=self.timeout, + multiprocessing_context=self.multiprocessing_context, + generator=self.generator, + prefetch_factor=self.prefetch_factor, + persistent_workers=self.persistent_workers, + pin_memory_device=self.pin_memory_device, + ) + dataloaders.append(dataloader_i) + + self.dataloaders = dataloaders + + def __iter__(self) -> List[str]: + ConcatBaseLoaderIter = [] + if hasattr(self, "dataloaders"): + if self.paired: + return self.dataloaders.__iter__() + else: + for dataloder in self.dataloaders: + print(dataloder.__iter__()) + ConcatBaseLoaderIter.append(dataloder.__iter__()) + return MultiLoaderIterable(ConcatBaseLoaderIter).__iter__() + else: + raise ValueError("No Dataloader created") + + +class MultiLoaderIterable: + def __init__(self, list_iterable_loader): + self.listIterableLoader = list_iterable_loader + self._sampler_iter = None + + def __iter__(self): + return self + + def __next__(self) -> Any: + data = [] + for iterator in self.listIterableLoader: + data.append(iterator.__next__()) + return data diff --git a/clinicadl/utils/caps_dataset/data.py b/clinicadl/utils/caps_dataset/data.py old mode 100644 new mode 100755 index 9e50533ea..be87ebfdd --- a/clinicadl/utils/caps_dataset/data.py +++ b/clinicadl/utils/caps_dataset/data.py @@ -3,14 +3,16 @@ import abc from logging import getLogger from os import path +from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import pandas as pd import torch import torchvision.transforms as transforms -from clinica.utils.exceptions import ClinicaCAPSError +from pythae.data.datasets import DatasetOutput from torch.utils.data import Dataset +from torchio.transforms import Resize from clinicadl.extract.extract_utils import ( PATTERN_DICT, @@ -27,6 +29,7 @@ ) from clinicadl.utils.exceptions import ( ClinicaDLArgumentError, + ClinicaDLCAPSError, ClinicaDLConfigurationError, ClinicaDLTSVError, ) @@ -82,7 +85,10 @@ def __init__( ) self.elem_per_image = self.num_elem_per_image() - self.size = self[0]["image"].size() + if "data" in self[0].keys(): + self.size = self[0]["data"].size() + else: + self.size = self[0].data.size() @property @abc.abstractmethod @@ -112,9 +118,8 @@ def __len__(self) -> int: return len(self.df) * self.elem_per_image @staticmethod - def create_caps_dict(caps_directory: str, multi_cohort: bool) -> Dict[str, str]: - - from clinica.utils.inputs import check_caps_folder + def create_caps_dict(caps_directory: Path, multi_cohort: bool) -> Dict[str, Path]: + from clinicadl.utils.clinica_utils import check_caps_folder if multi_cohort: if not caps_directory.endswith(".tsv"): @@ -147,15 +152,18 @@ def _get_image_path(self, participant: str, session: str, cohort: str) -> str: Returns: image_path: path to the tensor containing the whole image. """ - from clinica.utils.inputs import clinica_file_reader + from clinicadl.utils.clinica_utils import clinicadl_file_reader # Try to find .nii.gz file try: file_type = self.preprocessing_dict["file_type"] - image_path_list, _ = clinica_file_reader( + results = clinicadl_file_reader( [participant], [session], self.caps_dict[cohort], file_type ) - image_filename = path.basename(image_path_list[0]).replace(".nii.gz", ".pt") + logger.debug(f"clinicadl_file_reader output: {results}") + filepath = Path(results[0][0]) + image_filename = filepath.name.replace(".nii.gz", ".pt") + folder, _ = compute_folder_and_file_type(self.preprocessing_dict) image_dir = path.join( self.caps_dict[cohort], @@ -168,10 +176,10 @@ def _get_image_path(self, participant: str, session: str, cohort: str) -> str: ) image_path = path.join(image_dir, image_filename) # Try to find .pt file - except ClinicaCAPSError: + except ClinicaDLCAPSError: file_type = self.preprocessing_dict["file_type"] file_type["pattern"] = file_type["pattern"].replace(".nii.gz", ".pt") - image_path_list, _ = clinica_file_reader( + results = clinicadl_file_reader( [participant], [session], self.caps_dict[cohort], file_type ) image_path = image_path_list[0] @@ -218,7 +226,8 @@ def _get_full_image(self) -> torch.Tensor: image tensor of the full image first image. """ import nibabel as nib - from clinica.utils.inputs import clinica_file_reader + + from clinicadl.utils.clinica_utils import clinicadl_file_reader participant_id = self.df.loc[0, "participant_id"] session_id = self.df.loc[0, "session_id"] @@ -229,7 +238,7 @@ def _get_full_image(self) -> torch.Tensor: image = torch.load(image_path) except IndexError: file_type = self.preprocessing_dict["file_type"] - image_path_list, _ = clinica_file_reader( + results = clinicadl_file_reader( [participant_id], [session_id], self.caps_dict[cohort], file_type ) image_nii = nib.load(image_path_list[0]) @@ -332,7 +341,7 @@ def __getitem__(self, idx): image = self.augmentation_transformations(image) sample = { - "image": image, + "data": image, "label": label, "participant_id": participant, "session_id": session, @@ -346,6 +355,35 @@ def num_elem_per_image(self): return 1 +class PythaeCAPS(CapsDatasetImage): + def __init__( + self, + caps_directory, + data_file, + preprocessing_dict, + train_transformations, + all_transformations, + ): + super().__init__( + caps_directory, + data_file, + preprocessing_dict, + train_transformations=train_transformations, + label_presence=False, + all_transformations=all_transformations, + ) + + def __getitem__(self, index): + X = super().__getitem__(index) + return DatasetOutput( + data=X["data"], + participant_id=X["participant_id"], + session_id=X["session_id"], + image_id=X["image_id"], + image_path=X["image_path"], + ) + + class CapsDatasetPatch(CapsDataset): def __init__( self, @@ -422,7 +460,7 @@ def __getitem__(self, idx): patch_tensor = self.augmentation_transformations(patch_tensor) sample = { - "image": patch_tensor, + "data": patch_tensor, "label": label, "participant_id": participant, "session_id": session, @@ -533,7 +571,7 @@ def __getitem__(self, idx): roi_tensor = self.augmentation_transformations(roi_tensor) sample = { - "image": roi_tensor, + "data": roi_tensor, "label": label, "participant_id": participant, "session_id": session, @@ -697,7 +735,7 @@ def __getitem__(self, idx): slice_tensor = self.augmentation_transformations(slice_tensor) sample = { - "image": slice_tensor, + "data": slice_tensor, "label": label, "participant_id": participant, "session_id": session, @@ -732,6 +770,7 @@ def return_dataset( cnn_index: int = None, label_presence: bool = True, multi_cohort: bool = False, + for_pythae: bool = False, ) -> CapsDataset: """ Return appropriate Dataset according to given options. @@ -756,61 +795,71 @@ def return_dataset( f"Multi-CNN is not implemented for {preprocessing_dict['mode']} mode." ) - if preprocessing_dict["mode"] == "image": - return CapsDatasetImage( - input_dir, - data_df, - preprocessing_dict, - train_transformations=train_transformations, - all_transformations=all_transformations, - label_presence=label_presence, - label=label, - label_code=label_code, - multi_cohort=multi_cohort, - ) - elif preprocessing_dict["mode"] == "patch": - return CapsDatasetPatch( - input_dir, - data_df, - preprocessing_dict, - train_transformations=train_transformations, - all_transformations=all_transformations, - patch_index=cnn_index, - label_presence=label_presence, - label=label, - label_code=label_code, - multi_cohort=multi_cohort, - ) - elif preprocessing_dict["mode"] == "roi": - return CapsDatasetRoi( - input_dir, - data_df, - preprocessing_dict, - train_transformations=train_transformations, - all_transformations=all_transformations, - roi_index=cnn_index, - label_presence=label_presence, - label=label, - label_code=label_code, - multi_cohort=multi_cohort, - ) - elif preprocessing_dict["mode"] == "slice": - return CapsDatasetSlice( + if for_pythae: + return PythaeCAPS( input_dir, data_df, preprocessing_dict, train_transformations=train_transformations, all_transformations=all_transformations, - slice_index=cnn_index, - label_presence=label_presence, - label=label, - label_code=label_code, - multi_cohort=multi_cohort, ) + else: - raise NotImplementedError( - f"Mode {preprocessing_dict['mode']} is not implemented." - ) + if preprocessing_dict["mode"] == "image": + return CapsDatasetImage( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "patch": + return CapsDatasetPatch( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + patch_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "roi": + return CapsDatasetRoi( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + roi_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + elif preprocessing_dict["mode"] == "slice": + return CapsDatasetSlice( + input_dir, + data_df, + preprocessing_dict, + train_transformations=train_transformations, + all_transformations=all_transformations, + slice_index=cnn_index, + label_presence=label_presence, + label=label, + label_code=label_code, + multi_cohort=multi_cohort, + ) + else: + raise NotImplementedError( + f"Mode {preprocessing_dict['mode']} is not implemented." + ) ################################## @@ -878,10 +927,10 @@ def __init__(self, sigma): def __call__(self, sample): from scipy.ndimage.filters import gaussian_filter - image = sample["image"] + image = sample["data"] np.nan_to_num(image, copy=False) smoothed_image = gaussian_filter(image, sigma=self.sigma) - sample["image"] = smoothed_image + sample["data"] = smoothed_image return sample @@ -919,8 +968,32 @@ def __call__(self, image): return image +class SizeReduction(object): + """Reshape the input tensor to be of size [80, 96, 80]""" + + def __init__(self, size_reduction_factor=2) -> None: + self.size_reduction_factor = size_reduction_factor + + def __call__(self, image): + if self.size_reduction_factor == 2: + return image[:, 4:164:2, 8:200:2, 8:168:2] + elif self.size_reduction_factor == 3: + return image[:, 0:168:3, 8:200:3, 4:172:3] + elif self.size_reduction_factor == 4: + return image[:, 4:164:4, 8:200:4, 8:168:4] + elif self.size_reduction_factor == 5: + return image[:, 4:164:5, 0:200:5, 8:168:5] + else: + raise ClinicaDLConfigurationError( + "size_reduction_factor must be 2, 3, 4 or 5." + ) + + def get_transforms( - normalize: bool = True, data_augmentation: List[str] = None + normalize: bool = True, + data_augmentation: List[str] = None, + size_reduction: bool = False, + size_reduction_factor: int = 2, ) -> Tuple[transforms.Compose, transforms.Compose]: """ Outputs the transformations that will be applied to the dataset @@ -939,17 +1012,21 @@ def get_transforms( "Smoothing": RandomSmoothing(), "None": None, } + + augmentation_list = [] + transformations_list = [] + if data_augmentation: - augmentation_list = [ - augmentation_dict[augmentation] for augmentation in data_augmentation - ] - else: - augmentation_list = [] + augmentation_list.extend( + [augmentation_dict[augmentation] for augmentation in data_augmentation] + ) + transformations_list.append(NanRemoval()) if normalize: - transformations_list = [NanRemoval(), MinMaxNormalization()] - else: - transformations_list = [NanRemoval()] + transformations_list.append(MinMaxNormalization()) + if size_reduction: + # transformations_list.append(SizeReduction(size_reduction_factor)) + transformations_list.append(Resize((256, 256, 256))) all_transformations = transforms.Compose(transformations_list) train_transformations = transforms.Compose(augmentation_list) @@ -960,10 +1037,9 @@ def get_transforms( ################################ # TSV files loaders ################################ -def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False): +def load_data_test(test_path: Path, diagnoses_list, baseline=True, multi_cohort=False): """ Load data not managed by split_manager. - Args: test_path (str): path to the test TSV files / split directory / TSV file for multi-cohort diagnoses_list (List[str]): list of the diagnoses wanted in case of split_dir or multi-cohort @@ -973,7 +1049,7 @@ def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False) # TODO: computes baseline sessions on-the-fly to manager TSV file case if multi_cohort: - if not test_path.endswith(".tsv"): + if not test_path.suffix == ".tsv": raise ClinicaDLArgumentError( "If multi_cohort is given, the TSV_DIRECTORY argument should be a path to a TSV file." ) @@ -1006,7 +1082,7 @@ def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False) ) test_df.reset_index(inplace=True, drop=True) else: - if test_path.endswith(".tsv"): + if test_path.suffix == ".tsv": tsv_df = pd.read_csv(test_path, sep="\t") multi_col = {"cohort", "path"} if multi_col.issubset(tsv_df.columns.values): @@ -1019,9 +1095,8 @@ def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False) return test_df -def load_data_test_single(test_path, diagnoses_list, baseline=True): - - if test_path.endswith(".tsv"): +def load_data_test_single(test_path: Path, diagnoses_list, baseline=True): + if test_path.suffix == ".tsv": test_df = pd.read_csv(test_path, sep="\t") if "diagnosis" not in test_df.columns.values: raise ClinicaDLTSVError( @@ -1036,15 +1111,28 @@ def load_data_test_single(test_path, diagnoses_list, baseline=True): test_df = pd.DataFrame() - for diagnosis in diagnoses_list: - - if baseline: - test_diagnosis_path = path.join(test_path, diagnosis + "_baseline.tsv") + if baseline: + if not (test_path.parent / "train_baseline.tsv").is_file(): + if not (test_path.parent / "labels_baseline.tsv").is_file(): + raise ClinicaDLTSVError( + f"There is no train_baseline.tsv nor labels_baseline.tsv in your folder {test_path.parents[0]} " + ) + else: + test_path = test_path.parent / "labels_baseline.tsv" else: - test_diagnosis_path = path.join(test_path, diagnosis + ".tsv") + test_path = test_path.parent / "train_baseline.tsv" + else: + if not (test_path.parent / "train.tsv").is_file(): + if not (test_path.parent / "labels.tsv").is_file(): + raise ClinicaDLTSVError( + f"There is no train.tsv or labels.tsv in your folder {test_path.parent} " + ) + else: + test_path = test_path.parent / "labels.tsv" + else: + test_path = test_path.parent / "train.tsv" - test_diagnosis_df = pd.read_csv(test_diagnosis_path, sep="\t") - test_df = pd.concat([test_df, test_diagnosis_df]) + test_df = pd.read_csv(test_path, sep="\t") test_df.reset_index(inplace=True, drop=True) @@ -1054,7 +1142,6 @@ def load_data_test_single(test_path, diagnoses_list, baseline=True): def check_multi_cohort_tsv(tsv_df, purpose): """ Checks that a multi-cohort TSV file is valid. - Args: tsv_df (pd.DataFrame): DataFrame of multi-cohort definition. purpose (str): what the TSV file describes (CAPS or TSV). diff --git a/clinicadl/utils/cli_param/__init__.py b/clinicadl/utils/cli_param/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/cli_param/argument.py b/clinicadl/utils/cli_param/argument.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/cli_param/option.py b/clinicadl/utils/cli_param/option.py old mode 100644 new mode 100755 index f25a997e3..a020cc5a3 --- a/clinicadl/utils/cli_param/option.py +++ b/clinicadl/utils/cli_param/option.py @@ -1,7 +1,8 @@ """Common CLI options used by Clinica pipelines.""" import click -from clinica.utils.pet import LIST_SUVR_REFERENCE_REGIONS + +LIST_SUVR_REFERENCE_REGIONS = ["pons", "cerebellumPons", "pons2", "cerebellumPons2"] # TSV TOOLS diagnoses = click.option( @@ -70,7 +71,7 @@ batch_size = click.option( "--batch_size", type=int, - default=2, + default=8, show_default=True, help="Batch size for data loading.", ) @@ -138,12 +139,27 @@ "`segm-whitematter_probability.nii.gz`" ), ) + +# PREDICT & INTERPRET # Data group overwrite = click.option( "--overwrite", "-o", default=False, is_flag=True, - help="Will overwrite data group if existing. Please give caps_directory and partcipants_tsv to" + help="Will overwrite data group if existing. Please give caps_directory and participants_tsv to" " define new data group.", ) +split = click.option( + "--split", + "-s", + type=int, + multiple=True, + help="Make inference on the list of given splits. By default, inference is done on all the splits.", +) +selection_metrics = click.option( + "--selection_metrics", + "-sm", + multiple=True, + help="""Make inference on the list of given metrics used for selection. By default, inference is done on all the metrics.""", +) diff --git a/clinicadl/utils/cli_param/option_group.py b/clinicadl/utils/cli_param/option_group.py old mode 100644 new mode 100755 diff --git a/clinicadl/train/train_option.py b/clinicadl/utils/cli_param/train_option.py old mode 100644 new mode 100755 similarity index 99% rename from clinicadl/train/train_option.py rename to clinicadl/utils/cli_param/train_option.py index f659e248f..1417ce6e7 --- a/clinicadl/train/train_option.py +++ b/clinicadl/utils/cli_param/train_option.py @@ -13,7 +13,7 @@ config_file = click.option( "--config_file", "-c", - type=click.File(), + type=click.Path(exists=True), help="Path to the TOML or JSON file containing the values of the options needed for training.", ) # Computational diff --git a/clinicadl/utils/clinica_utils.py b/clinicadl/utils/clinica_utils.py new file mode 100644 index 000000000..2b1b1ff62 --- /dev/null +++ b/clinicadl/utils/clinica_utils.py @@ -0,0 +1,1153 @@ +import hashlib +import os +import shutil +import ssl +import tempfile +from collections import namedtuple +from functools import partial +from glob import glob +from pathlib import Path, PurePath +from time import localtime, strftime, time +from typing import Callable, Dict, List, Optional, Tuple +from urllib.error import URLError +from urllib.request import Request, urlopen + +import pandas as pd + +from clinicadl.utils.exceptions import ( + ClinicaDLArgumentError, + ClinicaDLBIDSError, + ClinicaDLCAPSError, +) +from clinicadl.utils.logger import cprint + +RemoteFileStructure = namedtuple("RemoteFileStructure", ["filename", "url", "checksum"]) + + +def linear_nii(modality: str, uncropped_image: bool) -> dict: + + if modality not in ("T1w", "T2w", "flair"): + raise ClinicaDLArgumentError( + f"ClinicaDL is Unable to read this modality ({modality}) of images" + ) + elif modality == "T1w": + needed_pipeline = "t1-linear" + elif modality == "T2w": + needed_pipeline = "t2-linear" + elif modality == "flair": + needed_pipeline = "flair-linear" + + if uncropped_image: + desc_crop = "" + else: + desc_crop = "_desc-Crop" + + information = { + "pattern": f"*space-MNI152NLin2009cSym{desc_crop}_res-1x1x1_{modality}.nii.gz", + "description": f"{modality} Image registred in MNI152NLin2009cSym space using {needed_pipeline} pipeline " + + ( + "" + if uncropped_image + else "and cropped (matrix size 169×208×179, 1 mm isotropic voxels)" + ), + "needed_pipeline": needed_pipeline, + } + return information + + +def pet_linear_nii( + acq_label: str, suvr_reference_region: str, uncropped_image: bool +) -> dict: + + if uncropped_image: + description = "" + else: + description = "_desc-Crop" + + information = { + "pattern": str( + Path("pet_linear") + / f"*_trc-{acq_label}_pet_space-MNI152NLin2009cSym{description}_res-1x1x1_suvr-{suvr_reference_region}_pet.nii.gz" + ), + "description": "", + "needed_pipeline": "pet-linear", + } + return information + + +def container_from_filename(bids_or_caps_filename: str) -> str: + """Extract container from BIDS or CAPS file. + + Parameters + ---------- + bids_or_caps_filename : str + Full path to BIDS or CAPS filename. + + Returns + ------- + str : + Container path of the form "subjects//". + + Examples + -------- + >>> from clinica.utils.nipype import container_from_filename + >>> container_from_filename('/path/to/bids/sub-CLNC01/ses-M000/anat/sub-CLNC01_ses-M000_T1w.nii.gz') + 'subjects/sub-CLNC01/ses-M000' + >>> container_from_filename('caps/subjects/sub-CLNC01/ses-M000/dwi/preprocessing/sub-CLNC01_ses-M000_preproc.nii') + 'subjects/sub-CLNC01/ses-M000' + """ + import os + import re + + bids_or_caps_filename = str(bids_or_caps_filename) # Path(bids_or_caps_filename) + + m = re.search(r"(sub-[a-zA-Z0-9]+)/(ses-[a-zA-Z0-9]+)", bids_or_caps_filename) + if not m: + raise ValueError( + f"Input filename {bids_or_caps_filename} is not in a BIDS or CAPS compliant format." + "It does not contain the participant and session ID." + ) + subject = m.group(1) + session = m.group(2) + + return f"subjects/{subject}/{session}" + + +def read_participant_tsv(tsv_file: str) -> Tuple[List[str], List[str]]: + """Extract participant IDs and session IDs from TSV file. + + Parameters + ---------- + tsv_file: str + Participant TSV file from which to extract the participant and session IDs. + + Returns + ------- + participants: List[str] + List of participant IDs. + + sessions: List[str] + List of session IDs. + + Raises + ------ + ClinicaException + If `tsv_file` is not a file. + If `participant_id` or `session_id` column is missing from TSV file. + + Examples + -------- + >>> dframe = pd.DataFrame({ + ... "participant_id": ["sub-01", "sub-01", "sub-02"], + ... "session_id": ["ses-M000", "ses-M006", "ses-M000"], + ...}) + >>> dframe.to_csv("participants.tsv", sep="\t") + >>> read_participant_tsv("participant.tsv") + (["sub-01", "sub-01", "sub-02"], ["ses-M000", "ses-M006", "ses-M000"]) + """ + import pandas as pd + + from clinicadl.utils.exceptions import ClinicaDLException + + tsv_file = Path(tsv_file) + + try: + df = pd.read_csv(tsv_file, sep="\t") + except FileNotFoundError: + raise ClinicaDLException( + "The TSV file you gave is not a file.\nError explanations:\n" + f"\t- Clinica expected the following path to be a file: {tsv_file}\n" + "\t- If you gave relative path, did you run Clinica on the good folder?" + ) + + for column in ("participant_id", "session_id"): + if column not in list(df.columns.values): + raise ClinicaDLException( + f"The TSV file does not contain {column} column (path: {tsv_file})" + ) + + return ( + [sub.strip(" ") for sub in list(df.participant_id)], + [ses.strip(" ") for ses in list(df.session_id)], + ) + + +def get_subject_session_list( + input_dir: str, + subject_session_file: Optional[str] = None, + is_bids_dir: bool = True, + use_session_tsv: bool = False, + tsv_dir: Optional[str] = None, +) -> Tuple[List[str], List[str]]: + """Parse a BIDS or CAPS directory to get the subjects and sessions. + + This function lists all the subjects and sessions based on the content of + the BIDS or CAPS directory or (if specified) on the provided + subject-sessions TSV file. + + Parameters + ---------- + input_dir : PathLike + A BIDS or CAPS directory path. + + subject_session_file : PathLike, optional + A subjects-sessions file in TSV format. + + is_bids_dir : bool, optional + Indicates if input_dir is a BIDS or CAPS directory. + Default=True. + + use_session_tsv : bool, optional + Specify if the list uses the sessions listed in the sessions.tsv files. + Default=False. + + tsv_dir : PathLike, optional + If TSV file does not exist, it will be created in output_dir. + If not specified, output_dir will be in folder + + Returns + ------- + subjects : list of str + Subjects list. + + sessions : list of str + Sessions list. + + Notes + ----- + This is a generic method based on folder names. If your dataset contains e.g.: + - sub-CLNC01/ses-M000/anat/sub-CLNC01_ses-M000_T1w.nii + - sub-CLNC02/ses-M000/dwi/sub-CLNC02_ses-M000_dwi.{bval|bvec|json|nii} + - sub-CLNC02/ses-M000/anat/sub-CLNC02_ses-M000_T1w.nii + get_subject_session_list(, None, True) will return + ['ses-M000', 'ses-M000'], ['sub-CLNC01', 'sub-CLNC02']. + + However, if your pipeline needs both T1w and DWI files, you will need to check + with e.g. clinicadl_file_reader_function. + """ + + input_dir = Path(input_dir) + + if not subject_session_file: + output_dir = tsv_dir if tsv_dir else Path(tempfile.mkdtemp()) + timestamp = strftime("%Y%m%d_%H%M%S", localtime(time())) + tsv_file = f"subjects_sessions_list_{timestamp}.tsv" + subject_session_file = output_dir / tsv_file + create_subs_sess_list( + input_dir=input_dir, + output_dir=output_dir, + file_name=tsv_file, + is_bids_dir=is_bids_dir, + use_session_tsv=use_session_tsv, + ) + + if subject_session_file is not None: + subject_session_file = Path(subject_session_file) + + if tsv_dir is not None: + tsv_dir = Path(tsv_dir) + + return read_participant_tsv(subject_session_file) + + +def create_subs_sess_list( + input_dir: str, + output_dir: str, + file_name: str = None, + is_bids_dir: bool = True, + use_session_tsv: bool = False, +): + """Create the file subject_session_list.tsv that contains the list of the visits for each subject for a BIDS or CAPS compliant dataset. + + Args: + input_dir (str): Path to the BIDS or CAPS directory. + output_dir (str): Path to the output directory + file_name: name of the output file + is_bids_dir (boolean): Specify if input_dir is a BIDS directory or + not (i.e. a CAPS directory) + use_session_tsv (boolean): Specify if the list uses the sessions listed in the sessions.tsv files + """ + + input_dir = Path(input_dir) + output_dir = Path(output_dir) + + output_dir.mkdir(parents=True, exist_ok=True) + + if not file_name: + file_name = "subjects_sessions_list.tsv" + subjs_sess_tsv = open(output_dir / file_name, "w") + subjs_sess_tsv.write("participant_id" + "\t" + "session_id" + "\n") + + if is_bids_dir: + path_to_search = input_dir + else: + path_to_search = input_dir / "subjects" + subjects_paths = list(path_to_search.glob("*sub-*")) + + # Sort the subjects list + subjects_paths.sort() + + if len(subjects_paths) == 0: + raise IOError("Dataset empty or not BIDS/CAPS compliant.") + + for sub_path in subjects_paths: + subj_id = sub_path.name + + if use_session_tsv: + session_df = pd.read_csv(sub_path / subj_id + "_sessions.tsv", sep="\t") + session_df.dropna(how="all", inplace=True) + session_list = sorted(list(session_df["session_id"].to_numpy())) + for session in session_list: + subjs_sess_tsv.write(subj_id + "\t" + session + "\n") + + else: + sess_list = list(sub_path.glob("*ses-*")) + + for ses_path in sorted(sess_list): + session_name = ses_path.name + subjs_sess_tsv.write(subj_id + "\t" + session_name + "\n") + + subjs_sess_tsv.close() + + +def insensitive_glob(pattern_glob: str, recursive: Optional[bool] = False) -> List[str]: + """This function is the glob.glob() function that is insensitive to the case. + + Parameters + ---------- + pattern_glob : str + Sensitive-to-the-case pattern. + + recursive : bool, optional + Recursive parameter for `glob.glob()`. + Default=False. + + Returns + ------- + List[str] : + Insensitive-to-the-case pattern. + """ + + def make_case_insensitive_pattern(c: str) -> str: + return "[%s%s]" % (c.lower(), c.upper()) if c.isalpha() else c + + insensitive_pattern = "".join(map(make_case_insensitive_pattern, pattern_glob)) + return glob(insensitive_pattern, recursive=recursive) + + +def determine_caps_or_bids(input_dir: str) -> bool: + """Determine if the `input_dir` is a CAPS or a BIDS folder. + + Parameters + ---------- + input_dir : Path + The input folder. + + Returns + ------- + bool : + True if `input_dir` is a BIDS folder, False if `input_dir` + is a CAPS folder or could not be determined. + """ + + input_dir = Path(input_dir) + + subjects_dir = input_dir / "subjects" + groups_dir = input_dir / "groups" + + dir_to_look = subjects_dir if subjects_dir.is_dir() else input_dir + subjects_sub_folders = _list_subjects_sub_folders(dir_to_look, groups_dir) + if subjects_dir.is_dir(): + return False + return len(subjects_sub_folders) > 0 + + +def _list_subjects_sub_folders(root_dir: str, groups_dir: str) -> List[str]: + + root_dir = Path(root_dir) + groups_dir = Path(groups_dir) + + warning_msg = ( + f"Could not determine if {groups_dir.parent} is a CAPS or BIDS directory. " + "Clinica will assume this is a CAPS directory." + ) + folder_content = [f for f in root_dir.iterdir()] + subjects_sub_folders = [ + sub for sub in folder_content if (sub.name.startswith("sub-") and sub.is_dir()) + ] + if len(subjects_sub_folders) == 0 and not groups_dir.is_dir(): + cprint(msg=warning_msg, lvl="warning") + + return str(subjects_sub_folders) + + +def _common_checks(directory: str, folder_type: str) -> None: + """Utility function which performs checks common to BIDS and CAPS folder structures. + + Parameters + ---------- + directory : PathLike + Directory to check. + + folder_type : {"BIDS", "CAPS"} + The type of directory. + """ + + directory = Path(directory) + + if not isinstance(directory, (Path, str)): + raise ValueError( + f"Argument you provided to check_{folder_type.lower()}_folder() is not a string." + ) + + error = ClinicaDLBIDSError if folder_type == "BIDS" else ClinicaDLCAPSError + + if not directory.is_dir(): + raise error( + f"The {folder_type} directory you gave is not a folder.\n" + "Error explanations:\n" + f"\t- Clinica expected the following path to be a folder: {directory}\n" + "\t- If you gave relative path, did you run Clinica on the good folder?" + ) + + +def check_bids_folder(bids_directory: str) -> None: + """Check if provided `bids_directory` is a BIDS folder. + + Parameters + ---------- + bids_directory : PathLike + The input folder to check. + + Raises + ------ + ValueError : + If `bids_directory` is not a string. + + ClinicaDLBIDSError : + If the provided path does not exist, or is not a directory. + If the provided path is a CAPS folder (BIDS and CAPS could + be swapped by user). We simply check that there is not a folder + called 'subjects' in the provided path (that exists in CAPS hierarchy). + If the provided folder is empty. + If the provided folder does not contain at least one directory whose + name starts with 'sub-'. + """ + + bids_directory = Path(bids_directory) + + _common_checks(bids_directory, "BIDS") + + if (bids_directory / "subjects").is_dir(): + raise ClinicaDLBIDSError( + f"The BIDS directory ({bids_directory}) you provided seems to " + "be a CAPS directory due to the presence of a 'subjects' folder." + ) + + if len([f for f in bids_directory.iterdir()]) == 0: + raise ClinicaDLBIDSError( + f"The BIDS directory you provided is empty. ({bids_directory})." + ) + + subj = [f for f in bids_directory.iterdir() if f.name.startswith("sub-")] + if len(subj) == 0: + raise ClinicaDLBIDSError( + "Your BIDS directory does not contains a single folder whose name " + "starts with 'sub-'. Check that your folder follow BIDS standard." + ) + + +def check_caps_folder(caps_directory: str) -> None: + """Check if provided `caps_directory`is a CAPS folder. + + Parameters + ---------- + caps_directory : Path + The input folder to check. + + Raises + ------ + ValueError : + If `caps_directory` is not a string. + + ClinicaCAPSError : + If the provided path does not exist, or is not a directory. + If the provided path is a BIDS folder (BIDS and CAPS could be + swapped by user). We simply check that there is not a folder + whose name starts with 'sub-' in the provided path (that exists + in BIDS hierarchy). + + Notes + ----- + Keep in mind that a CAPS folder can be empty. + """ + from clinicadl.utils.exceptions import ClinicaDLCAPSError + + caps_directory = Path(caps_directory) + + _common_checks(caps_directory, "CAPS") + + sub_folders = [f for f in caps_directory.iterdir() if f.name.startswith("sub-")] + if len(sub_folders) > 0: + error_string = ( + "Your CAPS directory contains at least one folder whose name " + "starts with 'sub-'. Check that you did not swap BIDS and CAPS folders.\n" + "Folder(s) found that match(es) BIDS architecture:\n" + ) + for directory in sub_folders: + error_string += f"\t{directory}\n" + error_string += ( + "A CAPS directory has a folder 'subjects' at its root, in which " + "are stored the output of the pipeline for each subject." + ) + raise ClinicaDLCAPSError(error_string) + + +def find_sub_ses_pattern_path( + input_directory: str, + subject: str, + session: str, + error_encountered: list, + results: list, + is_bids: bool, + pattern: str, +) -> None: + """Appends the output path corresponding to subject, session and pattern in results. + + If an error is encountered, its corresponding message is added to the list `error_encountered`. + + Parameters + ---------- + input_directory : str + Path to the root of the input directory (BIDS or CAPS). + + .. warning:: + This function does not perform any check on `input_directory`. + It is assumed that it has been previously checked by either + `check_bids_directory` or `check_caps_directory`, and that + the flag `is_bids` has been set accordingly. + + subject : str + Name given to the folder of a participant (ex: sub-ADNI002S0295). + + session : str + Name given to the folder of a session (ex: ses-M00). + + error_encountered : List + List to which errors encountered in this function are added. + + results : List + List to which the output path corresponding to subject, session + and pattern is added. + + is_bids : bool + True if `input_dir` is a BIDS folder, False if `input_dir` is a + CAPS folder. + + pattern : str + Define the pattern of the final file. + """ + + input_directory = Path(input_directory) + + if is_bids: + origin_pattern = input_directory / subject / session + else: + origin_pattern = input_directory / "subjects" / subject / session + + current_pattern = origin_pattern / "**" / pattern + current_glob_found = insensitive_glob(str(current_pattern), recursive=True) + if len(current_glob_found) > 1: + # If we have more than one file at this point, there are two possibilities: + # - there is a problem somewhere which made us catch too many files + # --> In this case, we raise an error. + # - we have captured multiple runs for the same subject and session + # --> In this case, we need to select one of these runs to proceed. + # Ideally, this should be done via QC but for now, we simply + # select the latest run and warn the user about it. + if _are_multiple_runs(current_glob_found): + selected = _select_run(current_glob_found) + list_of_found_files_for_reporting = "" + for filename in current_glob_found: + list_of_found_files_for_reporting += f"- {filename}\n" + cprint( + f"More than one run were found for subject {subject} and session {session} : " + f"\n\n{list_of_found_files_for_reporting}\n" + f"Clinica will proceed with the latest run available, that is \n\n-{selected}.", + lvl="warning", + ) + results.append(selected) + else: + error_str = f"\t* ({subject} | {session}): More than 1 file found:\n" + for found_file in current_glob_found: + error_str += f"\t\t{found_file}\n" + error_encountered.append(error_str) + elif len(current_glob_found) == 0: + error_encountered.append(f"\t* ({subject} | {session}): No file found\n") + # Otherwise the file found is added to the result + else: + results.append(current_glob_found[0]) + + +def _are_multiple_runs(files: List[str]) -> bool: + """Returns whether the files in the provided list only differ through their run number. + + The provided files must have exactly the same parent paths, extensions, and BIDS entities + excepted for the 'run' entity which must be different. + + Parameters + ---------- + files : List of str + The files to analyze. + + Returns + ------- + bool : + True if the provided files only differ through their run number, False otherwise. + """ + + _files = files + files = [] + for file in _files: + files.append(Path(file)) + + # Exit quickly if less than one file or if at least one file does not have the entity run + if len(files) < 2 or any(["_run-" not in f.name for f in files]): + return False + try: + _check_common_parent_path(files) + _check_common_extension(files) + common_suffix = _check_common_suffix(files) + except ValueError: + return False + found_entities = _get_entities(files, common_suffix) + for entity_name, entity_values in found_entities.items(): + if entity_name != "run": + # All entities except run numbers should be the same + if len(entity_values) != 1: + return False + else: + # Run numbers should differ otherwise this is a BIDS violation at this point + if len(entity_values) != len(files): + return False + return True + + +def get_filename_no_ext(filename: str) -> str: + """Get the filename without the extension. + + Parameters + ---------- + filename: str + The full filename from which to extract the extension out. + + Returns + ------- + filename_no_ext: str + The filename with extension removed. + + Examples + -------- + >>> get_filename_no_ext("foo.nii.gz") + 'foo' + >>> get_filename_no_ext("sub-01/ses-M000/sub-01_ses-M000.tar.gz") + 'sub-01_ses-M000' + """ + + stem = PurePath(filename).stem + while "." in stem: + stem = PurePath(stem).stem + + return stem + + +def _get_entities(files: List[str], common_suffix: str) -> dict: + """Compute a dictionary where the keys are entity names and the values + are sets of all the corresponding entity values found while iterating over + the provided files. + + Parameters + ---------- + files : List of Path + List of paths to get entities of. + + common_suffix : str + The suffix common to all the files. This suffix will be stripped + from the file names in order to only analyze BIDS entities. + + Returns + ------- + dict : + The entities dictionary. + """ + + from collections import defaultdict + + _files = files + files = [] + for file in _files: + files.append(Path(file)) + + found_entities = defaultdict(set) + # found_entities = dict() + for f in files: + entities = get_filename_no_ext(f.name).rstrip(common_suffix).split("_") + for entity in entities: + entity_name, entity_value = entity.split("-") + found_entities[entity_name].add(entity_value) + + return found_entities + + +def _check_common_properties_of_files( + files: List[str], + property_name: str, + property_extractor: Callable, +) -> str: + """Verify that all provided files share the same property and return its value. + + Parameters + ---------- + files : List of Paths + List of file paths for which to verify common property. + + property_name : str + The name of the property to verify. + + property_extractor : Callable + The function which is responsible for the property extraction. + It must implement the interface `property_extractor(filename: Path) -> str` + + Returns + ------- + str : + The value of the common property. + + Raises + ------ + ValueError : + If the provided files do not have the same given property. + """ + + _files = files + files = [] + for file in _files: + files.append(Path(file)) + + extracted_properties = {property_extractor(f) for f in files} + if len(extracted_properties) != 1: + raise ValueError( + f"The provided files do not share the same {property_name}." + f"The following {property_name}s were found: {extracted_properties}" + ) + return extracted_properties.pop() + + +def _get_parent_path(filename: str) -> str: + filename = Path(filename) + return str(filename.parent) + + +def _get_extension(filename: str) -> str: + filename = Path(filename) + return "".join(filename.suffixes) + + +def _get_suffix(filename: str) -> str: + filename = Path(filename) + return f"_{get_filename_no_ext(filename.name).split('_')[-1]}" + + +_check_common_parent_path = partial( + _check_common_properties_of_files, + property_name="parent path", + property_extractor=_get_parent_path, +) + + +_check_common_extension = partial( + _check_common_properties_of_files, + property_name="extension", + property_extractor=_get_extension, +) + + +_check_common_suffix = partial( + _check_common_properties_of_files, + property_name="suffix", + property_extractor=_get_suffix, +) + + +def _select_run(files: List[str]) -> str: + import numpy as np + + runs = [int(_get_run_number(f)) for f in files] + return files[np.argmax(runs)] + + +def _get_run_number(filename: str) -> str: + import re + + matches = re.match(r".*_run-(\d+).*", filename) + if matches: + return matches[1] + raise ValueError(f"Filename {filename} should contain one and only one run entity.") + + +def _check_information(information: Dict) -> None: + if not isinstance(information, (dict, list)): + raise TypeError( + "A dict or list of dicts must be provided for the argument 'information'" + ) + + if isinstance(information, list): + for item in information: + if not all(elem in item for elem in ["pattern", "description"]): + raise ValueError( + "'information' must contain the keys 'pattern' and 'description'" + ) + + if not all( + elem in ["pattern", "description", "needed_pipeline"] + for elem in item.keys() + ): + raise ValueError( + "'information' can only contain the keys 'pattern', 'description' and 'needed_pipeline'" + ) + + if item["pattern"][0] == "/": + raise ValueError( + "pattern argument cannot start with char: / (does not work in os.path.join function). " + "If you want to indicate the exact name of the file, use the format " + "directory_name/filename.extension or filename.extension in the pattern argument." + ) + else: + if not all(elem in information for elem in ["pattern", "description"]): + raise ValueError( + "'information' must contain the keys 'pattern' and 'description'" + ) + + if not all( + elem in ["pattern", "description", "needed_pipeline"] + for elem in information.keys() + ): + raise ValueError( + "'information' can only contain the keys 'pattern', 'description' and 'needed_pipeline'" + ) + + if information["pattern"][0] == "/": + raise ValueError( + "pattern argument cannot start with char: / (does not work in os.path.join function). " + "If you want to indicate the exact name of the file, use the format " + "directory_name/filename.extension or filename.extension in the pattern argument." + ) + + +def _format_errors(errors: List, information: Dict) -> str: + error_message = ( + f"Clinica encountered {len(errors)} " + f"problem(s) while getting {information['description']}:\n" + ) + if "needed_pipeline" in information and information["needed_pipeline"]: + error_message += ( + "Please note that the following clinica pipeline(s) must " + f"have run to obtain these files: {information['needed_pipeline']}\n" + ) + error_message += "\n".join(errors) + + return error_message + + +def clinicadl_file_reader( + subjects: List[str], + sessions: List[str], + input_directory: str, + information: Dict, + raise_exception: Optional[bool] = True, + n_procs: Optional[int] = 1, +): + """Read files in BIDS or CAPS directory based on participant ID(s). + + This function grabs files relative to a subject and session list according to a glob pattern (using *) + + Parameters + ---------- + subjects : List[str] + List of subjects. + + sessions : List[str] + List of sessions. Must be same size as `subjects` and must correspond. + + input_directory : PathLike + Path to the BIDS or CAPS directory to read from. + + information : Dict + Dictionary containing all the relevant information to look for the files. + The possible keys are: + + - `pattern`: Required. Define the pattern of the final file. + - `description`: Required. String to describe what the file is. + - `needed_pipeline` : Optional. String describing the pipeline(s) + needed to obtain the related file. + + raise_exception : bool, optional + If True, an exception is raised if errors happen. If not, we return the file + list as it is. Default=True. + + n_procs : int, optional + Number of cores used to fetch files in parallel. + If set to 1, subjects and sessions will be processed sequentially. + Default=1. + + Returns + ------- + results : List[str] + List of files respecting the subject/session order provided in input. + + error_message : str + Error message which contains all errors encountered while reading the files. + + Raises + ------ + TypeError + If `information` is not a dictionary. + + ValueError + If `information` is not formatted correctly. See function `_check_information` + for more details. + If the length of `subjects` is different from the length of `sessions`. + + ClinicaDLCAPSError or ClinicaDLBIDSError + If multiples files are found for 1 subject/session, or if no file is found. + + .. note:: + If `raise_exception` is False, no exception is raised. + + Notes + ----- + This function is case-insensitive, meaning that the pattern argument can, for example, + contain upper case letters that do not exist in the existing file path. + + You should always use `clinicadl_file_reader` in the following manner: + + .. code-block:: python + + try: + file_list = clinicadl_file_reader(...) + except ClinicaException as e: + # Deal with the error + + Examples + -------- + The paths are shortened for readability. + + You have the full name of a file. + + File `orig_nu.mgz` from FreeSurfer of subject `sub-ADNI011S4105`, session `ses-M00` + located in mri folder of FreeSurfer output : + + >>> clinicadl_file_reader( + ['sub-ADNI011S4105'], + ['ses-M00'], + caps_directory, + { + 'pattern': 'freesurfer_cross_sectional/sub-*_ses-*/mri/orig_nu.mgz', + 'description': 'freesurfer file orig_nu.mgz', + 'needed_pipeline': 't1-freesurfer' + } + ) + ['/caps/subjects/sub-ADNI011S4105/ses-M00/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M00/mri/orig_nu.mgz'] + + You have a partial name of the file. + + File `sub-ADNI011S4105_ses-M00_trc-18FFDG_pet.nii.gz` in BIDS directory. + Here, filename depends on subject and session name : + + >>> clinicadl_file_reader( + ['sub-ADNI011S4105'], + ['ses-M00'], + bids_directory, + { + 'pattern': '*18FFDG_pet.nii*', + 'description': 'FDG PET data' + } + ) + ['/bids/sub-ADNI011S4105/ses-M00/pet/sub-ADNI011S4105_ses-M00_trc-18FFDG_pet.nii.gz'] + + Tricky example. + + Get the file `rh.white` from FreeSurfer : + + This will fail : + + >>> clinicadl_file_reader( + ['sub-ADNI011S4105'], + ['ses-M00'], + caps, + { + 'pattern': 'rh.white', + 'description': 'right hemisphere of outer cortical surface.', + 'needed_pipeline': 't1-freesurfer' + } + ) + * More than 1 file found:: + /caps/subjects/sub-ADNI011S4105/ses-M00/t1/freesurfer_cross_sectional/fsaverage/surf/rh.white + /caps/subjects/sub-ADNI011S4105/ses-M00/t1/freesurfer_cross_sectional/rh.EC_average/surf/rh.white + /caps/subjects/sub-ADNI011S4105/ses-M00/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M00/surf/rh.white + + Correct usage (e.g. in pet-surface): pattern string must be 'sub-*_ses-*/surf/rh.white', + or even more precise: 't1/freesurfer_cross_sectional/sub-*_ses-*/surf/rh.white' + It then gives: ['/caps/subjects/sub-ADNI011S4105/ses-M00/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M00/surf/rh.white'] + """ + from clinicadl.utils.exceptions import ClinicaDLBIDSError, ClinicaDLCAPSError + + input_directory = Path(input_directory) + + _check_information(information) + pattern = information["pattern"] + is_bids = determine_caps_or_bids(input_directory) + if is_bids: + check_bids_folder(input_directory) + else: + check_caps_folder(input_directory) + + if len(subjects) != len(sessions): + raise ValueError("Subjects and sessions must have the same length.") + if len(subjects) == 0: + return [], "" + + file_reader = _read_files_parallel if n_procs > 1 else _read_files_sequential + results, errors_encountered = file_reader( + input_directory, + subjects, + sessions, + is_bids, + pattern, + n_procs=n_procs, + ) + error_message = _format_errors(errors_encountered, information) + if len(errors_encountered) > 0 and raise_exception: + if is_bids: + raise ClinicaDLBIDSError(error_message) + else: + raise ClinicaDLCAPSError(error_message) + + return results, error_message + + +def _read_files_parallel( + input_directory: str, + subjects: List[str], + sessions: List[str], + is_bids: bool, + pattern: str, + n_procs: int, +) -> Tuple[List[str], List[str]]: + from multiprocessing import Manager + + from joblib import Parallel, delayed + + input_directory = Path(input_directory) + + manager = Manager() + shared_results = manager.list() + shared_errors_encountered = manager.list() + Parallel(n_jobs=n_procs)( + delayed(find_sub_ses_pattern_path)( + input_directory, + sub, + ses, + shared_errors_encountered, + shared_results, + is_bids, + pattern, + ) + for sub, ses in zip(subjects, sessions) + ) + results = list(shared_results) + errors_encountered = list(shared_errors_encountered) + return results, errors_encountered + + +def _read_files_sequential( + input_directory: str, + subjects: List[str], + sessions: List[str], + is_bids: bool, + pattern: str, + **kwargs, +) -> Tuple[List[str], List[str]]: + input_direcotry = Path(input_directory) + + errors_encountered, results = [], [] + for sub, ses in zip(subjects, sessions): + find_sub_ses_pattern_path( + input_directory, sub, ses, errors_encountered, results, is_bids, pattern + ) + return results, errors_encountered + + +def _sha256(path: str): + """Calculate the sha256 hash of the file at path.""" + path = Path(path) + sha256hash = hashlib.sha256() + chunk_size = 8192 + with open(path, "rb") as f: + while True: + buffer = f.read(chunk_size) + if not buffer: + break + sha256hash.update(buffer) + return sha256hash.hexdigest() + + +def fetch_file(remote: RemoteFileStructure, dirname: Optional[Path]) -> Path: + """Download a specific file and save it into the resources folder of the package. + + Parameters + ---------- + remote : RemoteFileStructure + Structure containing url, filename and checksum. + + dirname : str + Absolute path where the file will be downloaded. + + Returns + ------- + file_path : str + Absolute file path. + """ + + if not dirname.exists(): + cprint(msg="Path to the file does not exist", lvl="warning") + cprint(msg="Stop Clinica and handle this error", lvl="warning") + + file_path = dirname / remote.filename + # Download the file from `url` and save it locally under `file_name`: + gcontext = ssl.SSLContext() + req = Request(remote.url + remote.filename) + try: + response = urlopen(req, context=gcontext) + except URLError as e: + if hasattr(e, "reason"): + cprint(msg=f"We failed to reach a server. Reason: {e.reason}", lvl="error") + elif hasattr(e, "code"): + cprint( + msg=f"The server could not fulfill the request. Error code: {e.code}", + lvl="error", + ) + else: + try: + with open(file_path, "wb") as out_file: + shutil.copyfileobj(response, out_file) + except OSError as err: + cprint(msg="OS error: {0}".format(err), lvl="error") + + checksum = _sha256(file_path) + if remote.checksum != checksum: + raise IOError( + f"{file_path} has an SHA256 checksum ({checksum}) from expected " + f"({remote.checksum}), file may be corrupted." + ) + return file_path diff --git a/clinicadl/utils/cluster/profiler/__init__.py b/clinicadl/utils/cluster/profiler/__init__.py new file mode 100644 index 000000000..5aea07d28 --- /dev/null +++ b/clinicadl/utils/cluster/profiler/__init__.py @@ -0,0 +1,3 @@ +from torch.profiler import * + +from .patch_kineto import tensorboard_trace_handler diff --git a/clinicadl/utils/cluster/profiler/patch_kineto.py b/clinicadl/utils/cluster/profiler/patch_kineto.py new file mode 100644 index 000000000..2cc00602c --- /dev/null +++ b/clinicadl/utils/cluster/profiler/patch_kineto.py @@ -0,0 +1,86 @@ +import os +import sys +from functools import wraps +from re import sub +from typing import Optional + +import torch.profiler +from packaging.version import Version + + +class persistent_locals(object): + """ + Allows access to local variables of a function. + Shamelessly stolen from + https://stackoverflow.com/questions/9186395/python-is-there-a-way-to-get-a-local-function-variable-from-within-a-decorator + """ + + def __init__(self, func): + self._locals = {} + self.func = func + + def __call__(self, *args, **kwargs): + def tracer(frame, event, arg): + if event == "return": + self._locals = frame.f_locals.copy() + + # tracer is activated on next call, return or exception + sys.setprofile(tracer) + try: + # trace the function call + res = self.func(*args, **kwargs) + finally: + # disable tracer and replace with old one + sys.setprofile(None) + return res + + def clear_locals(self): + self._locals = {} + + @property + def locals(self): + return self._locals + + +if Version(torch.__version__) >= Version("1.12.0"): + # This tensorboard_trace_handler wraps Pytorch's version. It restores a feature + # of Kineto profiler which was lost when upgrading Pytorch from 1.11 to 1.12. + # In the profiler, some category names were changed. But in the tensorboard + # visualization from Kineto, those category have not been renamed accordingly. + # This loses the dataloader step profiling. We can restore this feature by + # renaming the category in the output trace file. + + @wraps(torch.profiler.tensorboard_trace_handler) + def tensorboard_trace_handler( + dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False + ): + handler_fn = torch.profiler.tensorboard_trace_handler( + dir_name=dir_name, + worker_name=worker_name, + use_gzip=use_gzip, + ) + handler_fn = persistent_locals(handler_fn) + + @wraps(handler_fn) + def wrapper(prof): + handler_fn(prof) + file_name = handler_fn._locals["file_name"] + dir_name = handler_fn._locals["dir_name"] + handler_fn.clear_locals() + with open(os.path.join(dir_name, file_name), "r+") as file: + content = file.read() + file.seek(0) + file.truncate() + + # Restore profiler steps and dataloader + new_content = sub("user_annotation", "cpu_op", content) + + # Restore runtime category + new_content = sub("cuda_runtime", "runtime", new_content) + + file.write(new_content) + + return wrapper + +else: + tensorboard_trace_handler = torch.profiler.tensorboard_trace_handler diff --git a/clinicadl/utils/cmdline_utils.py b/clinicadl/utils/cmdline_utils.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/descriptors.py b/clinicadl/utils/descriptors.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/early_stopping.py b/clinicadl/utils/early_stopping.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/exceptions.py b/clinicadl/utils/exceptions.py old mode 100644 new mode 100755 index 44ba0951d..c91994e9d --- a/clinicadl/utils/exceptions.py +++ b/clinicadl/utils/exceptions.py @@ -28,3 +28,11 @@ class ClinicaDLDataLeakageError(ClinicaDLException): class ClinicaDLTSVError(ClinicaDLException): """Base class for tsv files exceptions.""" + + +class ClinicaDLBIDSError(ClinicaDLException): + """Base class for tsv files exceptions.""" + + +class ClinicaDLCAPSError(ClinicaDLException): + """Base class for tsv files exceptions.""" diff --git a/clinicadl/utils/logger.py b/clinicadl/utils/logger.py new file mode 100644 index 000000000..b5cfabad7 --- /dev/null +++ b/clinicadl/utils/logger.py @@ -0,0 +1,109 @@ +import logging +import sys +from enum import Enum +from pathlib import Path + + +class StdLevelFilter(logging.Filter): + def __init__(self, err=False): + super().__init__() + self.err = err + + def filter(self, record): + if record.levelno <= logging.WARNING: + return not self.err + return self.err + + +# Create formatter for console +class ConsoleFormatter(logging.Formatter): + FORMATS = { + logging.INFO: "%(asctime)s - %(message)s", + logging.WARNING: "%(asctime)s - %(levelname)s: %(message)s", + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt, "%H:%M:%S") + return formatter.format(record) + + +def setup_logging(verbose: bool = False) -> None: + """ + Setup ClinicaDL's logging facilities. + Parameters + ---------- + verbose: bool + The desired level of verbosity for logging. + (False (default): INFO, True: DEBUG) + """ + logging_level = "DEBUG" if verbose else "INFO" + + # Define the module level logger. + logger = logging.getLogger("clinicadl") + logger.setLevel(logging_level) + + console_formatter = ConsoleFormatter() + console_handler = logging.StreamHandler(stream=sys.stdout) + console_handler.setLevel(logging.INFO) + console_handler.setFormatter(console_formatter) + + # est ce que les erreurs s'affichent sur la console + err_formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s: %(message)s", "%Y-%m-%d %H:%M:%S" + ) + err_handler = logging.StreamHandler(stream=sys.stderr) + err_handler.addFilter(StdLevelFilter(err=True)) + err_handler.setFormatter(err_formatter) + + logger.addHandler(console_handler) + logger.addHandler(err_handler) + + # Create file handler for debug mode with its own formatter and add it to the logger + if verbose: + debug_file_formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s: %(message)s", "%Y-%m-%d %H:%M:%S" + ) + debug_file_name = "clinicadl_debug.log" + file_handler = logging.FileHandler(debug_file_name) + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(debug_file_formatter) + logger.addHandler(file_handler) + logger.warning(f"Debug log will be saved at {Path.cwd() / debug_file_name}") + + +class LoggingLevel(str, Enum): + debug = "debug" + info = "info" + warning = "warning" + error = "error" + critical = "critical" + + +def cprint(msg: str, lvl: str = "info") -> None: + """ + Print message to the console at the desired logging level. + + Args: + msg (str): Message to print. + lvl (str): Logging level between "debug", "info", "warning", "error" and "critical". + The default value is "info". + """ + from logging import getLogger + + # Use the package level logger. + logger = getLogger("clinicadl.clinica") + + # Log message as info level. + if lvl == LoggingLevel.debug: + logger.debug(msg=msg) + elif lvl == LoggingLevel.info: + logger.info(msg=msg) + elif lvl == LoggingLevel.warning: + logger.warning(msg=msg) + elif lvl == LoggingLevel.error: + logger.error(msg=msg) + elif lvl == LoggingLevel.critical: + logger.critical(msg=msg) + else: + pass diff --git a/clinicadl/utils/maps_manager/__init__.py b/clinicadl/utils/maps_manager/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/maps_manager/iotools.py b/clinicadl/utils/maps_manager/iotools.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/maps_manager/logwriter.py b/clinicadl/utils/maps_manager/logwriter.py old mode 100644 new mode 100755 index 236bafb38..324cffce3 --- a/clinicadl/utils/maps_manager/logwriter.py +++ b/clinicadl/utils/maps_manager/logwriter.py @@ -158,15 +158,15 @@ def step(self, epoch, i, metrics_train, metrics_valid, len_epoch): row_df.to_csv(f, header=False, index=False, sep="\t") # Write tensorboard logs - global_step = i + epoch * len_epoch - for metric_idx, metric in enumerate(self.evaluation_metrics): - self.writer_train.add_scalar( - metric, - train_row[metric_idx], - global_step, - ) - self.writer_valid.add_scalar( - metric, - valid_row[metric_idx], - global_step, - ) + #global_step = i + epoch * len_epoch + #for metric_idx, metric in enumerate(self.evaluation_metrics): + # self.writer_train.add_scalar( + # metric, + # train_row[metric_idx], + # global_step, + # ) + # self.writer_valid.add_scalar( + # metric, + # valid_row[metric_idx], + # global_step, + # ) diff --git a/clinicadl/utils/maps_manager/maps_manager.py b/clinicadl/utils/maps_manager/maps_manager.py old mode 100644 new mode 100755 index 5ab9b3327..4202ca9e3 --- a/clinicadl/utils/maps_manager/maps_manager.py +++ b/clinicadl/utils/maps_manager/maps_manager.py @@ -6,6 +6,7 @@ from glob import glob from logging import getLogger from os import listdir, makedirs, path +from pathlib import Path, PosixPath from typing import Any, Dict, List, Optional, Tuple, Union import pandas as pd @@ -137,6 +138,77 @@ def train(self, split_list: List[int] = None, overwrite: bool = False): else: self._train_single(split_list, resume=False) + def train_pythae(self, split_list: List[int] = None): + """ + Train using Pythae procedure + only works for single splits + """ + from pythae.pipelines import TrainingPipeline + from pythae.trainers.training_callbacks import TrainHistoryCallback + + from clinicadl.utils.caps_dataset.data import PythaeCAPS + + train_transforms, all_transforms = get_transforms( + normalize=self.normalize, + data_augmentation=self.data_augmentation, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, + ) + + split_manager = self._init_split_manager(split_list) + for split in split_manager.split_iterator(): + logger.info(f"Training split {split}") + + model_dir = path.join(self.maps_path, f"split-{split}", "best-loss") + if not path.exists(model_dir): + os.makedirs(model_dir) + + seed_everything(self.seed, self.deterministic, self.compensation) + + split_df_dict = split_manager[split] + train_dataset = PythaeCAPS( + self.caps_directory, + split_df_dict["train"], + self.preprocessing_dict, + train_transformations=train_transforms, + all_transformations=all_transforms, + ) + eval_dataset = PythaeCAPS( + self.caps_directory, + split_df_dict["validation"], + self.preprocessing_dict, + train_transformations=train_transforms, + all_transformations=all_transforms, + ) + + # Import the model + clinicadl_model, _ = self._init_model( + split=split, + gpu=True, + ) + model = clinicadl_model.model + config = clinicadl_model.get_trainer_config( + output_dir=model_dir, + num_epochs=self.epochs, + learning_rate=self.learning_rate, + batch_size=self.batch_size, + optimizer=self.optimizer, + ) + # Create Pythae Training Pipeline + pipeline = TrainingPipeline(training_config=config, model=model) + + # Launch training + pipeline( + train_data=train_dataset, # must be torch.Tensor or np.array + eval_data=eval_dataset, # must be torch.Tensor or np.array + callbacks=[ + TrainHistoryCallback(), + ], + ) + # Move saved model to the correct path in the MAPS + src = path.join(model_dir, "*_training_*/final_model/model.pt") + os.system(f"mv {src} {model_dir}") + def resume(self, split_list: List[int] = None): """ Resumes the training task for a defined list of splits. @@ -172,7 +244,7 @@ def predict( self, data_group: str, caps_directory: str = None, - tsv_path: str = None, + tsv_path: Path = None, split_list: List[int] = None, selection_metrics: List[str] = None, multi_cohort: bool = False, @@ -186,6 +258,11 @@ def predict( label_code: Optional[Dict[str, int]] = "default", save_tensor: bool = False, save_nifti: bool = False, + save_latent_tensor: bool = False, + pythae: bool = False, + sample_latent: int = 0, + save_caps: bool = False, + skip_leak_check: bool = False, ): """ Performs the prediction task on a subset of caps_directory defined in a TSV file. @@ -210,16 +287,27 @@ def predict( overwrite: If True erase the occurrences of data_group. label: Target label used for training (if network_task in [`regression`, `classification`]). label_code: dictionary linking the target values to a node number. + sample_latent: For reconstruction task only, will sample the latent space multiple times to generate + multiple reconstructions for a single input. """ - if split_list is None: + if not split_list: split_list = self._find_splits() logger.debug(f"List of splits {split_list}") _, all_transforms = get_transforms( normalize=self.normalize, data_augmentation=self.data_augmentation, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, ) + if pythae: + from clinicadl.utils.network.pythae.encoder_decoder_config import ( + make_encoder_decoder_config, + ) + + self.parameters = make_encoder_decoder_config(self.parameters) + group_df = None if tsv_path is not None: group_df = load_data_test( @@ -236,6 +324,7 @@ def predict( multi_cohort, overwrite, label=label, + skip_leak_check=skip_leak_check, ) for split in split_list: @@ -247,7 +336,7 @@ def predict( self.task_manager.generate_label_code(group_df, label) # Erase previous TSV files - if selection_metrics is None: + if not selection_metrics: split_selection_metrics = self._find_selection_metrics(split) else: split_selection_metrics = selection_metrics @@ -272,16 +361,17 @@ def predict( multi_cohort=group_parameters["multi_cohort"], label_presence=use_labels, label=self.label if label is None else label, - label_code=self.label_code - if label_code == "default" - else label_code, + label_code=( + self.label_code if label_code == "default" else label_code + ), cnn_index=network, + for_pythae=pythae, ) test_loader = DataLoader( data_test, - batch_size=batch_size - if batch_size is not None - else self.batch_size, + batch_size=( + batch_size if batch_size is not None else self.batch_size + ), shuffle=False, num_workers=n_proc if n_proc is not None else self.n_proc, ) @@ -294,25 +384,31 @@ def predict( use_labels=use_labels, gpu=gpu, network=network, + save_reconstruction_tensor=save_tensor, + save_reconstruction_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + sample_latent=sample_latent, + seed=self.parameters["seed"], + save_caps=save_caps, ) - if save_tensor: - self._compute_output_tensors( - data_test, - data_group, - split, - selection_metrics, - gpu=gpu, - network=network, - ) - if save_nifti: - self._compute_output_nifti( - data_test, - data_group, - split, - selection_metrics, - gpu=gpu, - network=network, - ) + if not pythae: + if ( + save_tensor + or save_nifti + or save_latent_tensor + or sample_latent + ): + self._save_model_output( + data_test, + data_group, + split, + split_selection_metrics, + save_reconstruction_tensor=save_tensor, + save_reconstruction_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + save_caps=save_caps, + gpu=gpu, + ) else: data_test = return_dataset( group_parameters["caps_directory"], @@ -325,13 +421,14 @@ def predict( label_code=self.label_code if label_code == "default" else label_code, + for_pythae=pythae, ) test_loader = DataLoader( data_test, - batch_size=batch_size - if batch_size is not None - else self.batch_size, + batch_size=( + batch_size if batch_size is not None else self.batch_size + ), shuffle=False, num_workers=n_proc if n_proc is not None else self.n_proc, ) @@ -343,24 +440,31 @@ def predict( split_selection_metrics, use_labels=use_labels, gpu=gpu, + for_pythae=pythae, + save_reconstruction_tensor=save_tensor, + save_reconstruction_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + sample_latent=sample_latent, + seed=self.parameters["seed"], + save_caps=save_caps, ) - if save_tensor: - self._compute_output_tensors( - data_test, - data_group, - split, - selection_metrics, - gpu=gpu, - ) - if save_nifti: - self._compute_output_nifti( - data_test, - data_group, - split, - selection_metrics, - gpu=gpu, - ) - self._ensemble_prediction(data_group, split, selection_metrics, use_labels) + if not pythae: + if save_tensor or save_nifti or save_latent_tensor or sample_latent: + self._save_model_output( + data_test, + data_group, + split, + split_selection_metrics, + save_reconstruction_tensor=save_tensor, + save_reconstruction_nifti=save_nifti, + save_latent_tensor=save_latent_tensor, + save_caps=save_caps, + gpu=gpu, + ) + + self._ensemble_prediction( + data_group, split, split_selection_metrics, use_labels + ) def interpret( self, @@ -432,6 +536,8 @@ def interpret( _, all_transforms = get_transforms( normalize=self.normalize, data_augmentation=self.data_augmentation, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, ) group_df = None @@ -500,7 +606,7 @@ def interpret( cum_maps = [0] * data_test.elem_per_image for data in test_loader: - images = data["image"].to(model.device) + images = data["data"].to(model.device) map_pt = interpreter.generate_gradients( images, target_node, level=level @@ -538,6 +644,8 @@ def _train_single(self, split_list=None, resume=False): train_transforms, all_transforms = get_transforms( normalize=self.normalize, data_augmentation=self.data_augmentation, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, ) split_manager = self._init_split_manager(split_list) @@ -581,6 +689,7 @@ def _train_single(self, split_list=None, resume=False): sampler=train_sampler, num_workers=self.n_proc, worker_init_fn=pl_worker_init_function, + drop_last=False, ) logger.debug(f"Train loader size is {len(train_loader)}") valid_loader = DataLoader( @@ -588,6 +697,7 @@ def _train_single(self, split_list=None, resume=False): batch_size=self.batch_size, shuffle=False, num_workers=self.n_proc, + drop_last=False, ) logger.debug(f"Validation loader size is {len(valid_loader)}") @@ -624,6 +734,8 @@ def _train_multi(self, split_list: List[int] = None, resume: bool = False): train_transforms, all_transforms = get_transforms( normalize=self.normalize, data_augmentation=self.data_augmentation, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, ) split_manager = self._init_split_manager(split_list) @@ -686,6 +798,7 @@ def _train_multi(self, split_list: List[int] = None, resume: bool = False): sampler=train_sampler, num_workers=self.n_proc, worker_init_fn=pl_worker_init_function, + drop_last=False, ) valid_loader = DataLoader( @@ -693,6 +806,7 @@ def _train_multi(self, split_list: List[int] = None, resume: bool = False): batch_size=self.batch_size, shuffle=False, num_workers=self.n_proc, + drop_last=False, ) self._train( @@ -743,7 +857,7 @@ def _train( transfer_selection=self.transfer_selection_metric, ) criterion = self.task_manager.get_criterion(self.loss) - logger.debug(f"Criterion for {self.network_task} is {criterion}") + logger.info(f"Criterion for {self.network_task} is {criterion}") optimizer = self._init_optimizer(model, split=split, resume=resume) logger.debug(f"Optimizer used for training is optimizer") @@ -757,7 +871,8 @@ def _train( log_writer = LogWriter( self.maps_path, - self.task_manager.evaluation_metrics + ["loss"], + self.task_manager.evaluation_metrics + + ["loss", "recon_loss", "kl_loss"], # to change split, resume=resume, beginning_epoch=beginning_epoch, @@ -848,7 +963,6 @@ def _train( model.train() train_loader.dataset.train() - log_writer.step(epoch, i, metrics_train, metrics_valid, len(train_loader)) logger.info( f"{self.mode} level training loss is {metrics_train['loss']} " @@ -902,7 +1016,7 @@ def _train( ) if self.task_manager.save_outputs: - self._compute_output_tensors( + self._save_model_output( train_loader.dataset, "train", split, @@ -910,7 +1024,7 @@ def _train( nb_images=1, network=network, ) - self._compute_output_tensors( + self._save_model_output( train_loader.dataset, "validation", split, @@ -929,6 +1043,13 @@ def _test_loader( use_labels=True, gpu=None, network=None, + for_pythae=False, + save_reconstruction_tensor=False, + save_reconstruction_nifti=False, + save_latent_tensor=False, + sample_latent=0, + seed=None, + save_caps=False, ): """ Launches the testing task on a dataset wrapped by a DataLoader and writes prediction TSV files. @@ -951,6 +1072,7 @@ def _test_loader( f"best-{selection_metric}", data_group, ) + self.write_description_log( log_dir, data_group, @@ -967,19 +1089,91 @@ def _test_loader( network=network, ) - prediction_df, metrics = self.task_manager.test( - model, dataloader, criterion, use_labels=use_labels - ) + tensor_path = None + if save_reconstruction_tensor: + tensor_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "tensors", + ) + makedirs(tensor_path, exist_ok=True) + + nifti_path = None + if save_reconstruction_nifti: + nifti_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "nifti_images", + ) + makedirs(nifti_path, exist_ok=True) + + latent_tensor_path = None + if save_latent_tensor: + latent_tensor_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "latent_tensors", + ) + makedirs(latent_tensor_path, exist_ok=True) + + caps_path = None + if save_caps: + caps_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + "CapsOutput", + ) + makedirs(caps_path, exist_ok=True) + + if for_pythae: + ( + prediction_df, + metrics, + sample_latent_prediction_df, + sample_latent_metrics_df, + ) = self.task_manager.test_pythae( + model, + dataloader, + criterion, + use_labels=use_labels, + save_reconstruction_tensor=save_reconstruction_tensor, + save_reconstruction_nifti=save_reconstruction_nifti, + save_latent_tensor=save_latent_tensor, + save_caps=save_caps, + tensor_path=tensor_path, + nifti_path=nifti_path, + latent_tensor_path=latent_tensor_path, + caps_path=caps_path, + sample_latent=sample_latent, + seed=seed, + ) + else: + prediction_df, metrics = self.task_manager.test( + model, dataloader, criterion, use_labels=use_labels + ) if use_labels: if network is not None: metrics[f"{self.mode}_id"] = network - logger.info( - f"{self.mode} level {data_group} loss is {metrics['loss']} for model selected on {selection_metric}" - ) + # logger.info( + # f"{self.mode} level {data_group} loss is {metrics['loss']} for model selected on {selection_metric}" + # ) # Replace here self._mode_level_to_tsv( - prediction_df, metrics, split, selection_metric, data_group=data_group + prediction_df, + metrics, + split, + selection_metric, + data_group=data_group, + sample_latent_results_df=sample_latent_prediction_df, + sample_latent_metrics_df=sample_latent_metrics_df, ) def _compute_output_nifti( @@ -1028,7 +1222,10 @@ def _compute_output_nifti( nb_imgs = len(dataset) for i in range(nb_imgs): data = dataset[i] - image = data["image"] + try: + image = data["data"] + except: + image = data["data"] output = ( model.predict(image.unsqueeze(0).to(model.device)) .squeeze(0) @@ -1037,7 +1234,7 @@ def _compute_output_nifti( ) # Convert tensor to nifti image with appropriate affine input_nii = nib.Nifti1Image(image[0].detach().cpu().numpy(), eye(4)) - output_nii = nib.Nifti1Image(output[0].numpy(), eye(4)) + output_nii = nib.Nifti1Image(output[0].detach().cpu().numpy(), eye(4)) # Create file name according to participant and session id participant_id = data["participant_id"] session_id = data["session_id"] @@ -1046,12 +1243,16 @@ def _compute_output_nifti( nib.save(input_nii, path.join(nifti_path, input_filename)) nib.save(output_nii, path.join(nifti_path, output_filename)) - def _compute_output_tensors( + def _save_model_output( self, dataset, data_group, split, selection_metrics, + save_reconstruction_tensor=True, + save_reconstruction_nifti=False, + save_latent_tensor=False, + save_caps=False, nb_images=None, gpu=None, network=None, @@ -1068,6 +1269,10 @@ def _compute_output_tensors( gpu (bool): If given, a new value for the device of the model will be computed. network (int): Index of the network tested (only used in multi-network setting). """ + + import nibabel as nib + from numpy import eye + for selection_metric in selection_metrics: # load the best trained model during the training model, _ = self._init_model( @@ -1078,14 +1283,44 @@ def _compute_output_tensors( network=network, ) - tensor_path = path.join( - self.maps_path, - f"{self.split_name}-{split}", - f"best-{selection_metric}", - data_group, - "tensors", - ) - makedirs(tensor_path, exist_ok=True) + if save_reconstruction_tensor: + tensor_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "tensors", + ) + makedirs(tensor_path, exist_ok=True) + + if save_reconstruction_nifti: + nifti_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "nifti_images", + ) + makedirs(nifti_path, exist_ok=True) + + if save_latent_tensor: + latent_tensor_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + data_group, + "latent_tensors", + ) + makedirs(latent_tensor_path, exist_ok=True) + + if save_caps: + caps_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + "CapsOutput", + ) + makedirs(caps_path, exist_ok=True) if nb_images is None: # Compute outputs for the whole data set nb_modes = len(dataset) @@ -1094,21 +1329,140 @@ def _compute_output_tensors( for i in range(nb_modes): data = dataset[i] - image = data["image"] - output = ( - model.predict(image.unsqueeze(0).to(model.device)).squeeze(0).cpu() - ) + image = data["data"] + data["data"] = data["data"].unsqueeze(0) + output = model.predict(data) participant_id = data["participant_id"] session_id = data["session_id"] mode_id = data[f"{self.mode}_id"] - input_filename = ( - f"{participant_id}_{session_id}_{self.mode}-{mode_id}_input.pt" - ) - output_filename = ( - f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output.pt" - ) - torch.save(image, path.join(tensor_path, input_filename)) - torch.save(output, path.join(tensor_path, output_filename)) + + if save_reconstruction_tensor: + reconstruction = output["recon_x"].squeeze(0).cpu() + input_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_input.pt" + ) + output_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output.pt" + ) + torch.save(image, path.join(tensor_path, input_filename)) + torch.save(reconstruction, path.join(tensor_path, output_filename)) + logger.debug(f"File saved at {[input_filename, output_filename]}") + + if save_reconstruction_nifti: + # Convert tensor to nifti image with appropriate affine + reconstruction = output["recon_x"].squeeze(0).cpu() + input_nii = nib.Nifti1Image(image[0].detach().numpy(), eye(4)) + output_nii = nib.Nifti1Image( + reconstruction[0].detach().numpy(), eye(4) + ) + # Create file name according to participant and session id + input_filename = f"{participant_id}_{session_id}_image_input.nii.gz" + output_filename = ( + f"{participant_id}_{session_id}_image_output.nii.gz" + ) + nib.save(input_nii, path.join(nifti_path, input_filename)) + nib.save(output_nii, path.join(nifti_path, output_filename)) + + if save_latent_tensor: + latent = output["embedding"].squeeze(0).cpu() + output_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent.pt" + ) + torch.save(latent, path.join(latent_tensor_path, output_filename)) + + if save_caps: + reconstruction = output["recon_x"].squeeze(0).cpu() + input_nii = nib.Nifti1Image(image[0].detach().numpy(), eye(4)) + output_nii = nib.Nifti1Image( + reconstruction[0].detach().numpy(), eye(4) + ) + latent_nii = nib.Nifti1Image( + output["embedding"].squeeze(0).cpu().detach().numpy(), eye(4) + ) + + input_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_input.nii.gz" + output_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output.nii.gz" + latent_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent.nii.gz" + + caps_sub_ses_path = path.join( + caps_path, + "subjects", + participant_id, + session_id, + "custom", + ) + makedirs(caps_sub_ses_path, exist_ok=True) + + nib.save(input_nii, path.join(caps_sub_ses_path, input_filename)) + nib.save(output_nii, path.join(caps_sub_ses_path, output_filename)) + nib.save(latent_nii, path.join(latent_tensor_path, latent_filename)) + + logger.debug( + f"File saved at {[input_filename, output_filename, latent_filename]}" + ) + + if sample_latent > 0: + data = dataset[i] + image = data["data"] + data["data"] = data["data"].unsqueeze(0) + participant_id = data["participant_id"] + session_id = data["session_id"] + mode_id = data[f"{self.mode}_id"] + + outputs = model.predict( + data, sample_latent=sample_latent, seed=seed + ) + + for i in range(sample_latent): + output = outputs[i] + + reconstruction = output["recon_x"].squeeze(0).cpu() + output_pt_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output-{i}.pt" + torch.save( + reconstruction, path.join(tensor_path, output_pt_filename) + ) + logger.debug(f"File saved at {output_pt_filename}") + + # Convert tensor to nifti image with appropriate affine + input_nii = nib.Nifti1Image(image[0].detach().numpy(), eye(4)) + output_nii = nib.Nifti1Image( + reconstruction[0].detach().numpy(), eye(4) + ) + # Create file name according to participant and session id + input_nii_filename = ( + f"{participant_id}_{session_id}_input.nii.gz" + ) + output_nii_filename = ( + f"{participant_id}_{session_id}_output-{i}.nii.gz" + ) + nib.save(input_nii, path.join(nifti_path, input_nii_filename)) + nib.save(output_nii, path.join(nifti_path, output_nii_filename)) + + latent = output["embedding"].squeeze(0).cpu() + latent_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent-{i}.pt" + torch.save( + latent, path.join(latent_tensor_path, latent_filename) + ) + + if save_caps: + nib.save( + input_nii, + path.join(caps_sub_ses_path, input_nii_filename), + ) + nib.save( + output_nii_filename, + path.join(caps_sub_ses_path, output_nii_filename), + ) + latent_nii = nib.Nifti1Image( + latent.detach().numpy(), eye(4) + ) + latent_nii_filename = ( + f"{participant_id}_{session_id}_latent-{i}.nii.gz" + ) + nib.save( + latent_nii, + path.join(caps_sub_ses_path, latent_nii_filename), + ) def _ensemble_prediction( self, @@ -1167,7 +1521,11 @@ def _check_args(self, parameters): if self.parameters["gpu"]: check_gpu() - _, transformations = get_transforms(self.normalize) + _, transformations = get_transforms( + normalize=self.normalize, + size_reduction=self.size_reduction, + size_reduction_factor=self.size_reduction_factor, + ) split_manager = self._init_split_manager(None) train_df = split_manager[0]["train"] @@ -1182,6 +1540,7 @@ def _check_args(self, parameters): self.parameters["selection_threshold"] = None if ( "label_code" not in self.parameters + or self.parameters["label_code"] is None or len(self.parameters["label_code"]) == 0 ): # Allows to set custom label code in TOML self.parameters["label_code"] = self.task_manager.generate_label_code( @@ -1311,6 +1670,7 @@ def _check_data_group( multi_cohort=False, overwrite=False, label=None, + skip_leak_check: bool = False, ): """ Check if a data group is already available if other arguments are None. @@ -1330,7 +1690,10 @@ def _check_data_group( when caps_directory or df are given but data group already exists when caps_directory or df are not given and data group does not exist """ - group_path = path.join(self.maps_path, "groups", data_group) + + maps_out_path = self.maps_path + + group_path = path.join(maps_out_path, "groups", data_group) logger.debug(f"Group path {group_path}") if path.exists(group_path): # Data group already exists if overwrite: @@ -1343,7 +1706,7 @@ def _check_data_group( selection_metrics = self._find_selection_metrics(split) for selection in selection_metrics: results_path = path.join( - self.maps_path, + maps_out_path, f"{self.split_name}-{split}", f"best-{selection}", data_group, @@ -1367,7 +1730,8 @@ def _check_data_group( elif not path.exists( group_path ): # Data group does not exist yet / was overwritten + all data is provided - self._check_leakage(data_group, df) + if not skip_leak_check: + self._check_leakage(data_group, df) self._write_data_group( data_group, df, caps_directory, multi_cohort, label=label ) @@ -1382,6 +1746,15 @@ def write_parameters(json_path, parameters, verbose=True): makedirs(json_path, exist_ok=True) # save to json file + for key, value in parameters.items(): + if type(value) is dict: + for key2, value2 in value.items(): + if type(value2) == PosixPath: + parameters[key2] = str(value2) + + if type(value) == PosixPath: + parameters[key] = str(value) + json_data = json.dumps(parameters, skipkeys=True, indent=4) json_path = path.join(json_path, "maps.json") if verbose: @@ -1409,7 +1782,7 @@ def _write_training_data(self): from clinicadl.utils.caps_dataset.data import load_data_test train_df = load_data_test( - self.tsv_path, + Path(self.tsv_path), self.diagnoses, baseline=False, multi_cohort=self.multi_cohort, @@ -1443,7 +1816,10 @@ def _write_data_group( caps_directory (str): caps_directory if different from the training caps_directory, multi_cohort (bool): multi_cohort used if different from the training multi_cohort. """ - group_path = path.join(self.maps_path, "groups", data_group) + + maps_out_path = self.maps_path + + group_path = path.join(maps_out_path, "groups", data_group) makedirs(group_path) columns = ["participant_id", "session_id", "cohort"] @@ -1458,12 +1834,14 @@ def _write_data_group( self.write_parameters( group_path, { - "caps_directory": caps_directory - if caps_directory is not None - else self.caps_directory, - "multi_cohort": multi_cohort - if multi_cohort is not None - else self.multi_cohort, + "caps_directory": ( + caps_directory + if caps_directory is not None + else self.caps_directory + ), + "multi_cohort": ( + multi_cohort if multi_cohort is not None else self.multi_cohort + ), }, ) @@ -1512,7 +1890,9 @@ def _write_weights( network: network number (multi-network framework). filename: name of the checkpoint file. """ - checkpoint_dir = path.join(self.maps_path, f"{self.split_name}-{split}", "tmp") + maps_path_out = self.maps_path + + checkpoint_dir = path.join(maps_path_out, f"{self.split_name}-{split}", "tmp") makedirs(checkpoint_dir, exist_ok=True) checkpoint_path = path.join(checkpoint_dir, filename) torch.save(state, checkpoint_path) @@ -1525,7 +1905,7 @@ def _write_weights( if metrics_dict is not None: for metric_name, metric_bool in metrics_dict.items(): metric_path = path.join( - self.maps_path, f"{self.split_name}-{split}", f"best-{metric_name}" + maps_path_out, f"{self.split_name}-{split}", f"best-{metric_name}" ) if metric_bool: makedirs(metric_path, exist_ok=True) @@ -1537,6 +1917,13 @@ def _write_information(self): """ Writes model architecture of the MAPS in MAPS root. """ + if "pythae" in self.parameters["architecture"]: + from clinicadl.utils.network.pythae.encoder_decoder_config import ( + make_encoder_decoder_config, + ) + + self.parameters = make_encoder_decoder_config(self.parameters) + from datetime import datetime import clinicadl.utils.network as network_package @@ -1601,6 +1988,8 @@ def _mode_level_to_tsv( split: int, selection: str, data_group: str = "train", + sample_latent_results_df: pd.DataFrame = None, + sample_latent_metrics_df: pd.DataFrame = None, ): """ Writes the outputs of the test function in tsv files. @@ -1612,8 +2001,10 @@ def _mode_level_to_tsv( selection: the metrics on which the model was selected (BA, loss...) data_group: the name referring to the data group on which evaluation is performed. """ + maps_out_path = self.maps_path + performance_dir = path.join( - self.maps_path, + maps_out_path, f"{self.split_name}-{split}", f"best-{selection}", data_group, @@ -1631,18 +2022,54 @@ def _mode_level_to_tsv( performance_path, index=False, sep="\t", mode="a", header=False ) + if sample_latent_results_df is not None: + latent_performance_path = path.join( + performance_dir, + f"{data_group}_{self.mode}_level_sample-latent_prediction.tsv", + ) + if not path.exists(latent_performance_path): + sample_latent_results_df.to_csv( + latent_performance_path, index=False, sep="\t" + ) + else: + sample_latent_results_df.to_csv( + latent_performance_path, + index=False, + sep="\t", + mode="a", + header=False, + ) + if sample_latent_metrics_df is not None: + latent_metrics_path = path.join( + performance_dir, + f"{data_group}_{self.mode}_level_sample-latent_metrics.tsv", + ) + if not path.exists(latent_metrics_path): + sample_latent_metrics_df.to_csv( + latent_metrics_path, index=False, sep="\t" + ) + else: + sample_latent_metrics_df.to_csv( + latent_metrics_path, index=False, sep="\t", mode="a", header=False + ) + metrics_path = path.join( performance_dir, f"{data_group}_{self.mode}_level_metrics.tsv" ) if metrics is not None: - if not path.exists(metrics_path): - pd.DataFrame(metrics, index=[0]).to_csv( - metrics_path, index=False, sep="\t" - ) + if isinstance(metrics, pd.DataFrame): + metrics.to_csv(metrics_path, sep="\t") else: - pd.DataFrame(metrics, index=[0]).to_csv( - metrics_path, index=False, sep="\t", mode="a", header=False - ) + if not path.exists(metrics_path): + pd.DataFrame(metrics, index=[0]).to_csv( + metrics_path, index=False, sep="\t" + ) + else: + pd.DataFrame(metrics, index=[0]).to_csv( + metrics_path, index=False, sep="\t", mode="a", header=False + ) + else: + results_df.describe().to_csv(metrics_path, sep="\t") def _ensemble_to_tsv( self, @@ -1674,8 +2101,10 @@ def _ensemble_to_tsv( validation_dataset, split, selection, self.mode, verbose=False ) + maps_out_path = self.maps_path + performance_dir = path.join( - self.maps_path, + maps_out_path, f"{self.split_name}-{split}", f"best-{selection}", data_group, @@ -1724,8 +2153,10 @@ def _mode_to_image_tsv( ) sub_df.rename(columns={f"{self.mode}_id": "image_id"}, inplace=True) + maps_out_path = self.maps_path + performance_dir = path.join( - self.maps_path, + maps_out_path, f"{self.split_name}-{split}", f"best-{selection}", data_group, @@ -1819,7 +2250,14 @@ def _init_model( ) transfer_class = getattr(network_package, transfer_maps.architecture) logger.debug(f"Transfer from {transfer_class}") - model.transfer_weights(transfer_state["model"], transfer_class) + if "model" in transfer_state.keys(): + model.transfer_weights(transfer_state["model"], transfer_class) + elif "model_state_dict" in transfer_state.keys(): + model.transfer_weights( + transfer_state["model_state_dict"], transfer_class + ) + else: + raise KeyError("Unknow key in model state dictionnary.") return model, current_epoch @@ -1904,7 +2342,6 @@ def _print_description_log( log_path = path.join(log_dir, "description.log") with open(log_path, "r") as f: content = f.read() - print(content) def get_group_info( self, data_group: str, split: int = None @@ -2007,6 +2444,13 @@ def get_state_dict( f"best-{selection_metric}", "model.pth.tar", ) + if not path.exists(model_path): + model_path = path.join( + self.maps_path, + f"{self.split_name}-{split}", + f"best-{selection_metric}", + "model.pt", + ) logger.info( f"Loading model trained for split {split} " diff --git a/clinicadl/utils/maps_manager/maps_manager_utils.py b/clinicadl/utils/maps_manager/maps_manager_utils.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/meta_maps/__init__.py b/clinicadl/utils/meta_maps/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/meta_maps/getter.py b/clinicadl/utils/meta_maps/getter.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/metric_module.py b/clinicadl/utils/metric_module.py old mode 100644 new mode 100755 index 93b812f5c..bc42e1482 --- a/clinicadl/utils/metric_module.py +++ b/clinicadl/utils/metric_module.py @@ -14,6 +14,8 @@ "BA": "max", "PSNR": "max", "SSIM": "max", + "MSSSIM": "max", + "LNCC": "max", "loss": "min", } @@ -227,9 +229,22 @@ def ssim_fn(y, y_pred): Returns: (float) SSIM """ - from skimage.metrics import structural_similarity + from clinicadl.utils.pytorch_ssim import ssim3D - return structural_similarity(y, y_pred) + return ssim3D(y, y_pred).item() + + @staticmethod + def msssim_fn(y, y_pred): + """ + Args: + y (List): list of labels + y_pred (List): list of predictions + Returns: + (float) MS-SSIM + """ + from clinicadl.utils.pytorch_msssim import MSSSIM + + return MSSSIM()(y, y_pred).item() @staticmethod def psnr_fn(y, y_pred): @@ -244,6 +259,35 @@ def psnr_fn(y, y_pred): return peak_signal_noise_ratio(y, y_pred) + @staticmethod + def lncc_fn(y, y_pred): + """ + Args: + y (List): list of labels + y_pred (List): list of predictions + Returns: + (float) LNCC + """ + from scipy.ndimage import gaussian_filter + + sigma = 2 + + mean1 = gaussian_filter(y, sigma) + mean2 = gaussian_filter(y_pred, sigma) + + mean12 = gaussian_filter( + y * y_pred, sigma + ) # the * operator is term by term product + mean11 = gaussian_filter(y * y, sigma) + mean22 = gaussian_filter(y_pred * y_pred, sigma) + + covar12 = mean12 - (mean1 * mean2) + var1 = np.sqrt(mean11 - (mean1 * mean1)) + var2 = np.sqrt(mean22 - (mean2 * mean2)) + + lcc_matrix = np.maximum(covar12 / (var1 * var2), 0) + return np.mean(lcc_matrix) + class RetainBest: """ diff --git a/clinicadl/utils/network/__init__.py b/clinicadl/utils/network/__init__.py old mode 100644 new mode 100755 index bf155524d..54cf809e7 --- a/clinicadl/utils/network/__init__.py +++ b/clinicadl/utils/network/__init__.py @@ -1,9 +1,40 @@ -from .autoencoder.models import AE_Conv4_FC3, AE_Conv5_FC3 +from .autoencoder.models import AE_Conv4_FC3, AE_Conv5_FC3, CAE_half from .cnn.models import Conv4_FC3, Conv5_FC3, Stride_Conv5_FC3, resnet18 from .cnn.random import RandomArchitecture +from .unet.unet import Unet_recon +from .vae.advanced_CVAE import CVAE_3D_final_conv +from .vae.convolutional_VAE import CVAE_3D, CVAE_3D_half from .vae.vanilla_vae import ( Vanilla3DdenseVAE, - Vanilla3DVAE, + Vanilla3DspacialVAE, VanillaDenseVAE, VanillaSpatialVAE, ) +from .pythae import ( + pythae_VAE, + pythae_BetaVAE, + pythae_VAE_LinNF, + pythae_VAE_IAF, + pythae_DisentangledBetaVAE, + pythae_FactorVAE, + pythae_BetaTCVAE, + pythae_MSSSIM_VAE, + pythae_INFOVAE_MMD, + pythae_SVAE, + pythae_PoincareVAE, + pythae_Adversarial_AE, + pythae_VAEGAN, + pythae_VQVAE, + pythae_HVAE, + pythae_RHVAE, + pythae_IWAE, + pythae_CIWAE, + pythae_PIWAE, + pythae_MIWAE, + pythae_VAMP, + pythae_WAE_MMD, + pythae_AE, + pythae_RAE_L2, + pythae_RAE_GP, + ResNet18_VAE, +) diff --git a/clinicadl/utils/network/autoencoder/__init__.py b/clinicadl/utils/network/autoencoder/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/autoencoder/cnn_transformer.py b/clinicadl/utils/network/autoencoder/cnn_transformer.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/autoencoder/models.py b/clinicadl/utils/network/autoencoder/models.py old mode 100644 new mode 100755 index 14b6a272a..9e38445e1 --- a/clinicadl/utils/network/autoencoder/models.py +++ b/clinicadl/utils/network/autoencoder/models.py @@ -1,7 +1,17 @@ +from torch import nn + from clinicadl.utils.network.autoencoder.cnn_transformer import CNN_Transformer from clinicadl.utils.network.cnn.models import Conv4_FC3, Conv5_FC3, resnet18 from clinicadl.utils.network.sub_network import AutoEncoder +from clinicadl.utils.network.vae.vae_layers import ( + DecoderUpsample3DLayer, + EncoderConv3DLayer, + Flatten, + Unflatten3D, +) +DecoderLayer3D = DecoderUpsample3DLayer +EncoderLayer3D = EncoderConv3DLayer class AE_Conv5_FC3(AutoEncoder): """ @@ -55,3 +65,30 @@ def get_dimension(): @staticmethod def get_task(): return ["reconstruction"] + + +class CAE_half(AutoEncoder): + """ + 3D Autoencoder derived from CVAE + """ + + def __init__(self, input_size, latent_space_size, gpu=True): + # fmt: off + self.encoder = nn.Sequential( + EncoderLayer3D(1, 32, kernel_size=3), + EncoderLayer3D(32, 64, kernel_size=3), + EncoderLayer3D(64, 128, kernel_size=3), + Flatten(), + nn.Linear(153600, latent_space_size) + ) + self.decoder = nn.Sequential( + nn.Linear(latent_space_size, 307200), + Unflatten3D(256, 10, 12, 10), + DecoderLayer3D(256, 128, kernel_size=3), + DecoderLayer3D(128, 64, kernel_size=3), + DecoderLayer3D(64, 1, kernel_size=3) + ) + # fmt: on + super(CAE_half, self).__init__( + encoder=self.encoder, decoder=self.decoder, gpu=gpu + ) diff --git a/clinicadl/utils/network/cnn/__init__.py b/clinicadl/utils/network/cnn/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/cnn/models.py b/clinicadl/utils/network/cnn/models.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/cnn/random.py b/clinicadl/utils/network/cnn/random.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/cnn/resnet.py b/clinicadl/utils/network/cnn/resnet.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/network.py b/clinicadl/utils/network/network.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/network_utils.py b/clinicadl/utils/network/network_utils.py old mode 100644 new mode 100755 index dba5a14e7..379cf3ec2 --- a/clinicadl/utils/network/network_utils.py +++ b/clinicadl/utils/network/network_utils.py @@ -123,3 +123,35 @@ def forward(self, f_maps, indices, padding=None): output = output[:, :, x1::, y1::] return output + + +def torch_summarize(model, show_weights=True, show_parameters=True): + """Summarizes torch model by showing trainable parameters and weights.""" + import numpy as np + import torch + from torch.nn.modules.module import _addindent + + tmpstr = model.__class__.__name__ + " (\n" + for key, module in model._modules.items(): + # if it contains layers let call it recursively to get params and weights + if type(module) in [ + torch.nn.modules.container.Container, + torch.nn.modules.container.Sequential, + ]: + modstr = torch_summarize(module) + else: + modstr = module.__repr__() + modstr = _addindent(modstr, 2) + + params = sum([np.prod(p.size()) for p in module.parameters()]) + weights = tuple([tuple(p.size()) for p in module.parameters()]) + + tmpstr += " (" + key + "): " + modstr + if show_weights: + tmpstr += ", weights={}".format(weights) + if show_parameters: + tmpstr += ", parameters={}".format(params) + tmpstr += "\n" + + tmpstr = tmpstr + ")" + return tmpstr diff --git a/clinicadl/utils/network/pythae/__init__.py b/clinicadl/utils/network/pythae/__init__.py new file mode 100644 index 000000000..dd40d245d --- /dev/null +++ b/clinicadl/utils/network/pythae/__init__.py @@ -0,0 +1,26 @@ +from .models.vae import pythae_VAE +from .models.beta_vae import pythae_BetaVAE +from .models.vae_lin_nf import pythae_VAE_LinNF +from .models.vae_iaf import pythae_VAE_IAF +from .models.disantangled_beta_vae import pythae_DisentangledBetaVAE +from .models.factor_vae import pythae_FactorVAE +from .models.beta_tc_vae import pythae_BetaTCVAE +from .models.ms_ssim_vae import pythae_MSSSIM_VAE +from .models.info_vae import pythae_INFOVAE_MMD +from .models.svae import pythae_SVAE +from .models.pvae import pythae_PoincareVAE +from .models.adversarial_ae import pythae_Adversarial_AE +from .models.vaegan import pythae_VAEGAN +from .models.vqvae import pythae_VQVAE +from .models.hvae import pythae_HVAE +from .models.rhvae import pythae_RHVAE +from .models.importance_weighted_ae import pythae_IWAE +from .models.multiply_iwae import pythae_MIWAE +from .models.partially_iwae import pythae_PIWAE +from .models.combinaison_iwae import pythae_CIWAE +from .models.vamp_ae import pythae_VAMP +from .models.wasserstein_ae import pythae_WAE_MMD +from .models.ae import pythae_AE +from .models.rae_l2 import pythae_RAE_L2 +from .models.rae_gp import pythae_RAE_GP +from .models.resnet_vae import ResNet18_VAE diff --git a/clinicadl/utils/network/pythae/encoder_decoder_config.py b/clinicadl/utils/network/pythae/encoder_decoder_config.py new file mode 100644 index 000000000..db05d4bc4 --- /dev/null +++ b/clinicadl/utils/network/pythae/encoder_decoder_config.py @@ -0,0 +1,23 @@ +from pydantic import BaseModel + + +class Encoder_Decoder_Config(BaseModel): + input_size: list + first_layer_channels: int = 32 + n_block_encoder: int = 5 + feature_size: int = 0 + latent_space_size: int = 256 + n_block_decoder: int = 5 + last_layer_channels: int = 32 + last_layer_conv: bool = False + n_layer_per_block_encoder: int = 1 + n_layer_per_block_decoder: int = 1 + block_type: str = "conv" + + +def make_encoder_decoder_config(parameters): + parameters["encoder_decoder_config"] = Encoder_Decoder_Config(**parameters) + for key in parameters["encoder_decoder_config"].__fields__.keys(): + if key != "input_size": + del parameters[key] + return parameters diff --git a/clinicadl/utils/network/pythae/models/adversarial_ae.py b/clinicadl/utils/network/pythae/models/adversarial_ae.py new file mode 100644 index 000000000..85b7a3be3 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/adversarial_ae.py @@ -0,0 +1,39 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_Adversarial_AE(BasePythae): + def __init__( + self, + encoder_decoder_config, + adversarial_loss_scale, + gpu=False, + ): + from pythae.models import Adversarial_AE, Adversarial_AE_Config + + encoder, decoder = super(pythae_Adversarial_AE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = Adversarial_AE_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + adversarial_loss_scale=adversarial_loss_scale, + ) + self.model = Adversarial_AE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import AdversarialTrainerConfig + + return AdversarialTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/ae.py b/clinicadl/utils/network/pythae/models/ae.py new file mode 100644 index 000000000..a49ae8ccb --- /dev/null +++ b/clinicadl/utils/network/pythae/models/ae.py @@ -0,0 +1,38 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_AE(BasePythae): + def __init__( + self, + encoder_decoder_config, + gpu=False, + ): + from pythae.models import AE, AEConfig + + encoder, decoder = super(pythae_AE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + is_ae=True, + ) + + model_config = AEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + ) + self.model = AE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/beta_tc_vae.py b/clinicadl/utils/network/pythae/models/beta_tc_vae.py new file mode 100644 index 000000000..596a808c6 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/beta_tc_vae.py @@ -0,0 +1,43 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_BetaTCVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + beta, + alpha, + gamma, + gpu=False, + ): + + from pythae.models import BetaTCVAE, BetaTCVAEConfig + + encoder, decoder = super(pythae_BetaTCVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = BetaTCVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + beta=beta, + alpha=alpha, + gamma=gamma, + ) + self.model = BetaTCVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/beta_vae.py b/clinicadl/utils/network/pythae/models/beta_vae.py new file mode 100644 index 000000000..79913e4f2 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/beta_vae.py @@ -0,0 +1,37 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_BetaVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + beta, + gpu=False, + ): + from pythae.models import BetaVAE, BetaVAEConfig + + encoder, decoder = super(pythae_BetaVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = BetaVAEConfig( + input_dim=self.input_size, latent_dim=self.latent_space_size, beta=beta + ) + self.model = BetaVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/combinaison_iwae.py b/clinicadl/utils/network/pythae/models/combinaison_iwae.py new file mode 100644 index 000000000..d1e730569 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/combinaison_iwae.py @@ -0,0 +1,41 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_CIWAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + beta, + number_samples, + gpu=False, + ): + + from pythae.models import CIWAE, CIWAEConfig + + encoder, decoder = super(pythae_CIWAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = CIWAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + beta=beta, + number_samples=number_samples, + ) + self.model = CIWAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/disantangled_beta_vae.py b/clinicadl/utils/network/pythae/models/disantangled_beta_vae.py new file mode 100644 index 000000000..8c198bdc1 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/disantangled_beta_vae.py @@ -0,0 +1,43 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_DisentangledBetaVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + beta, + C, + warmup_epoch, + gpu=False, + ): + + from pythae.models import DisentangledBetaVAE, DisentangledBetaVAEConfig + + encoder, decoder = super(pythae_DisentangledBetaVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = DisentangledBetaVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + beta=beta, + C=C, + warmup_epoch=warmup_epoch, + ) + self.model = DisentangledBetaVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/factor_vae.py b/clinicadl/utils/network/pythae/models/factor_vae.py new file mode 100644 index 000000000..ce0ef33bb --- /dev/null +++ b/clinicadl/utils/network/pythae/models/factor_vae.py @@ -0,0 +1,39 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_FactorVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + gamma, + gpu=False, + ): + + from pythae.models import FactorVAE, FactorVAEConfig + + encoder, decoder = super(pythae_FactorVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = FactorVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + gamma=gamma, + ) + self.model = FactorVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import AdversarialTrainerConfig + return AdversarialTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/hvae.py b/clinicadl/utils/network/pythae/models/hvae.py new file mode 100644 index 000000000..293ef27dd --- /dev/null +++ b/clinicadl/utils/network/pythae/models/hvae.py @@ -0,0 +1,43 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_HVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + n_lf, + eps_lf, + beta_zero, + gpu=False, + ): + + from pythae.models import HVAE, HVAEConfig + + encoder, decoder = super(pythae_HVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = HVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + n_lf=n_lf, + eps_lf=eps_lf, + beta_zero=beta_zero, + ) + self.model = HVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/importance_weighted_ae.py b/clinicadl/utils/network/pythae/models/importance_weighted_ae.py new file mode 100644 index 000000000..ace3c44d9 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/importance_weighted_ae.py @@ -0,0 +1,39 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_IWAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + number_samples, + gpu=False, + ): + + from pythae.models import IWAE, IWAEConfig + + encoder, decoder = super(pythae_IWAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = IWAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + number_samples=number_samples, + ) + self.model = IWAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/info_vae.py b/clinicadl/utils/network/pythae/models/info_vae.py new file mode 100644 index 000000000..80899e49f --- /dev/null +++ b/clinicadl/utils/network/pythae/models/info_vae.py @@ -0,0 +1,45 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_INFOVAE_MMD(BasePythae): + def __init__( + self, + encoder_decoder_config, + kernel_choice, + alpha, + lbd, + kernel_bandwidth, + gpu=False, + ): + + from pythae.models import INFOVAE_MMD, INFOVAE_MMD_Config + + encoder, decoder = super(pythae_INFOVAE_MMD, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = INFOVAE_MMD_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + kernel_choice=kernel_choice, + alpha=alpha, + lbd=lbd, + kernel_bandwidth=kernel_bandwidth, + ) + self.model = INFOVAE_MMD( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/ms_ssim_vae.py b/clinicadl/utils/network/pythae/models/ms_ssim_vae.py new file mode 100644 index 000000000..1c38e1464 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/ms_ssim_vae.py @@ -0,0 +1,41 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_MSSSIM_VAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + beta, + window_size, + gpu=False, + ): + + from pythae.models import MSSSIM_VAE, MSSSIM_VAEConfig + + encoder, decoder = super(pythae_MSSSIM_VAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = MSSSIM_VAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + beta=beta, + window_size=window_size, + ) + self.model = MSSSIM_VAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/multiply_iwae.py b/clinicadl/utils/network/pythae/models/multiply_iwae.py new file mode 100644 index 000000000..1a6c667c1 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/multiply_iwae.py @@ -0,0 +1,41 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_MIWAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + number_gradient_estimates, + number_samples, + gpu=False, + ): + + from pythae.models import MIWAE, MIWAEConfig + + encoder, decoder = super(pythae_MIWAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = MIWAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + number_gradient_estimates=number_gradient_estimates, + number_samples=number_samples + ) + self.model = MIWAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/partially_iwae.py b/clinicadl/utils/network/pythae/models/partially_iwae.py new file mode 100644 index 000000000..15acd0ec4 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/partially_iwae.py @@ -0,0 +1,41 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_PIWAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + number_gradient_estimates, + number_samples, + gpu=False, + ): + + from pythae.models import PIWAE, PIWAEConfig + + encoder, decoder = super(pythae_PIWAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = PIWAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + number_gradient_estimates=number_gradient_estimates, + number_samples=number_samples + ) + self.model = PIWAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import CoupledOptimizerTrainerConfig + return CoupledOptimizerTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/pvae.py b/clinicadl/utils/network/pythae/models/pvae.py new file mode 100644 index 000000000..b98de5c20 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/pvae.py @@ -0,0 +1,45 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_PoincareVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + reconstruction_loss, + prior_distribution, + posterior_distribution, + curvature, + gpu=False, + ): + + from pythae.models import PoincareVAE, PoincareVAEConfig + + encoder, decoder = super(pythae_PoincareVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = PoincareVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + reconstruction_loss=reconstruction_loss, + prior_distribution=prior_distribution, + posterior_distribution=posterior_distribution, + curvature=curvature, + ) + self.model = PoincareVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/rae_gp.py b/clinicadl/utils/network/pythae/models/rae_gp.py new file mode 100644 index 000000000..ff1b03dae --- /dev/null +++ b/clinicadl/utils/network/pythae/models/rae_gp.py @@ -0,0 +1,42 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_RAE_GP(BasePythae): + def __init__( + self, + encoder_decoder_config, + embedding_weight, + reg_weight, + gpu=False, + ): + + from pythae.models import RAE_GP, RAE_GP_Config + + encoder, decoder = super(pythae_RAE_GP, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + is_ae=True, + ) + + model_config = RAE_GP_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + embedding_weight=embedding_weight, + reg_weight=reg_weight, + ) + self.model = RAE_GP( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/rae_l2.py b/clinicadl/utils/network/pythae/models/rae_l2.py new file mode 100644 index 000000000..5d322a0b6 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/rae_l2.py @@ -0,0 +1,42 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_RAE_L2(BasePythae): + def __init__( + self, + encoder_decoder_config, + embedding_weight, + reg_weight, + gpu=False, + ): + + from pythae.models import RAE_L2, RAE_L2_Config + + encoder, decoder = super(pythae_RAE_L2, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + is_ae=True, + ) + + model_config = RAE_L2_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + embedding_weight=embedding_weight, + reg_weight=reg_weight, + ) + self.model = RAE_L2( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import CoupledOptimizerTrainerConfig + return CoupledOptimizerTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) \ No newline at end of file diff --git a/clinicadl/utils/network/pythae/models/resnet_vae.py b/clinicadl/utils/network/pythae/models/resnet_vae.py new file mode 100644 index 000000000..c06495bbc --- /dev/null +++ b/clinicadl/utils/network/pythae/models/resnet_vae.py @@ -0,0 +1,358 @@ +# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py +# and https://github.com/julianstastny/VAE-ResNet18-PyTorch/tree/master + +from typing import List, Tuple + +import torch +import torch.nn.functional as F +from clinicadl.utils.network.pythae.pythae_utils import BasePythae +from clinicadl.utils.network.vae.vae_layers import Flatten, Unflatten3D +from pythae.models.base.base_utils import ModelOutput +from pythae.models.nn import BaseEncoder, BaseDecoder +from torch import nn + + +class ResNet18_VAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + gpu=False, + ): + from pythae.models import VAE, VAEConfig + + encoder, decoder = super(ResNet18_VAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + encoder = ResNet18Enc( + latent_space_size=latent_space_size, input_size=input_size + ) + decoder = ResNet18Dec( + latent_space_size=latent_space_size, output_size=input_size + ) + + # encoder = Encoder_VAE(encoder_layers, mu_layer, logvar_layer) + # decoder = Decoder(decoder_layers) + + model_config = VAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + uses_default_encoder=False, + uses_default_decoder=False, + ) + + self.model = VAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + # amp=True, + ) + + +def conv3x3x3(input_channels: int, output_channels: int, stride: int = 1) -> nn.Conv3d: + """3x3x3 convolution with padding""" + return nn.Conv3d( + input_channels, + output_channels, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + ) + + +def conv1x1x1(input_channels: int, output_channels: int, stride: int = 1) -> nn.Conv3d: + """1x1x1 convolution""" + return nn.Conv3d( + input_channels, output_channels, kernel_size=1, stride=stride, bias=False + ) + + +class BasicBlockEnc(nn.Module): + def __init__(self, input_channels: int, stride: int = 1): + super(BasicBlockEnc, self).__init__() + + output_channels = input_channels * stride + + self.conv1 = conv3x3x3( + input_channels, + output_channels, + stride=stride, + ) + self.bn1 = nn.BatchNorm3d(output_channels) + self.relu = nn.ReLU(inplace=True) + + self.conv2 = conv3x3x3(output_channels, output_channels, stride=1) + self.bn2 = nn.BatchNorm3d(output_channels) + + if stride == 1: + self.shortcut = nn.Sequential() + else: + self.shortcut = nn.Sequential( + conv1x1x1(input_channels, output_channels, stride=stride), + nn.BatchNorm3d(output_channels), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + # print("After (layer x (y) 1st conv)", out.shape) + + out = self.conv2(out) + out = self.bn2(out) + # print("After (layer x (y) 2nd conv)", out.shape) + + out += self.shortcut(x) + out = self.relu(out) + # print("After (layer x (y) shortcut)", out.shape) + + return out + + +class BasicBlockDec(nn.Module): + def __init__(self, input_channels: int, up_size: List[int], stride: int = 1): + super(BasicBlockDec, self).__init__() + + output_channels = input_channels // stride + + self.conv2 = conv3x3x3( + input_channels, + input_channels, + stride=1, + ) + self.bn2 = nn.BatchNorm3d(input_channels) + + self.bn1 = nn.BatchNorm3d(output_channels) + + if stride == 1: + self.conv1 = conv3x3x3( + input_channels, + output_channels, + stride=1, + ) + self.shortcut = nn.Sequential() + else: + self.conv1 = nn.Sequential( + nn.Upsample(size=up_size, mode="nearest"), + conv3x3x3( + input_channels, + output_channels, + stride=1, + ), + ) + self.shortcut = nn.Sequential( + nn.Upsample(size=up_size, mode="nearest"), + conv3x3x3( + input_channels, + output_channels, + stride=1, + ), + nn.BatchNorm3d(output_channels), + ) + + def forward(self, x): + out = F.relu(self.bn2(self.conv2(x))) + # print("After layer (x (y) 1st conv)", out.shape) + out = self.bn1(self.conv1(out)) + # print("After layer (x (y) 2nd conv)", out.shape) + out += self.shortcut(x) + # print("After layer (x (y) shortcut)", out.shape) + out = F.relu(out) + return out + + +class ResNet18Enc(BaseEncoder): + def __init__( + self, + input_size: Tuple[int], + latent_space_size: int, + num_blocks: List[int] = [2, 2, 2, 2], + ): + super(ResNet18Enc, self).__init__() + + input_c = input_size[0] + input_d = input_size[1] + input_h = input_size[2] + input_w = input_size[3] + + self.input_channels = 64 + self.latent_space_size = latent_space_size + + self.layer1 = nn.Sequential( + nn.Conv3d( + input_c, + self.input_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ), + nn.BatchNorm3d(self.input_channels), + nn.ReLU(inplace=True), + ) + self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1) + + self.layer2 = self._make_layer(64, num_blocks[0], stride=1) + self.layer3 = self._make_layer(128, num_blocks[1], stride=2) + self.layer4 = self._make_layer(256, num_blocks[2], stride=2) + self.layer5 = self._make_layer(512, num_blocks[3], stride=2) + + self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1)) + self.flatten = Flatten() + + # n_pix_encoder = 512 * 6 * 7 * 6 + n_pix_encoder = 512 + feature_size = n_pix_encoder + + self.mu_layer = nn.Linear(feature_size, self.latent_space_size) + self.logvar_layer = nn.Linear(feature_size, self.latent_space_size) + + def _make_layer(self, output_channels: int, num_blocks: int, stride: int): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers += [BasicBlockEnc(self.input_channels, stride)] + self.input_channels = output_channels + return nn.Sequential(*layers) + + def forward(self, x: torch.torch.Tensor) -> ModelOutput: + # print("ResNet18Enc") + + x = self.layer1(x) + # print("After (layer 1)", x.shape) + + x = self.maxpool(x) + # print("After (maxpool)", x.shape) + + x = self.layer2(x) + # print("After (layer 2)", x.shape) + + x = self.layer3(x) + # print("After (layer 3)", x.shape) + + x = self.layer4(x) + # print("After (layer 4)", x.shape) + + x = self.layer5(x) + # print("After (layer 5)", x.shape) + + x = self.avgpool(x) + # print("After (avgpool)", x.shape) + x = self.flatten(x) + # print("After (flatten)", x.shape) + + mu = self.mu_layer(x) + # print("After mu", mu.shape) + logvar = self.logvar_layer(x) + # print("After logvar", logvar.shape) + + output = ModelOutput( + embedding=mu, + log_covariance=logvar, + ) + return output + + +class ResNet18Dec(BaseDecoder): + def __init__( + self, + output_size: Tuple[int], + latent_space_size: int, + num_blocks: List[int] = [2, 2, 2, 2], + ): + super(ResNet18Dec, self).__init__() + + self.input_channels = 512 + + # n_pix_decoder = 512 * 6 * 7 * 6 + n_pix_decoder = 512 + feature_size = n_pix_decoder + + self.linear = nn.Sequential( + nn.Linear(latent_space_size, feature_size), + nn.ReLU(inplace=True), + ) + + self.unflatten = Unflatten3D(512, 1, 1, 1) + + self.upsample = nn.Upsample(size=[6, 7, 6], mode="nearest") + + self.layer5 = self._make_layer( + 256, num_blocks[3], stride=2, up_size=[11, 13, 12] + ) + self.layer4 = self._make_layer( + 128, num_blocks[2], stride=2, up_size=[22, 26, 23] + ) + self.layer3 = self._make_layer( + 64, num_blocks[1], stride=2, up_size=[43, 52, 45] + ) + self.layer2 = self._make_layer( + 64, num_blocks[0], stride=1, up_size=[85, 104, 90] + ) + + self.layer1 = nn.Sequential( + nn.Upsample(size=[169, 208, 179], mode="nearest"), + conv3x3x3(64, output_size[0], 1), + nn.Sigmoid(), + ) + + def _make_layer( + self, + output_channels: int, + num_blocks: int, + stride: int, + up_size: List[int], + ): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in reversed(strides): + layers += [ + BasicBlockDec(self.input_channels, up_size=up_size, stride=stride) + ] + self.input_channels = output_channels + return nn.Sequential(*layers) + + def forward(self, z: torch.Tensor) -> ModelOutput: + # print("ResNet18Dec") + + # print("Latent vector", z.shape) + x = self.linear(z) + # print("After (linear)", x.shape) + + x = self.unflatten(x) + # print("After (unflatten)", x.shape) + x = self.upsample(x) + # print("After (upsample)", x.shape) + + out = self.layer5(x) + print("After (layer 5)", out.shape) + + out = self.layer4(out) + # print("After (layer 4)", out.shape) + + out = self.layer3(out) + # print("After (layer 3)", out.shape) + + out = self.layer2(out) + # print("After (layer 2)", out.shape) + + out = self.layer1(out) + # print("After (layer 1)", out.shape) + + output = ModelOutput(reconstruction=out) + + return output + diff --git a/clinicadl/utils/network/pythae/models/rhvae.py b/clinicadl/utils/network/pythae/models/rhvae.py new file mode 100644 index 000000000..0660a0205 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/rhvae.py @@ -0,0 +1,47 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_RHVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + n_lf, + eps_lf, + beta_zero, + temperature, + regularization, + gpu=False, + ): + + from pythae.models import RHVAE, RHVAEConfig + + encoder, decoder = super(pythae_RHVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = RHVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + n_lf=n_lf, + eps_lf=eps_lf, + beta_zero=beta_zero, + temperature=temperature, + regularization=regularization, + ) + self.model = RHVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/svae.py b/clinicadl/utils/network/pythae/models/svae.py new file mode 100644 index 000000000..31e4a176a --- /dev/null +++ b/clinicadl/utils/network/pythae/models/svae.py @@ -0,0 +1,141 @@ +from clinicadl.utils.network.vae.vae_layers import ( + Flatten, +) +from clinicadl.utils.network.vae.vae_blocks import EncoderBlock + +from clinicadl.utils.network.pythae.pythae_utils import BasePythae +from pythae.models.nn import BaseEncoder, BaseDecoder +from pythae.models.base.base_utils import ModelOutput + +import torch +from torch import nn + + +class pythae_SVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + gpu=False, + ): + + from pythae.models import SVAE, SVAEConfig + + _, decoder = super(pythae_SVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + self.svae_encoder = build_SVAE_encoder( + encoder_decoder_config=encoder_decoder_config + ) + + encoder = Encoder( + self.svae_encoder.encoder, + self.svae_encoder.mu_layer, + self.svae_encoder.log_concentration_layer + ) + + model_config = SVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size + ) + self.model = SVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) + + +class build_SVAE_encoder(): + + def __init__(self, encoder_decoder_config): + + self.input_size = encoder_decoder_config.input_size + self.first_layer_channels = encoder_decoder_config.first_layer_channels + self.n_block_encoder = encoder_decoder_config.n_block_encoder + self.feature_size = encoder_decoder_config.feature_size + self.latent_space_size = encoder_decoder_config.latent_space_size + self.n_layer_per_block_encoder = encoder_decoder_config.n_layer_per_block_encoder + self.block_type = encoder_decoder_config.block_type + + self.build_encoder() + + def build_encoder(self): + input_c = self.input_size[0] + input_d = self.input_size[1] + input_h = self.input_size[2] + input_w = self.input_size[3] + + # ENCODER + encoder_layers = [] + + # Input Layer + encoder_layers.append( + EncoderBlock(input_c, self.first_layer_channels, self.n_layer_per_block_encoder, self.block_type,) + ) + + # Conv Layers + for i in range(self.n_block_encoder-1): + encoder_layers.append( + EncoderBlock( + self.first_layer_channels * 2**i, + self.first_layer_channels * 2**(i+1), + self.n_layer_per_block_encoder, + self.block_type, + ) + ) + # Construct output paddings + + enc_feature_c = self.first_layer_channels * 2 ** (self.n_block_encoder - 1) + enc_feature_d = input_d // (2**self.n_block_encoder) + enc_feature_h = input_h // (2**self.n_block_encoder) + enc_feature_w = input_w // (2**self.n_block_encoder) + + # Compute size of the feature space + n_pix_encoder = enc_feature_c * enc_feature_d * enc_feature_h * enc_feature_w + + # Flatten + encoder_layers.append(Flatten()) + # Intermediate feature space + if self.feature_size == 0: + feature_space = n_pix_encoder + else: + feature_space = feature_size + encoder_layers.append( + nn.Sequential(nn.Linear(n_pix_encoder, feature_space), nn.ReLU()) + ) + + self.encoder = nn.Sequential(*encoder_layers) + + # LATENT SPACE + self.mu_layer = nn.Linear(feature_space, self.latent_space_size) + self.log_concentration_layer = nn.Linear(feature_space, 1) + + +class Encoder(BaseEncoder): + def __init__(self, encoder_layers, mu_layer, logc_layer): # Args is a ModelConfig instance + BaseEncoder.__init__(self) + + self.layers = encoder_layers + self.mu_layer = mu_layer + self.log_concentration = logc_layer + + def forward(self, x:torch.Tensor) -> ModelOutput: + h = self.layers(x) + mu, log_concentration = self.mu_layer(h), self.log_concentration(h) + output = ModelOutput( + embedding=mu, # Set the output from the encoder in a ModelOutput instance + log_concentration=log_concentration + ) + return output diff --git a/clinicadl/utils/network/pythae/models/vae.py b/clinicadl/utils/network/pythae/models/vae.py new file mode 100644 index 000000000..456135164 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vae.py @@ -0,0 +1,37 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_VAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + gpu=False, + ): + from pythae.models import VAE, VAEConfig + + encoder, decoder = super(pythae_VAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = VAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + ) + self.model = VAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/vae_iaf.py b/clinicadl/utils/network/pythae/models/vae_iaf.py new file mode 100644 index 000000000..413f29a4d --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vae_iaf.py @@ -0,0 +1,43 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_VAE_IAF(BasePythae): + def __init__( + self, + encoder_decoder_config, + n_made_blocks, + n_hidden_in_made, + hidden_size, + gpu=False, + ): + + from pythae.models import VAE_IAF, VAE_IAF_Config + + encoder, decoder = super(pythae_VAE_IAF, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = VAE_IAF_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + n_made_blocks=n_made_blocks, + n_hidden_in_made=n_hidden_in_made, + hidden_size=hidden_size, + ) + self.model = VAE_IAF( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/vae_lin_nf.py b/clinicadl/utils/network/pythae/models/vae_lin_nf.py new file mode 100644 index 000000000..32b7d6124 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vae_lin_nf.py @@ -0,0 +1,39 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_VAE_LinNF(BasePythae): + def __init__( + self, + encoder_decoder_config, + flows, + gpu=False, + ): + + from pythae.models import VAE_LinNF, VAE_LinNF_Config + + encoder, decoder = super(pythae_VAE_LinNF, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = VAE_LinNF_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + flows=flows, + ) + self.model = VAE_LinNF( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/vaegan.py b/clinicadl/utils/network/pythae/models/vaegan.py new file mode 100644 index 000000000..56f8d1b86 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vaegan.py @@ -0,0 +1,154 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + +from pythae.models.nn import BaseDecoder, BaseDiscriminator +from pythae.models.base.base_utils import ModelOutput + +from typing import List + +import torch +import torch.nn as nn + + +class pythae_VAEGAN(BasePythae): + def __init__( + self, + encoder_decoder_config, + adversarial_loss_scale, + reconstruction_layer, + margin, + equilibrium, + gpu=False, + ): + + from pythae.models import VAEGAN, VAEGANConfig + + encoder, decoder = super(pythae_VAEGAN, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + discriminator = Discriminator_VAEGAN(input_size=self.input_size) + + model_config = VAEGANConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + adversarial_loss_scale=adversarial_loss_scale, + reconstruction_layer=reconstruction_layer, + margin=margin, + equilibrium=equilibrium, + ) + self.model = VAEGAN( + model_config=model_config, + encoder=encoder, + decoder=decoder, + discriminator=discriminator, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import CoupledOptimizerAdversarialTrainerConfig + return CoupledOptimizerAdversarialTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) + + +class Discriminator_VAEGAN(BaseDiscriminator): + + def __init__(self, input_size): + + BaseDiscriminator.__init__(self) + + self.n_channels = 1 + + layers = nn.ModuleList() + + layers.append( + nn.Sequential( + nn.Conv3d(self.n_channels, 32, 4, 2, padding=1), + nn.BatchNorm3d(32), + nn.ReLU(), + ) + ) + + layers.append( + nn.Sequential( + nn.Conv3d(32, 64, 4, 2, padding=1), + nn.BatchNorm3d(64), + nn.ReLU(), + ) + ) + + layers.append( + nn.Sequential( + nn.Conv3d(64, 128, 4, 2, padding=1), + nn.BatchNorm3d(128), + nn.ReLU(), + ) + ) + + layers.append( + nn.Sequential( + nn.Conv3d(128, 256, 4, 2, padding=1), + nn.BatchNorm3d(256), + nn.ReLU(), + ) + ) + + n_pix = 256 * (input_size[1] // (2**4)) * (input_size[2] // (2**4)) * (input_size[3] // (2**4)) + layers.append( + nn.Sequential( + nn.Linear(n_pix, 512), + nn.BatchNorm1d(512), + nn.ReLU(), + ) + ) + layers.append( + nn.Sequential( + nn.Linear(512, 1), + nn.Sigmoid(), + ) + ) + + self.layers = layers + self.depth = len(layers) + + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): + output = ModelOutput() + + max_depth = self.depth + + if output_layer_levels is not None: + + assert all( + self.depth >= levels > 0 or levels == -1 + for levels in output_layer_levels + ), ( + f"Cannot output layer deeper than depth ({self.depth}). " + f"Got ({output_layer_levels})." + ) + + if -1 in output_layer_levels: + max_depth = self.depth + else: + max_depth = max(output_layer_levels) + + out = x + + for i in range(max_depth): + + if i == 4: + out = out.reshape(x.shape[0], -1) + + out = self.layers[i](out) + + if output_layer_levels is not None: + if i + 1 in output_layer_levels: + output[f"embedding_layer_{i+1}"] = out + if i + 1 == self.depth: + output["embedding"] = out + + return output diff --git a/clinicadl/utils/network/pythae/models/vamp_ae.py b/clinicadl/utils/network/pythae/models/vamp_ae.py new file mode 100644 index 000000000..3b596c12e --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vamp_ae.py @@ -0,0 +1,41 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_VAMP(BasePythae): + def __init__( + self, + encoder_decoder_config, + number_components, + linear_scheduling_steps, + gpu=False, + ): + + from pythae.models import VAMP, VAMPConfig + + encoder, decoder = super(pythae_VAMP, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + ) + + model_config = VAMPConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + number_components=number_components, + linear_scheduling_steps=linear_scheduling_steps, + ) + self.model = VAMP( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/models/vqvae.py b/clinicadl/utils/network/pythae/models/vqvae.py new file mode 100644 index 000000000..c8f7b8422 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/vqvae.py @@ -0,0 +1,140 @@ +from clinicadl.utils.network.vae.vae_layers import ( + EncoderConv3DLayer, + Flatten, +) + +from clinicadl.utils.network.pythae.pythae_utils import BasePythae +from pythae.models.nn import BaseEncoder +from pythae.models.base.base_utils import ModelOutput + +import torch +from torch import nn + +EncoderLayer3D = EncoderConv3DLayer + + +class pythae_VQVAE(BasePythae): + def __init__( + self, + encoder_decoder_config, + commitment_loss_factor, + quantization_loss_factor, + num_embeddings, + use_ema, + decay, + gpu=False, + ): + from pythae.models import VQVAE, VQVAEConfig + + encoder, decoder = super(pythae_VQVAE, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + is_ae=True, + ) + + # encoder_layers, emb_layer = build_VQVAE_encoder( + # input_size=input_size, + # latent_space_size=latent_space_size, + # feature_size=feature_size, + # n_conv=n_conv, + # io_layer_channels=io_layer_channels, + # ) + + # encoder = Encoder(encoder_layers, emb_layer) + + model_config = VQVAEConfig( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + commitment_loss_factor=commitment_loss_factor, + quantization_loss_factor=quantization_loss_factor, + num_embeddings=num_embeddings, + use_ema=use_ema, + decay=decay, + ) + self.model = VQVAE( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) + + +def build_VQVAE_encoder( + input_size=(1, 80, 96, 80), + latent_space_dim=16, + feature_size=0, + n_conv=3, + io_layer_channels=32, +): + first_layer_channels = io_layer_channels + last_layer_channels = io_layer_channels + + input_c = input_size[0] + input_d = input_size[1] + input_h = input_size[2] + input_w = input_size[3] + d, h, w = input_d, input_h, input_w + + # ENCODER + encoder_layers = [] + # Input Layer + encoder_layers.append(EncoderLayer3D(input_c, first_layer_channels)) + decoder_output_padding.append([d % 2, h % 2, w % 2]) + d, h, w = d // 2, h // 2, w // 2 + # Conv Layers + for i in range(n_conv - 1): + encoder_layers.append( + EncoderLayer3D( + first_layer_channels * 2**i, first_layer_channels * 2 ** (i + 1) + ) + ) + # # Compute size of the feature space + # n_pix = ( + # first_layer_channels + # * 2 ** (n_conv - 1) + # * (input_d // (2**n_conv)) + # * (input_h // (2**n_conv)) + # * (input_w // (2**n_conv)) + # ) + # # Flatten + # encoder_layers.append(Flatten()) + # # Intermediate feature space + # if feature_size == 0: + # feature_space = n_pix + # else: + # feature_space = feature_size + # encoder_layers.append( + # nn.Sequential(nn.Linear(n_pix, feature_space), nn.ReLU()) + # ) + # encoder = nn.Sequential(*encoder_layers) + + # LATENT SPACE + pre_qantized = nn.Conv3d( + first_layer_channels * 2 ** (n_conv + 1), latent_space_dim, 1, 1 + ) + + return encoder, pre_qantized + + +class Encoder(BaseEncoder): + def __init__(self, encoder_layers, pre_qantized): # Args is a ModelConfig instance + BaseEncoder.__init__(self) + + self.layers = encoder_layers + self.pre_qantized = pre_qantized + + def forward(self, x: torch.Tensor) -> ModelOutput: + out = self.layers(x) + output = ModelOutput(embedding=self.pre_qantized(out)) + return output diff --git a/clinicadl/utils/network/pythae/models/wasserstein_ae.py b/clinicadl/utils/network/pythae/models/wasserstein_ae.py new file mode 100644 index 000000000..7ae09be08 --- /dev/null +++ b/clinicadl/utils/network/pythae/models/wasserstein_ae.py @@ -0,0 +1,44 @@ +from clinicadl.utils.network.pythae.pythae_utils import BasePythae + + +class pythae_WAE_MMD(BasePythae): + def __init__( + self, + encoder_decoder_config, + kernel_choice, + reg_weight, + kernel_bandwidth, + gpu=False, + ): + + from pythae.models import WAE_MMD, WAE_MMD_Config + + encoder, decoder = super(pythae_WAE_MMD, self).__init__( + encoder_decoder_config = encoder_decoder_config, + gpu=gpu, + is_ae=True, + ) + + model_config = WAE_MMD_Config( + input_dim=self.input_size, + latent_dim=self.latent_space_size, + kernel_choice=kernel_choice, + reg_weight=reg_weight, + kernel_bandwidth=kernel_bandwidth, + ) + self.model = WAE_MMD( + model_config=model_config, + encoder=encoder, + decoder=decoder, + ) + + def get_trainer_config(self, output_dir, num_epochs, learning_rate, batch_size, optimizer): + from pythae.trainers import BaseTrainerConfig + return BaseTrainerConfig( + output_dir=output_dir, + num_epochs=num_epochs, + learning_rate=learning_rate, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + optimizer_cls=optimizer, + ) diff --git a/clinicadl/utils/network/pythae/pythae_utils.py b/clinicadl/utils/network/pythae/pythae_utils.py new file mode 100755 index 000000000..e95bdb3ba --- /dev/null +++ b/clinicadl/utils/network/pythae/pythae_utils.py @@ -0,0 +1,284 @@ +import abc +from collections import OrderedDict + +from pythae.models.nn import BaseEncoder, BaseDecoder +from pythae.models.base.base_utils import ModelOutput + +from clinicadl.utils.network.network import Network +from clinicadl.utils.network.vae.vae_layers import ( + Flatten, + Unflatten3D, +) +from clinicadl.utils.network.vae.vae_blocks import EncoderBlock, DecoderBlock + +import torch +from torch import nn + + +class BasePythae(Network): + def __init__( + self, + encoder_decoder_config, + gpu, + is_ae=False, + ): + super(BasePythae, self).__init__(gpu=gpu) + + self.input_size = encoder_decoder_config.input_size + self.latent_space_size = encoder_decoder_config.latent_space_size + + self.encoder_decoder = Encoder_Decoder(encoder_decoder_config) + + if is_ae: + encoder = Encoder_AE(self.encoder_decoder.encoder, self.encoder_decoder.mu_layer) + else: + encoder = Encoder_VAE( + self.encoder_decoder.encoder, + self.encoder_decoder.mu_layer, + self.encoder_decoder.var_layer + ) + decoder = Decoder(self.encoder_decoder.decoder) + + return encoder, decoder + + @abc.abstractmethod + def get_model(self, encoder, decoder): + pass + + @property + def layers(self): + return torch.nn.Sequential( + self.model.encoder, self.model.decoder + ) + + def compute_outputs_and_loss(self, input_dict, criterion, use_labels=False): + # x = input_dict["image"].to(self.device) + model_outputs = self.forward(input_dict) + loss_dict = { + "loss": model_outputs.loss, + } + for key in model_outputs.keys(): + if "loss" in key: + loss_dict[key] = model_outputs[key] + return model_outputs.recon_x, loss_dict + + # Network specific + def predict(self, x, sample_latent=0, seed=None): + if sample_latent == 0: + return self.model.predict(x.data) + else: + return self.model.predict(x.data, sample_latent=sample_latent, seed=seed) + + def forward(self, x): + return self.model.forward(x) + + def transfer_weights(self, state_dict, transfer_class): + self.model.load_state_dict(state_dict) + + # VAE specific + # def encode(self, x): + # encoder_output = self.encoder(x) + # mu, logVar = encoder_output.embedding, encoder_output.log_covariance + # z = self.reparameterize(mu, logVar) + # return mu, logVar, z + + # def decode(self, z): + # z = self.decoder(z) + # return z + + # def reparameterize(self, mu, logVar): + # #print(logVar.shape) + # std = torch.exp(0.5 * logVar) + # eps = torch.randn_like(std) + # return eps.mul(std).add_(mu) + + +class Encoder_Decoder(): + + def __init__(self, encoder_decoder_config): + + self.input_size = encoder_decoder_config.input_size + self.first_layer_channels = encoder_decoder_config.first_layer_channels + self.n_block_encoder = encoder_decoder_config.n_block_encoder + self.feature_size = encoder_decoder_config.feature_size + self.latent_space_size = encoder_decoder_config.latent_space_size + self.n_block_decoder = encoder_decoder_config.n_block_decoder + self.last_layer_channels = encoder_decoder_config.last_layer_channels + self.last_layer_conv = encoder_decoder_config.last_layer_conv + self.n_layer_per_block_encoder = encoder_decoder_config.n_layer_per_block_encoder + self.n_layer_per_block_decoder = encoder_decoder_config.n_layer_per_block_decoder + self.block_type = encoder_decoder_config.block_type + + self.build_encoder_decoder() + + def build_encoder_decoder(self): + input_c = self.input_size[0] + input_d = self.input_size[1] + input_h = self.input_size[2] + input_w = self.input_size[3] + + # ENCODER + encoder_layers = [] + + # Input Layer + encoder_layers.append( + EncoderBlock(input_c, self.first_layer_channels, self.n_layer_per_block_encoder, self.block_type,) + ) + + # Conv Layers + for i in range(self.n_block_encoder-1): + encoder_layers.append( + EncoderBlock( + self.first_layer_channels * 2**i, + self.first_layer_channels * 2**(i+1), + self.n_layer_per_block_encoder, + self.block_type, + ) + ) + # Construct output paddings + + enc_feature_c = self.first_layer_channels * 2 ** (self.n_block_encoder - 1) + enc_feature_d = input_d // (2**self.n_block_encoder) + enc_feature_h = input_h // (2**self.n_block_encoder) + enc_feature_w = input_w // (2**self.n_block_encoder) + + # Compute size of the feature space + n_pix_encoder = enc_feature_c * enc_feature_d * enc_feature_h * enc_feature_w + + # Flatten + encoder_layers.append(Flatten()) + # Intermediate feature space + if self.feature_size == 0: + feature_space = n_pix_encoder + else: + feature_space = feature_size + encoder_layers.append( + nn.Sequential(nn.Linear(n_pix_encoder, feature_space), nn.ReLU()) + ) + + self.encoder = nn.Sequential(*encoder_layers) + + # LATENT SPACE + self.mu_layer = nn.Linear(feature_space, self.latent_space_size) + self.var_layer = nn.Linear(feature_space, self.latent_space_size) + + # DECODER + + # automatically compute output padding and image size + d, h, w = input_d, input_h, input_w + decoder_output_padding = [] + decoder_input_size = [] + decoder_output_padding.append([d % 2, h % 2, w % 2]) + d, h, w = d // 2, h // 2, w // 2 + decoder_input_size.append([d, h, w]) + for i in range(self.n_block_decoder - 1): + decoder_output_padding.append([d % 2, h % 2, w % 2]) + d, h, w = d // 2, h // 2, w // 2 + decoder_input_size.append([d, h, w]) + + dec_feature_c = self.last_layer_channels * 2 ** (self.n_block_decoder - 1) + dec_feature_d = input_d // (2**self.n_block_decoder) + dec_feature_h = input_h // (2**self.n_block_decoder) + dec_feature_w = input_w // (2**self.n_block_decoder) + + n_pix_decoder = dec_feature_c * dec_feature_d * dec_feature_h * dec_feature_w + + decoder_layers = [] + # Intermediate feature space + if self.feature_size == 0: + decoder_layers.append( + nn.Sequential( + nn.Linear(self.latent_space_size, n_pix_decoder), + nn.ReLU(), + ) + ) + else: + decoder_layers.append( + nn.Sequential( + nn.Linear(self.latent_space_size, self.feature_size), + nn.ReLU(), + nn.Linear(self.feature_size, n_pix_decoder), + nn.ReLU(), + ) + ) + # Unflatten + decoder_layers.append( + Unflatten3D(dec_feature_c, dec_feature_d, dec_feature_h, dec_feature_w) + ) + + # Decoder layers + for i in range(self.n_block_decoder-1, 0, -1): + decoder_layers.append( + DecoderBlock( + self.last_layer_channels * 2 ** (i), + self.last_layer_channels * 2 ** (i-1), + decoder_input_size[i], + decoder_output_padding[i], + self.n_layer_per_block_decoder, + self.block_type, + ), + ) + + # Output layer + decoder_layers.append( + DecoderBlock( + self.last_layer_channels, + input_c, + decoder_input_size[0], + decoder_output_padding[0], + self.n_layer_per_block_decoder, + self.block_type, + is_last_block=True, + last_layer_conv=self.last_layer_conv, + ) + ) + + self.decoder = nn.Sequential(*decoder_layers) + + +class Encoder_VAE(BaseEncoder): + def __init__(self, encoder_layers, mu_layer, logvar_layer): # Args is a ModelConfig instance + BaseEncoder.__init__(self) + + self.layers = encoder_layers + self.mu_layer = mu_layer + self.logvar_layer = logvar_layer + + def forward(self, x: torch.Tensor) -> ModelOutput: + h = self.layers(x) + mu, logVar = self.mu_layer(h), self.logvar_layer(h) + output = ModelOutput( + embedding=mu, # Set the output from the encoder in a ModelOutput instance + log_covariance=logVar, + ) + return output + + +class Encoder_AE(BaseEncoder): + def __init__(self, encoder_layers, mu_layer): # Args is a ModelConfig instance + BaseEncoder.__init__(self) + + self.layers = encoder_layers + self.mu_layer = mu_layer + + def forward(self, x: torch.Tensor) -> ModelOutput: + h = self.layers(x) + embedding = self.mu_layer(h) + output = ModelOutput( + embedding=embedding, # Set the output from the encoder in a ModelOutput instance + ) + return output + + +class Decoder(BaseDecoder): + def __init__(self, decoder_layers): + BaseDecoder.__init__(self) + + self.layers = decoder_layers + + def forward(self, x: torch.Tensor) -> ModelOutput: + out = self.layers(x) + output = ModelOutput( + reconstruction=out # Set the output from the decoder in a ModelOutput instance + ) + return output \ No newline at end of file diff --git a/clinicadl/utils/network/sub_network.py b/clinicadl/utils/network/sub_network.py old mode 100644 new mode 100755 index 01452355f..8cca8a13d --- a/clinicadl/utils/network/sub_network.py +++ b/clinicadl/utils/network/sub_network.py @@ -82,7 +82,7 @@ def forward(self, x): def compute_outputs_and_loss(self, input_dict, criterion, use_labels=True): - images = input_dict["image"].to(self.device) + images = input_dict["data"].to(self.device) train_output = self.predict(images) loss = criterion(train_output, images) @@ -126,7 +126,7 @@ def predict(self, x): def compute_outputs_and_loss(self, input_dict, criterion, use_labels=True): - images, labels = input_dict["image"].to(self.device), input_dict["label"].to( + images, labels = input_dict["data"].to(self.device), input_dict["label"].to( self.device ) train_output = self.forward(images) diff --git a/clinicadl/utils/network/unet/__init__.py b/clinicadl/utils/network/unet/__init__.py new file mode 100755 index 000000000..e69de29bb diff --git a/clinicadl/utils/network/unet/unet.py b/clinicadl/utils/network/unet/unet.py new file mode 100755 index 000000000..921704d6c --- /dev/null +++ b/clinicadl/utils/network/unet/unet.py @@ -0,0 +1,156 @@ +import torch +from torch import nn + +from clinicadl.utils.network.network import Network +from clinicadl.utils.network.vae.vae_layers import Flatten, Unflatten3D + + +class Unet_recon(Network): + def __init__(self, gpu=True): + super(Unet_recon, self).__init__(gpu=gpu) + + self.down1 = UNetDown(1, 32).to(self.device) + self.down2 = UNetDown(32, 64).to(self.device) + self.down3 = UNetDown(64, 128).to(self.device) + self.down4 = UNetDown(128, 256).to(self.device) + self.down5 = UNetDown(256, 512).to(self.device) + + self.flatten = Flatten().to(self.device) + self.densedown = nn.Linear(76800, 256).to(self.device) + + self.denseup = nn.Linear(256, 76800).to(self.device) + self.unflatten = Unflatten3D(512, 5, 6, 5).to(self.device) + + self.up1 = UNetUp(512, 256, [10, 13, 11]).to(self.device) + self.up2 = UNetUp(512, 128, [21, 26, 22]).to(self.device) + self.up3 = UNetUp(256, 64, [42, 52, 44]).to(self.device) + self.up4 = UNetUp(128, 32, [84, 104, 89]).to(self.device) + + self.final = FinalLayer(64, 1).to(self.device) + + def forward(self, x): + d1 = self.down1(x) + d2 = self.down2(d1) + d3 = self.down3(d2) + d4 = self.down4(d3) + d5 = self.down5(d4) + df = self.flatten(d5) + z = self.densedown(df) + + uf = self.denseup(z) + u0 = self.unflatten(uf) + u1 = self.up1(u0) + u2 = self.up2(u1, d4) + u3 = self.up3(u2, d3) + u4 = self.up4(u3, d2) + + return self.final(u4, d1) + + def predict(self, x): + img = x["data"].to(self.device) + recon_x = self.forward(img) + return {"recon_x": recon_x} + + def compute_outputs_and_loss(self, input_dict, criterion, use_labels=True): + + images = input_dict["data"].to(self.device) + recon_images = self.forward(images) + + loss = criterion(recon_images, images) + + return recon_images, {"loss": loss} + + @property + def layers(self): + return torch.nn.Sequential( + self.down1, + self.down2, + self.down3, + self.down4, + self.down5, + self.flatten, + self.densedown, + self.denseup, + self.unflatten, + self.up1, + self.up2, + self.up3, + self.up4, + self.final + ) + +class UNetDown(nn.Module): + """Descending block of the U-Net. + + Args: + in_size: (int) number of channels in the input image. + out_size : (int) number of channels in the output image. + + """ + + def __init__(self, in_size, out_size): + super(UNetDown, self).__init__() + self.model = nn.Sequential( + nn.Conv3d(in_size, out_size, kernel_size=4, stride=2, padding=1, bias=False,), + nn.BatchNorm3d(out_size), + nn.LeakyReLU(0.2), + ) + + def forward(self, x): + return self.model(x) + + +class UNetUp(nn.Module): + """Ascending block of the U-Net. + + Args: + in_channels: (int) number of channels in the input image. + out_channels : (int) number of channels in the output image. + + """ + + def __init__(self, in_channels, out_channels, up_size): + super(UNetUp, self).__init__() + self.model = nn.Sequential( + nn.Upsample(size=up_size, mode='nearest'), + nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False,), + nn.BatchNorm3d(out_channels), + nn.ReLU(inplace=True), + ) + + def forward(self, x, skip_input=None): + if skip_input is not None: + x = torch.cat((x, skip_input), 1) # add the skip connection + x = self.model(x) + return x + + +class FinalLayer(nn.Module): + """Final block of the U-Net. + + Args: + in_size: (int) number of channels in the input image. + out_size : (int) number of channels in the output image. + + """ + + def __init__(self, in_size, out_size): + super(FinalLayer, self).__init__() + self.model = nn.Sequential( + nn.Upsample(size=[169, 208, 179]), + nn.Conv3d( + in_size, + out_size, + 3, + stride=1, + padding=1, + bias=False, + ), + nn.Sigmoid(), + ) + + def forward(self, x, skip_input=None): + if skip_input is not None: + x = torch.cat((x, skip_input), 1) # add the skip connection + x = self.model(x) + return x diff --git a/clinicadl/utils/network/vae/__init__.py b/clinicadl/utils/network/vae/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/network/vae/advanced_CVAE.py b/clinicadl/utils/network/vae/advanced_CVAE.py new file mode 100755 index 000000000..a71ec8c0c --- /dev/null +++ b/clinicadl/utils/network/vae/advanced_CVAE.py @@ -0,0 +1,139 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from clinicadl.utils.network.network import Network +from clinicadl.utils.network.vae.vae_utils import multiply_list + + +class CVAE_3D_final_conv(Network): + """ + This is the convolutional autoencoder whose main objective is to project the MRI into a smaller space + with the sole criterion of correctly reconstructing the data. Nothing longitudinal here. + fc = final layer conv + """ + + def __init__( + self, size_reduction_factor, latent_space_size, gpu, recons_weight, kl_weight + ): + super(CVAE_3D_final_conv, self).__init__(gpu=gpu) + nn.Module.__init__(self) + self.alpha = recons_weight + self.beta = kl_weight + self.n_conv = 3 + self.latent_space_size = latent_space_size + if size_reduction_factor == 2: + self.input_size = [1, 80, 96, 80] + elif size_reduction_factor == 3: + self.input_size = [1, 56, 64, 56] + elif size_reduction_factor == 4: + self.input_size = [1, 40, 48, 40] + elif size_reduction_factor == 5: + self.input_size = [1, 32, 40, 32] + self.feature_size = int( + multiply_list(self.input_size[1:], 2**self.n_conv) * 128 + ) + + # Encoder + self.conv1 = nn.Conv3d(1, 32, 3, stride=2, padding=1) # 32 x 40 x 48 x 40 + self.conv2 = nn.Conv3d(32, 64, 3, stride=2, padding=1) # 64 x 20 x 24 x 20 + self.conv3 = nn.Conv3d(64, 128, 3, stride=2, padding=1) # 128 x 10 x 12 x 10 + # self.conv4 = nn.Conv3d(128, 128, 3, stride=1, padding=1) # 256 x 10 x 12 x 10 + self.in1 = nn.InstanceNorm3d(32) + self.in2 = nn.InstanceNorm3d(64) + self.in3 = nn.InstanceNorm3d(128) + # self.in4 = nn.InstanceNorm3d(128) + self.fc10 = nn.Linear(self.feature_size, self.latent_space_size) + self.fc11 = nn.Linear(self.feature_size, self.latent_space_size) + + # Decoder + self.fc2 = nn.Linear(self.latent_space_size, 2 * self.feature_size) + self.upconv1 = nn.ConvTranspose3d( + 256, 128, 3, stride=2, padding=1, output_padding=1 + ) # 64 x 10 x 12 x 10 + self.upconv2 = nn.ConvTranspose3d( + 128, 64, 3, stride=2, padding=1, output_padding=1 + ) # 64 x 20 x 24 x 20 + # self.upconv3 = nn.ConvTranspose3d(64, 32, 3, stride=1, padding=1) # 32 x 40 x 48 x 40 + self.upconv4 = nn.ConvTranspose3d( + 64, 1, 3, stride=2, padding=1, output_padding=1 + ) # 1 x 80 x 96 x 80 + self.final = nn.Conv3d(1, 1, 3, stride=1, padding=1) + self.in5 = nn.InstanceNorm3d(128) + self.in6 = nn.InstanceNorm3d(64) + self.in7 = nn.InstanceNorm3d(1) + + self.to(self.device) + + def encoder(self, image): + h1 = F.leaky_relu(self.in1(self.conv1(image)), negative_slope=0.2, inplace=True) + h2 = F.leaky_relu(self.in2(self.conv2(h1)), negative_slope=0.2, inplace=True) + h3 = F.leaky_relu(self.in3(self.conv3(h2)), negative_slope=0.2, inplace=True) + # h4 = F.relu(self.in4(self.conv4(h3))) + # h5 = F.relu(self.fc1(h4.flatten(start_dim=1))) + h5 = h3.flatten(start_dim=1) + mu = torch.tanh(self.fc10(h5)) + logVar = self.fc11(h5) + return mu, logVar + + def decoder(self, encoded): + h5 = F.leaky_relu(self.fc2(encoded)).reshape( + [ + encoded.size()[0], + 256, + self.input_size[1] // 2**self.n_conv, + self.input_size[2] // 2**self.n_conv, + self.input_size[3] // 2**self.n_conv, + ] + ) + h6 = F.leaky_relu(self.in5(self.upconv1(h5))) + h7 = F.leaky_relu(self.in6(self.upconv2(h6))) + h8 = F.leaky_relu(self.in7(self.upconv4(h7))) + reconstructed = torch.sigmoid(self.final(h8)) + return reconstructed + + def reparametrize(self, mu, logVar): + # Reparameterization takes in the input mu and logVar and sample the mu + std * eps + std = torch.exp(logVar / 2).to(self.device) + eps = torch.normal( + mean=torch.tensor([0 for i in range(std.shape[1])]).float(), std=1 + ).to(self.device) + if self.beta != 0: # beta VAE + return mu + eps * std + else: # regular AE + return mu + + def forward(self, image): + mu, logVar = self.encoder(image) + if self.training: + encoded = self.reparametrize(mu, logVar) + else: + encoded = mu + reconstructed = self.decoder(encoded) + return mu, logVar, reconstructed + + def predict(self, x): + mu, _ = self.encoder(x) + reconstructed = self.decoder(mu) + return reconstructed + + def compute_outputs_and_loss(self, input_dict, criterion, use_labels=False): + + input_ = input_dict["data"].to(self.device) + mu, logVar, reconstructed = self.forward(input_) + losses = criterion(input_, reconstructed, mu, logVar) + reconstruction_loss, kl_loss = losses[0], losses[1] + total_loss = reconstruction_loss + self.beta * kl_loss + + if len(losses) > 2: + regularization = losses[2] + total_loss = ( + self.alpha * reconstruction_loss + self.beta * kl_loss + regularization + ) + + loss_dict = { + "loss": total_loss, + "recon_loss": reconstruction_loss, + "kl_loss": kl_loss, + } + return reconstructed, loss_dict diff --git a/clinicadl/utils/network/vae/base_vae.py b/clinicadl/utils/network/vae/base_vae.py old mode 100644 new mode 100755 index 6c53a128b..fb02e84be --- a/clinicadl/utils/network/vae/base_vae.py +++ b/clinicadl/utils/network/vae/base_vae.py @@ -14,12 +14,12 @@ def __init__( gpu=True, is_3D=False, recons_weight=1, - KL_weight=1, + kl_weight=1, ): super(BaseVAE, self).__init__(gpu=gpu) self.lambda1 = recons_weight - self.lambda2 = KL_weight + self.lambda2 = kl_weight self.latent_size = latent_size self.is_3D = is_3D @@ -40,44 +40,49 @@ def predict(self, x): return output def forward(self, x): - mu, logvar = self.encode(x) - z = self.reparameterize(mu, logvar) - return self.decode(z), mu, logvar + mu, logVar = self.encode(x) + z = self.reparameterize(mu, logVar) + return self.decode(z), mu, logVar def compute_outputs_and_loss(self, input_dict, criterion, use_labels=False): - images = input_dict["image"].to(self.device) - recon_images, mu, log_var = self.forward(images) + images = input_dict["data"].to(self.device) + recon_images, mu, logVar = self.forward(images) - recon_loss = criterion(recon_images, images) - if self.is_3D: - kl_loss = -0.5 * torch.mean( - torch.sum(1 + log_var - mu**2 - log_var.exp(), dim=1) - ) - else: - kl_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) - - loss = self.lambda1 * recon_loss + self.lambda2 * kl_loss / self.latent_size + losses = criterion(images, recon_images, mu, logVar) + reconstruction_loss, kl_loss = losses[0], losses[1] + total_loss = self.lambda1 * reconstruction_loss + self.lambda2 * kl_loss loss_dict = { - "loss": loss, - "recon_loss": recon_loss, + "recon_loss": reconstruction_loss, "kl_loss": kl_loss, } + # In the case there is a regularization term + if len(losses) > 2: + regularization = losses[2] + total_loss = ( + self.lambda1 * reconstruction_loss + + self.lambda2 * kl_loss + + regularization + ) + loss_dict["regularization"] = regularization + + loss_dict["loss"] = total_loss + return recon_images, loss_dict # VAE specific def encode(self, x): h = self.encoder(x) - mu, logvar = self.mu_layer(h), self.var_layer(h) - return mu, logvar + mu, logVar = self.mu_layer(h), self.var_layer(h) + return mu, logVar def decode(self, z): z = self.decoder(z) return z - def reparameterize(self, mu, logvar): - std = torch.exp(0.5 * logvar) + def reparameterize(self, mu, logVar): + std = torch.exp(0.5 * logVar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) diff --git a/clinicadl/utils/network/vae/convolutional_VAE.py b/clinicadl/utils/network/vae/convolutional_VAE.py new file mode 100755 index 000000000..0349ea9b2 --- /dev/null +++ b/clinicadl/utils/network/vae/convolutional_VAE.py @@ -0,0 +1,289 @@ +""" Credits to Benoit Sauty de Chalon @bsauty """ + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from clinicadl.utils.exceptions import ClinicaDLArgumentError +from clinicadl.utils.network.network import Network +from clinicadl.utils.network.vae.vae_utils import multiply_list + + +class CVAE_3D(Network): + """ + This is the convolutional autoencoder whose main objective is to project the MRI into a smaller space + with the sole criterion of correctly reconstructing the data. Nothing longitudinal here. + """ + + def __init__(self, latent_space_size, gpu): + super(CVAE_3D, self).__init__(gpu=gpu) + nn.Module.__init__(self) + self.beta = 5 + + # Encoder + # Input size 1 x 169 x 208 x 179 + self.conv1 = nn.Conv3d(1, 32, 3, stride=2, padding=1) # 32 x 85 x 104 x 90 + self.conv2 = nn.Conv3d(32, 64, 3, stride=2, padding=1) # 64 x 43 x 52 x 45 + self.conv3 = nn.Conv3d(64, 128, 3, stride=2, padding=1) # 128 x 22 x 26 x 23 + # self.conv4 = nn.Conv3d(128, 128, 3, stride=1, padding=1) # 256 x 10 x 12 x 10 + self.bn1 = nn.BatchNorm3d(32) + self.bn2 = nn.BatchNorm3d(64) + self.bn3 = nn.BatchNorm3d(128) + self.bn4 = nn.BatchNorm3d(128) + self.fc10 = nn.Linear(1683968, latent_space_size) + self.fc11 = nn.Linear(1683968, latent_space_size) + + # Decoder + self.fc2 = nn.Linear(latent_space_size, 3367936) + self.upconv1 = nn.ConvTranspose3d( + 256, 128, 3, stride=2, padding=1, output_padding=[0, 1, 0] + ) # 64 x 10 x 12 x 10 + self.upconv2 = nn.ConvTranspose3d( + 128, 64, 3, stride=2, padding=1, output_padding=[0, 1, 1] + ) # 64 x 20 x 24 x 20 + # self.upconv3 = nn.ConvTranspose3d(64, 32, 3, stride=1, padding=1) # 32 x 40 x 48 x 40 + self.upconv4 = nn.ConvTranspose3d( + 64, 1, 3, stride=2, padding=1, output_padding=[0, 1, 0] + ) # 1 x 80 x 96 x 80 + self.bn5 = nn.BatchNorm3d(128) + self.bn6 = nn.BatchNorm3d(64) + self.bn7 = nn.BatchNorm3d(32) + + self.to(self.device) + + def encoder(self, image): + h1 = F.relu(self.bn1(self.conv1(image))) + h2 = F.relu(self.bn2(self.conv2(h1))) + h3 = F.relu(self.bn3(self.conv3(h2))) + # h4 = F.relu(self.bn4(self.conv4(h3))) + # h5 = F.relu(self.fc1(h4.flatten(start_dim=1))) + h5 = h3.flatten(start_dim=1) + mu = torch.tanh(self.fc10(h5)) + logVar = self.fc11(h5) + return mu, logVar + + def decoder(self, encoded): + h5 = F.relu(self.fc2(encoded)).reshape([encoded.size()[0], 256, 22, 26, 23]) + h6 = F.relu(self.bn5(self.upconv1(h5))) + h7 = F.relu(self.bn6(self.upconv2(h6))) + # h8 = F.relu(self.bn7(self.upconv3(h7))) + reconstructed = F.relu(self.upconv4(h7)) + return reconstructed + + def reparametrize(self, mu, logVar): + # Reparameterization takes in the input mu and logVar and sample the mu + std * eps + std = torch.exp(logVar / 2).to(self.device) + eps = torch.normal( + mean=torch.tensor([0 for i in range(std.shape[1])]).float(), std=1 + ).to(self.device) + if self.beta != 0: # beta VAE + return mu + eps * std + else: # regular AE + return mu + + def forward(self, image): + mu, logVar = self.encoder(image) + if self.training: + encoded = self.reparametrize(mu, logVar) + else: + encoded = mu + reconstructed = self.decoder(encoded) + return mu, logVar, reconstructed + + def loss(self, mu, logVar, reconstructed, input_): + kl_divergence = ( + 0.5 * torch.sum(-1 - logVar + mu.pow(2) + logVar.exp()) / mu.shape[0] + ) + # recon_error = torch.nn.MSELoss(reduction='mean')(reconstructed, input_) + recon_error = torch.sum((reconstructed - input_) ** 2) / input_.shape[0] + return recon_error, kl_divergence + + def predict(self, x): + self.training = False + _, _, output = self.forward(x) + return output + + def compute_outputs_and_loss(self, input_dict, criterion, use_labels=False): + + vae_criterion = self.loss + + self.training = True + + input_ = input_dict["data"].to(self.device) + mu, logVar, reconstructed = self.forward(input_) + reconstruction_loss, kl_loss = vae_criterion(mu, logVar, input_, reconstructed) + + loss_dict = { + "loss": reconstruction_loss + self.beta * kl_loss, + "recon_loss": reconstruction_loss, + "kl_loss": kl_loss, + } + + return reconstructed, loss_dict + + +class CVAE_3D_half(Network): + """ + This is the convolutional autoencoder whose main objective is to project the MRI into a smaller space + with the sole criterion of correctly reconstructing the data. Nothing longitudinal here. + """ + + def __init__( + self, + size_reduction_factor, + latent_space_size, + gpu, + recons_weight, + kl_weight, + # normalization, + ): + super(CVAE_3D_half, self).__init__(gpu=gpu) + nn.Module.__init__(self) + self.alpha = recons_weight + self.beta = kl_weight + self.n_conv = 3 + self.latent_space_size = latent_space_size + if size_reduction_factor == 2: + self.input_size = [1, 80, 96, 80] + elif size_reduction_factor == 3: + self.input_size = [1, 56, 64, 56] + elif size_reduction_factor == 4: + self.input_size = [1, 40, 48, 40] + elif size_reduction_factor == 5: + self.input_size = [1, 32, 40, 32] + self.feature_size = int( + multiply_list(self.input_size[1:], 2**self.n_conv) * 128 + ) + + # Encoder + self.conv1 = nn.Conv3d(1, 32, 3, stride=2, padding=1) # 32 x 40 x 48 x 40 + self.conv2 = nn.Conv3d(32, 64, 3, stride=2, padding=1) # 64 x 20 x 24 x 20 + self.conv3 = nn.Conv3d(64, 128, 3, stride=2, padding=1) # 128 x 10 x 12 x 10 + # self.conv4 = nn.Conv3d(128, 128, 3, stride=1, padding=1) # 256 x 10 x 12 x 10 + self.bn1 = nn.BatchNorm3d(32) + self.bn2 = nn.BatchNorm3d(64) + self.bn3 = nn.BatchNorm3d(128) + self.bn4 = nn.BatchNorm3d(128) + + self.fc10 = nn.Linear(self.feature_size, self.latent_space_size) + self.fc11 = nn.Linear(self.feature_size, self.latent_space_size) + + # Decoder + self.fc2 = nn.Linear(self.latent_space_size, 2 * self.feature_size) + self.upconv1 = nn.ConvTranspose3d( + 256, 128, 3, stride=2, padding=1, output_padding=1 + ) # 64 x 10 x 12 x 10 + self.upconv2 = nn.ConvTranspose3d( + 128, 64, 3, stride=2, padding=1, output_padding=1 + ) # 64 x 20 x 24 x 20 + # self.upconv3 = nn.ConvTranspose3d(64, 32, 3, stride=1, padding=1) # 32 x 40 x 48 x 40 + self.upconv4 = nn.ConvTranspose3d( + 64, 1, 3, stride=2, padding=1, output_padding=1 + ) # 1 x 80 x 96 x 80 + self.bn5 = nn.BatchNorm3d(128) + self.bn6 = nn.BatchNorm3d(64) + self.bn7 = nn.BatchNorm3d(32) + + # if normalization == "batch": + # self.norm1 = nn.BatchNorm3d(32) + # self.norm2 = nn.BatchNorm3d(64) + # self.norm3 = nn.BatchNorm3d(128) + # # self.norm4 = nn.BatchNorm3d(128) + # self.norm5 = nn.BatchNorm3d(128) + # self.norm6 = nn.BatchNorm3d(64) + # self.norm7 = nn.BatchNorm3d(32) + # elif normalization == "group": + # self.norm1 = nn.GroupNorm(6, 32) + # self.norm2 = nn.GroupNorm(6, 64) + # self.norm3 = nn.GroupNorm(6, 128) + # # self.norm4 = nn.GroupNorm(128) + # self.norm5 = nn.GroupNorm(6, 128) + # self.norm6 = nn.GroupNorm(6, 64) + # self.norm7 = nn.GroupNorm(6, 32) + # elif normalization == "instance": + # self.norm1 = nn.InstanceNorm3d(32) + # self.norm2 = nn.InstanceNorm3d(64) + # self.norm3 = nn.InstanceNorm3d(128) + # # self.norm4 = nn.InstanceNorm3d(128) + # self.norm5 = nn.InstanceNorm3d(128) + # self.norm6 = nn.InstanceNorm3d(64) + # self.norm7 = nn.InstanceNorm3d(32) + # else: + # raise ClinicaDLArgumentError( + # f"{normalization} is an unknown normalization method. Please choose between 'batch', 'group' or 'instance'." + # ) + + self.to(self.device) + + def encoder(self, image): + h1 = F.leaky_relu(self.bn1(self.conv1(image)), negative_slope=0.2, inplace=True) + h2 = F.leaky_relu(self.bn2(self.conv2(h1)), negative_slope=0.2, inplace=True) + h3 = F.leaky_relu(self.bn3(self.conv3(h2)), negative_slope=0.2, inplace=True) + # h4 = F.relu(self.bn4(self.conv4(h3))) + # h5 = F.relu(self.fc1(h4.flatten(start_dim=1))) + h5 = h3.flatten(start_dim=1) + mu = torch.tanh(self.fc10(h5)) + logVar = self.fc11(h5) + return mu, logVar + + def decoder(self, encoded): + h5 = F.relu(self.fc2(encoded)).reshape( + [ + encoded.size()[0], + 256, + self.input_size[1] // 2**self.n_conv, + self.input_size[2] // 2**self.n_conv, + self.input_size[3] // 2**self.n_conv, + ] + ) + h6 = F.relu(self.bn5(self.upconv1(h5))) + h7 = F.relu(self.bn6(self.upconv2(h6))) + # h8 = F.relu(self.bn7(self.upconv3(h7))) + reconstructed = torch.sigmoid(self.upconv4(h7)) + return reconstructed + + def reparametrize(self, mu, logVar): + # Reparameterization takes in the input mu and logVar and sample the mu + std * eps + std = torch.exp(logVar / 2).to(self.device) + eps = torch.normal( + mean=torch.tensor([0 for i in range(std.shape[1])]).float(), std=1 + ).to(self.device) + if self.beta != 0: # beta VAE + return mu + eps * std + else: # regular AE + return mu + + def forward(self, image): + mu, logVar = self.encoder(image) + if self.training: + encoded = self.reparametrize(mu, logVar) + else: + encoded = mu + reconstructed = self.decoder(encoded) + return mu, logVar, reconstructed + + def predict(self, x): + mu, _ = self.encoder(x) + reconstructed = self.decoder(mu) + return reconstructed + + def compute_outputs_and_loss(self, input_dict, criterion, use_labels=False): + + input_ = input_dict["data"].to(self.device) + mu, logVar, reconstructed = self.forward(input_) + losses = criterion(input_, reconstructed, mu, logVar) + reconstruction_loss, kl_loss = losses[0], losses[1] + total_loss = reconstruction_loss + self.beta * kl_loss + + if len(losses) > 2: + regularization = losses[2] + total_loss = ( + self.alpha * reconstruction_loss + self.beta * kl_loss + regularization + ) + + loss_dict = { + "loss": total_loss, + "recon_loss": reconstruction_loss, + "kl_loss": kl_loss, + } + return reconstructed, loss_dict diff --git a/clinicadl/utils/network/vae/vae_blocks.py b/clinicadl/utils/network/vae/vae_blocks.py new file mode 100755 index 000000000..4d5b94a39 --- /dev/null +++ b/clinicadl/utils/network/vae/vae_blocks.py @@ -0,0 +1,210 @@ +from typing import List + +from torch import nn + +from clinicadl.utils.network.vae.vae_layers import ( + EncoderConv3DLayer, + DecoderTranspose3DLayer, + DecoderUpsample3DLayer, + EncoderResLayer, + DecoderResLayer, +) + + +class EncoderBlock(nn.Module): + """Encoder block where we choose the type of layer (conv or res)""" + + def __init__( + self, + input_channels, + output_channels, + n_layer_per_block_encoder=3, + block_type="conv", + normalization="batch", + ): + super(EncoderBlock, self).__init__() + + channels = get_channels( + input_channels, output_channels, n_layer_per_block_encoder + ) + + layers = [] + + layers.append( + get_encoder_layer( + block_type, channels[0], channels[1], 4, 2, 1, normalization + ) + ) + + for i in range(1, n_layer_per_block_encoder): + layers.append( + get_encoder_layer( + block_type, + channels[i], + channels[i + 1], + 3, + 1, + 1, + normalization, + ) + ) + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class DecoderBlock(nn.Module): + """Decoder block where we choose the type of layer (conv or res)""" + + def __init__( + self, + input_channels, + output_channels, + input_size, + output_padding, + n_layer_per_block_decoder, + block_type="upsample", + normalization="batch", + is_last_block=False, + last_layer_conv=False, + ): + super(DecoderBlock, self).__init__() + + channels = get_channels( + output_channels, input_channels, n_layer_per_block_decoder + ) + + layers = [] + + for i in range(n_layer_per_block_decoder, 1, -1): + layers.append( + get_decoder_layer( + block_type, + input_channels=channels[i], + output_channels=channels[i - 1], + input_size=input_size, + kernel_size=3, + stride=1, + padding=1, + output_padding=[0, 0, 0], + normalization=normalization, + ) + ) + + layers.append( + get_decoder_layer( + block_type, + input_channels=channels[1], + output_channels=channels[0], + input_size=input_size, + kernel_size=3, + stride=1, + padding=1, + output_padding=output_padding, + normalization=normalization, + is_last_layer=(is_last_block and (not last_layer_conv)), + ) + ) + + if last_layer_conv: + layers.append(nn.Conv3d(channels[0], channels[0], 3, stride=1, padding=1)) + + if is_last_block: + layers.append(nn.Sigmoid()) + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + + +def get_channels( + input_channels: int, output_channels: int, n_layer_per_block_encoder: int +): + channels = [] + channels.append(input_channels) + for _ in range(n_layer_per_block_encoder): + channels.append(output_channels) + return channels + + +def get_encoder_layer( + block_type: str, + input_channels: int, + output_channels: int, + kernel_size: int, + stride: int, + padding: int, + normalization: str, +): + if block_type in ["conv", "transpose", "upsample"]: + return EncoderConv3DLayer( + input_channels, + output_channels, + kernel_size, + stride, + padding, + normalization, + ) + elif block_type == "res": + return EncoderResLayer( + input_channels, + output_channels, + normalization, + ) + else: + raise AttributeError("Bad block type specified. Block type must be conv or res") + + +def get_decoder_layer( + block_type: str, + input_channels: int, + output_channels: int, + input_size: int, + kernel_size: int, + stride: int, + padding: int, + output_padding: List[int], + normalization: str, + is_last_layer:bool=False, +): + if block_type in ["conv", "upsample"]: + return DecoderUpsample3DLayer( + input_channels, + output_channels, + input_size, + kernel_size, + stride, + padding, + output_padding, + normalization, + is_last_layer, + ) + elif block_type == "transpose": + return DecoderTranspose3DLayer( + input_channels, + output_channels, + input_size, + kernel_size, + stride, + padding, + output_padding, + normalization, + is_last_layer, + ) + elif block_type == "res": + return DecoderResLayer( + input_channels, + output_channels, + input_size, + output_padding, + normalization, + is_last_layer, + ) + else: + raise AttributeError( + "Bad block type specified. Block type must be conv, upsample, transpose or res" + ) diff --git a/clinicadl/utils/network/vae/vae_layers.py b/clinicadl/utils/network/vae/vae_layers.py new file mode 100755 index 000000000..ef4c9ce49 --- /dev/null +++ b/clinicadl/utils/network/vae/vae_layers.py @@ -0,0 +1,596 @@ +from typing import List + +import torch.nn.functional as F +from torch import nn +import torch + +from clinicadl.utils.network.vae.vae_utils import get_norm2d, get_norm3d + + +class EncoderLayer2D(nn.Module): + """ + Class defining the encoder's part of the Autoencoder. + This layer is composed of one 2D convolutional layer, + a batch normalization layer with a leaky relu + activation function. + """ + + def __init__( + self, + input_channels, + output_channels, + kernel_size=4, + stride=2, + padding=1, + output_padding=0, + normalization="batch", + ): + super(EncoderLayer2D, self).__init__() + self.layer = nn.Sequential( + nn.Conv2d( + input_channels, + output_channels, + kernel_size, + stride=stride, + padding=padding, + output_padding=output_padding, + bias=False, + ), + get_norm2d(normalization, output_channels), + ) + + def forward(self, x): + # out = F.leaky_relu(self.layer(x), negative_slope=0.2, inplace=True) + out = F.silu(self.layer(x), inplace=True) + return out + + +class DecoderLayer2D(nn.Module): + """ + Class defining the decoder's part of the Autoencoder. + This layer is composed of one 2D transposed convolutional layer, + a batch normalization layer with a relu activation function. + """ + + def __init__( + self, + input_channels, + output_channels, + kernel_size=4, + stride=2, + padding=1, + output_padding=0, + normalization="batch", + ): + super(DecoderLayer2D, self).__init__() + self.layer = nn.Sequential( + nn.ConvTranspose2d( + input_channels, + output_channels, + kernel_size, + stride=stride, + padding=padding, + output_padding=output_padding, + bias=False, + ), + get_norm2d(normalization, output_channels), + ) + + def forward(self, x): + # out = F.relu(self.layer(x), inplace=True) + out = F.silu(self.layer(x), inplace=True) + return out + + +class EncoderConv3DLayer(nn.Module): + """ + Class defining the encoder's part of the Autoencoder. + This layer is composed of one 3D convolutional layer, + a batch normalization layer with a leaky relu + activation function. + """ + + def __init__( + self, + input_channels, + output_channels, + kernel_size=4, + stride=2, + padding=1, + normalization="batch", + ): + super(EncoderConv3DLayer, self).__init__() + self.conv = nn.Conv3d( + input_channels, + output_channels, + kernel_size, + stride=stride, + padding=padding, + bias=False, + ) + self.norm = get_norm3d(normalization, output_channels) + + def forward(self, x): + out = self.norm(self.conv(x)) + out = F.silu(out, inplace=True) + # out = F.leaky_relu(out, negative_slope=0.2, inplace=True) + return out + + +class DecoderTranspose3DLayer(nn.Module): + """ + Class defining the decoder's part of the Autoencoder. + This layer is composed of one 3D transposed convolutional layer, + a batch normalization layer with a relu activation function. + """ + + def __init__( + self, + input_channels, + output_channels, + input_size=None, + kernel_size=4, + stride=1, + padding=1, + output_padding=0, + normalization="batch", + ): + super(DecoderTranspose3DLayer, self).__init__() + self.convtranspose = nn.ConvTranspose3d( + input_channels, + output_channels, + kernel_size, + stride=stride, + padding=padding, + output_padding=output_padding, + bias=False, + ) + self.norm = get_norm3d(normalization, output_channels) + + def forward(self, x): + out = self.norm(self.convtranspose(x)) + out = F.silu(out, inplace=True) + # out = F.leaky_relu(out, negative_slope=0.2, inplace=True) + return out + + +class DecoderUpsample3DLayer(nn.Module): + """ + Class defining the decoder's part of the Autoencoder. + This layer is composed of one 3D transposed convolutional layer, + a batch normalization layer with a relu activation function. + """ + + def __init__( + self, + input_channels, + output_channels, + input_size, + kernel_size=3, + stride=1, + padding=1, + output_padding=[0, 0, 0], + normalization="batch", + is_last_layer=False, + ): + super(DecoderUpsample3DLayer, self).__init__() + self.upsample = nn.Upsample( + size=[ + input_size[0] * 2 + output_padding[0], + input_size[1] * 2 + output_padding[1], + input_size[2] * 2 + output_padding[2], + ], + mode="nearest", + ) if input_channels != output_channels else nn.Sequential() + + self.conv = nn.Conv3d( + input_channels, + output_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=False, + ) + self.norm = get_norm3d(normalization, output_channels) if not is_last_layer else nn.Sequential() + + self.activation = nn.SiLU(inplace=True) if not is_last_layer else nn.Sequential() + + self.input_size = input_size + self.padding = output_padding + self.dim = ( + [ + input_size[0] * 2 + output_padding[0], + input_size[1] * 2 + output_padding[1], + input_size[2] * 2 + output_padding[2], + ], + ) + + def forward(self, x): + out = self.norm(self.conv(self.upsample(x))) + out = self.activation(out) + # out = F.leaky_relu(out, negative_slope=0.2, inplace=True) + return out + + +class Flatten(nn.Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +class Unflatten2D(nn.Module): + def __init__(self, channel, height, width): + super(Unflatten2D, self).__init__() + self.channel = channel + self.height = height + self.width = width + + def forward(self, input): + return input.view(input.size(0), self.channel, self.height, self.width) + + +class Unflatten3D(nn.Module): + def __init__(self, channel, height, width, depth): + super(Unflatten3D, self).__init__() + self.channel = channel + self.height = height + self.width = width + self.depth = depth + + def forward(self, input): + return input.view( + input.size(0), self.channel, self.height, self.width, self.depth + ) + + +class EncoderResLayer(nn.Module): + """ + This layer is composed of a residual block. + """ + + def __init__( + self, + input_channels, + output_channels, + normalization="batch", + ): + super(EncoderResLayer, self).__init__() + + if output_channels != input_channels: + stride = 2 + kernel_size = 4 + else: + stride = 1 + kernel_size = 3 + + self.conv1 = nn.Conv3d( + input_channels, + output_channels, + kernel_size=kernel_size, + stride=stride, + padding=1, + bias=False, + ) + + self.norm1 = get_norm3d(normalization, output_channels) + + self.conv2 = nn.Conv3d( + output_channels, + output_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ) + self.norm2 = get_norm3d(normalization, output_channels) + + if input_channels == output_channels: + self.shortcut = nn.Sequential() + else: + self.shortcut = nn.Sequential( + nn.Conv3d( + input_channels, + output_channels, + kernel_size=kernel_size, + stride=stride, + padding=1, + bias=False, + ), + nn.BatchNorm3d(output_channels), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + out = self.norm1(self.conv1(x)) + out = F.silu(out, inplace=True) + # out = F.relu(out, inplace=True) + + out = self.norm2(self.conv2(out)) + + out += self.shortcut(x) + out = F.silu(out, inplace=True) + # out = F.relu(out, inplace=True) + + return out + + +class DecoderResLayer(nn.Module): + def __init__( + self, + input_channels, + output_channels, + input_size, + output_padding, + normalization="batch", + is_last_layer=False, + ): + super(DecoderResLayer, self).__init__() + + self.conv2 = nn.Conv3d( + input_channels, + input_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ) + + self.norm2 = get_norm3d(normalization, input_channels) + + if input_channels == output_channels: + self.conv1 = nn.Conv3d( + input_channels, + output_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ) + self.shortcut = nn.Sequential() + else: + self.conv1 = nn.Sequential( + nn.Upsample( + size=[ + input_size[0] * 2 + output_padding[0], + input_size[1] * 2 + output_padding[1], + input_size[2] * 2 + output_padding[2], + ], + mode="nearest", + ), + nn.Conv3d( + input_channels, + output_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + ) + self.shortcut = nn.Sequential( + nn.Upsample( + size=[ + input_size[0] * 2 + output_padding[0], + input_size[1] * 2 + output_padding[1], + input_size[2] * 2 + output_padding[2], + ], + mode="nearest", + ), + nn.Conv3d( + input_channels, + output_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + get_norm3d(normalization, output_channels), + ) + + self.norm1 = get_norm3d(normalization, output_channels) + + self.activation = nn.SiLU(inplace=True) if not is_last_layer else nn.Sequential() + + def forward(self, x): + out = self.norm2(self.conv2(x)) + out = F.silu(out, inplace=True) + # out = F.relu(out, inplace=True) + + out = self.norm1(self.conv1(out)) + + out += self.shortcut(x) + out = self.activation(out) + # out = F.relu(out) + + return out + + +class VAE_Encoder(nn.Module): + """""" + + def __init__( + self, + input_shape, + n_conv=4, + first_layer_channels=32, + latent_dim=1, + feature_size=1024, + ): + """ + Feature size is the size of the vector if latent_dim=1 + or is the number of feature maps (number of channels) if latent_dim=2 + """ + super(VAE_Encoder, self).__init__() + + self.input_c = input_shape[0] + self.input_h = input_shape[1] + self.input_w = input_shape[2] + + decoder_padding = [] + tensor_h, tensor_w = self.input_h, self.input_w + + self.layers = [] + + # Input Layer + self.layers.append(EncoderLayer2D(self.input_c, first_layer_channels)) + padding_h, padding_w = 0, 0 + if tensor_h % 2 != 0: + padding_h = 1 + if tensor_w % 2 != 0: + padding_w = 1 + decoder_padding.append([padding_h, padding_w]) + tensor_h, tensor_w = tensor_h // 2, tensor_w // 2 + # Conv Layers + for i in range(n_conv - 1): + self.layers.append( + EncoderLayer2D( + first_layer_channels * 2**i, first_layer_channels * 2 ** (i + 1) + ) + ) + padding_h, padding_w = 0, 0 + if tensor_h % 2 != 0: + padding_h = 1 + if tensor_w % 2 != 0: + padding_w = 1 + decoder_padding.append([padding_h, padding_w]) + tensor_h, tensor_w = tensor_h // 2, tensor_w // 2 + + self.decoder_padding = decoder_padding + + # Final Layer + if latent_dim == 1: + n_pix = ( + first_layer_channels + * 2 ** (n_conv - 1) + * (self.input_h // (2**n_conv)) + * (self.input_w // (2**n_conv)) + ) + self.layers.append( + nn.Sequential(Flatten(), nn.Linear(n_pix, feature_size), nn.ReLU()) + ) + elif latent_dim == 2: + self.layers.append( + nn.Sequential( + nn.Conv2d( + first_layer_channels * 2 ** (n_conv - 1), + feature_size, + 3, + stride=1, + padding=1, + bias=False, + ), + nn.ReLU(), + ) + ) + else: + raise AttributeError( + "Bad latent dimension specified. Latent dimension must be 1 or 2" + ) + + self.sequential = nn.Sequential(*self.layers) + + def forward(self, x): + z = self.sequential(x) + return z + + +class VAE_Decoder(nn.Module): + """""" + + def __init__( + self, + input_shape, + latent_size, + n_conv=4, + last_layer_channels=32, + latent_dim=1, + feature_size=1024, + padding=None, + ): + """ + Feature size is the size of the vector if latent_dim=1 + or is the W/H of the output channels if laten_dim=2 + """ + super(VAE_Decoder, self).__init__() + + self.input_c = input_shape[0] + self.input_h = input_shape[1] + self.input_w = input_shape[2] + + if not padding: + output_padding = [[0, 0] for i in range(n_conv - 1)] + else: + output_padding = padding + + self.layers = [] + + if latent_dim == 1: + n_pix = ( + last_layer_channels + * 2 ** (n_conv - 1) + * (self.input_h // (2**n_conv)) + * (self.input_w // (2**n_conv)) + ) + self.layers.append( + nn.Sequential( + nn.Linear(latent_size, feature_size), + nn.ReLU(), + nn.Linear(feature_size, n_pix), + nn.ReLU(), + Unflatten2D( + last_layer_channels * 2 ** (n_conv - 1), + self.input_h // (2**n_conv), + self.input_w // (2**n_conv), + ), + nn.ReLU(), + ) + ) + elif latent_dim == 2: + self.layers.append( + nn.Sequential( + nn.ConvTranspose2d( + latent_size, feature_size, 3, stride=1, padding=1, bias=False + ), + nn.ReLU(), + nn.ConvTranspose2d( + feature_size, + last_layer_channels * 2 ** (n_conv - 1), + 3, + stride=1, + padding=1, + bias=False, + ), + nn.ReLU(), + ) + ) + else: + raise AttributeError( + "Bad latent dimension specified. Latent dimension must be 1 or 2" + ) + + for i in range(n_conv - 1, 0, -1): + self.layers.append( + DecoderLayer2D( + last_layer_channels * 2 ** (i), + last_layer_channels * 2 ** (i - 1), + output_padding=output_padding[i], + ) + ) + + self.layers.append( + nn.Sequential( + nn.ConvTranspose2d( + last_layer_channels, + self.input_c, + 4, + stride=2, + padding=1, + output_padding=output_padding[0], + bias=False, + ), + nn.Sigmoid(), + ) + ) + + self.sequential = nn.Sequential(*self.layers) + + def forward(self, z): + y = self.sequential(z) + return y diff --git a/clinicadl/utils/network/vae/vae_utils.py b/clinicadl/utils/network/vae/vae_utils.py old mode 100644 new mode 100755 index 0f9b87430..a90914614 --- a/clinicadl/utils/network/vae/vae_utils.py +++ b/clinicadl/utils/network/vae/vae_utils.py @@ -1,352 +1,87 @@ +import numpy as np import torch -import torch.nn.functional as F from torch import nn - -class EncoderLayer2D(nn.Module): - """ - Class defining the encoder's part of the Autoencoder. - This layer is composed of one 2D convolutional layer, - a batch normalization layer with a leaky relu - activation function. - """ - - def __init__( - self, - input_channels, - output_channels, - kernel_size=4, - stride=2, - padding=1, - ): - super(EncoderLayer2D, self).__init__() - self.layer = nn.Sequential( - nn.Conv2d( - input_channels, - output_channels, - kernel_size, - stride=stride, - padding=padding, - bias=False, - ), - nn.BatchNorm2d(output_channels), - ) - - def forward(self, x): - x = F.leaky_relu(self.layer(x), negative_slope=0.2, inplace=True) - return x +from clinicadl.utils.exceptions import ClinicaDLArgumentError -class DecoderLayer2D(nn.Module): - """ - Class defining the decoder's part of the Autoencoder. - This layer is composed of one 2D transposed convolutional layer, - a batch normalization layer with a relu activation function. - """ - - def __init__( - self, input_channels, output_channels, kernel_size=4, stride=2, padding=1 - ): - super(DecoderLayer2D, self).__init__() - self.layer = nn.Sequential( - nn.ConvTranspose2d( - input_channels, - output_channels, - kernel_size, - stride=stride, - padding=padding, - bias=False, - ), - nn.BatchNorm2d(output_channels), - ) - - def forward(self, x): - x = F.relu(self.layer(x), inplace=True) - return x +def VAEGaussianLoss(input, reconstruction, mu, logVar): + kl_divergence = ( + 0.5 * torch.sum(-1 - logVar + mu.pow(2) + logVar.exp()) / mu.shape[0] + ) / np.prod(input.shape) + recon_error = nn.MSELoss(reduction="mean")(reconstruction, input) + return recon_error, kl_divergence -class EncoderLayer3D(nn.Module): - """ - Class defining the encoder's part of the Autoencoder. - This layer is composed of one 3D convolutional layer, - a batch normalization layer with a leaky relu - activation function. - """ +def VAEBernoulliLoss(input, reconstruction, mu, logVar): + kl_divergence = ( + 0.5 * torch.sum(-1 - logVar + mu.pow(2) + logVar.exp()) / mu.shape[0] + ) / np.prod(input.shape) + recon_error = nn.BCELoss(reduction="mean")(reconstruction, input) + return recon_error, kl_divergence - def __init__( - self, input_channels, output_channels, kernel_size=4, stride=2, padding=1 - ): - super(EncoderLayer3D, self).__init__() - self.layer = nn.Sequential( - nn.Conv3d( - input_channels, - output_channels, - kernel_size, - stride=stride, - padding=padding, - bias=False, - ), - nn.BatchNorm3d(output_channels), - ) - def forward(self, x): - x = F.leaky_relu(self.layer(x), negative_slope=0.2, inplace=True) - return x +def VAEContinuousBernoulliLoss(input, reconstruction, mu, logVar): + kl_divergence = ( + 0.5 * torch.sum(-1 - logVar + mu.pow(2) + logVar.exp()) / mu.shape[0] + ) / np.prod(input.shape) + recon_error = nn.BCELoss(reduction="mean")(reconstruction, input) + log_constant = sumlogC(reconstruction) / np.prod(input.shape) + return kl_divergence, recon_error, log_constant -class DecoderLayer3D(nn.Module): +def sumlogC(x, eps=1e-5): """ - Class defining the decoder's part of the Autoencoder. - This layer is composed of one 3D transposed convolutional layer, - a batch normalization layer with a relu activation function. + Numerically stable implementation of + sum of logarithm of Continous Bernoulli + constant C, using Taylor 2nd degree approximation + + Parameter + ---------- + x : Tensor of dimensions (batch_size, dim) + x takes values in (0,1) """ - - def __init__( - self, - input_channels, - output_channels, - kernel_size=4, - stride=2, - padding=1, - output_padding=0, - ): - super(DecoderLayer3D, self).__init__() - self.layer = nn.Sequential( - nn.ConvTranspose3d( - input_channels, - output_channels, - kernel_size, - stride=stride, - padding=padding, - output_padding=output_padding, - bias=False, - ), - nn.BatchNorm3d(output_channels), + x = torch.clamp(x, eps, 1.0 - eps) + mask = torch.abs(x - 0.5).ge(eps) + far = torch.masked_select(x, mask) + close = torch.masked_select(x, ~mask) + far_values = torch.log((torch.log(1.0 - far) - torch.log(far)).div(1.0 - 2.0 * far)) + close_values = torch.log(torch.tensor((2.0))) + torch.log( + 1.0 + torch.pow(1.0 - 2.0 * close, 2) / 3.0 + ) + return far_values.sum() + close_values.sum() + + +def multiply_list(L, factor): + product = 1 + for x in L: + product = product * x / factor + return product + + +def get_norm2d(normalization, output_channels): + if normalization == "batch": + norm_layer = nn.BatchNorm2d(output_channels) + elif normalization == "group": + norm_layer = nn.GroupNorm(output_channels / 2, output_channels) + elif normalization == "instance": + norm_layer = nn.InstanceNorm2d(output_channels) + else: + raise ClinicaDLArgumentError( + f"{normalization} is an unknown normalization method. Please choose between 'batch', 'group' or 'instance'." ) - - def forward(self, x): - x = F.relu(self.layer(x), inplace=True) - return x - - -class Flatten(nn.Module): - def forward(self, input): - return input.view(input.size(0), -1) - - -class Unflatten2D(nn.Module): - def __init__(self, channel, height, width): - super(Unflatten2D, self).__init__() - self.channel = channel - self.height = height - self.width = width - - def forward(self, input): - return input.view(input.size(0), self.channel, self.height, self.width) - - -class Unflatten3D(nn.Module): - def __init__(self, channel, height, width, depth): - super(Unflatten3D, self).__init__() - self.channel = channel - self.height = height - self.width = width - self.depth = depth - - def forward(self, input): - return input.view( - input.size(0), self.channel, self.height, self.width, self.depth + return norm_layer + + +def get_norm3d(normalization, output_channels): + if normalization == "batch": + norm_layer = nn.BatchNorm3d(output_channels) + elif normalization == "group": + norm_layer = nn.GroupNorm(output_channels / 2, output_channels) + elif normalization == "instance": + norm_layer = nn.InstanceNorm3d(output_channels) + else: + raise ClinicaDLArgumentError( + f"{normalization} is an unknown normalization method. Please choose between 'batch', 'group' or 'instance'." ) - - -class VAE_Encoder(nn.Module): - """""" - - def __init__( - self, - input_shape, - n_conv=4, - first_layer_channels=32, - latent_dim=1, - feature_size=1024, - ): - """ - Feature size is the size of the vector if latent_dim=1 - or is the number of feature maps (number of channels) if latent_dim=2 - """ - super(VAE_Encoder, self).__init__() - - self.input_c = input_shape[0] - self.input_h = input_shape[1] - self.input_w = input_shape[2] - - decoder_padding = [] - tensor_h, tensor_w = self.input_h, self.input_w - - self.layers = [] - - # Input Layer - self.layers.append(EncoderLayer2D(self.input_c, first_layer_channels)) - padding_h, padding_w = 0, 0 - if tensor_h % 2 != 0: - padding_h = 1 - if tensor_w % 2 != 0: - padding_w = 1 - decoder_padding.append([padding_h, padding_w]) - tensor_h, tensor_w = tensor_h // 2, tensor_w // 2 - # Conv Layers - for i in range(n_conv - 1): - self.layers.append( - EncoderLayer2D( - first_layer_channels * 2**i, first_layer_channels * 2 ** (i + 1) - ) - ) - padding_h, padding_w = 0, 0 - if tensor_h % 2 != 0: - padding_h = 1 - if tensor_w % 2 != 0: - padding_w = 1 - decoder_padding.append([padding_h, padding_w]) - tensor_h, tensor_w = tensor_h // 2, tensor_w // 2 - - self.decoder_padding = decoder_padding - - # Final Layer - if latent_dim == 1: - n_pix = ( - first_layer_channels - * 2 ** (n_conv - 1) - * (self.input_h // (2**n_conv)) - * (self.input_w // (2**n_conv)) - ) - self.layers.append( - nn.Sequential(Flatten(), nn.Linear(n_pix, feature_size), nn.ReLU()) - ) - elif latent_dim == 2: - self.layers.append( - nn.Sequential( - nn.Conv2d( - first_layer_channels * 2 ** (n_conv - 1), - feature_size, - 3, - stride=1, - padding=1, - bias=False, - ), - nn.ReLU(), - ) - ) - else: - raise AttributeError( - "Bad latent dimension specified. Latent dimension must be 1 or 2" - ) - - self.sequential = nn.Sequential(*self.layers) - - def forward(self, x): - z = self.sequential(x) - return z - - -class VAE_Decoder(nn.Module): - """""" - - def __init__( - self, - input_shape, - latent_size, - n_conv=4, - last_layer_channels=32, - latent_dim=1, - feature_size=1024, - padding=None, - ): - """ - Feature size is the size of the vector if latent_dim=1 - or is the W/H of the output channels if laten_dim=2 - """ - super(VAE_Decoder, self).__init__() - - self.input_c = input_shape[0] - self.input_h = input_shape[1] - self.input_w = input_shape[2] - - if not padding: - output_padding = [[0, 0] for i in range(n_conv - 1)] - else: - output_padding = padding - - self.layers = [] - - if latent_dim == 1: - n_pix = ( - last_layer_channels - * 2 ** (n_conv - 1) - * (self.input_h // (2**n_conv)) - * (self.input_w // (2**n_conv)) - ) - self.layers.append( - nn.Sequential( - nn.Linear(latent_size, feature_size), - nn.ReLU(), - nn.Linear(feature_size, n_pix), - nn.ReLU(), - Unflatten2D( - last_layer_channels * 2 ** (n_conv - 1), - self.input_h // (2**n_conv), - self.input_w // (2**n_conv), - ), - nn.ReLU(), - ) - ) - elif latent_dim == 2: - self.layers.append( - nn.Sequential( - nn.ConvTranspose2d( - latent_size, feature_size, 3, stride=1, padding=1, bias=False - ), - nn.ReLU(), - nn.ConvTranspose2d( - feature_size, - last_layer_channels * 2 ** (n_conv - 1), - 3, - stride=1, - padding=1, - bias=False, - ), - nn.ReLU(), - ) - ) - else: - raise AttributeError( - "Bad latent dimension specified. Latent dimension must be 1 or 2" - ) - - for i in range(n_conv - 1, 0, -1): - self.layers.append( - DecoderLayer2D( - last_layer_channels * 2 ** (i), - last_layer_channels * 2 ** (i - 1), - ) - ) - - self.layers.append( - nn.Sequential( - nn.ConvTranspose2d( - last_layer_channels, - self.input_c, - 4, - stride=2, - padding=1, - output_padding=output_padding[0], - bias=False, - ), - nn.Sigmoid(), - ) - ) - - self.sequential = nn.Sequential(*self.layers) - - def forward(self, z): - y = self.sequential(z) - return y + return norm_layer diff --git a/clinicadl/utils/network/vae/vanilla_vae.py b/clinicadl/utils/network/vae/vanilla_vae.py old mode 100644 new mode 100755 index 5207b6c67..bb06499bb --- a/clinicadl/utils/network/vae/vanilla_vae.py +++ b/clinicadl/utils/network/vae/vanilla_vae.py @@ -2,15 +2,17 @@ from torch import nn from clinicadl.utils.network.vae.base_vae import BaseVAE -from clinicadl.utils.network.vae.vae_utils import ( - DecoderLayer3D, - EncoderLayer3D, +from clinicadl.utils.network.vae.vae_layers import ( + DecoderTranspose3DLayer, + EncoderConv3DLayer, Flatten, Unflatten3D, VAE_Decoder, VAE_Encoder, ) +DecoderLayer3D = DecoderTranspose3DLayer +EncoderLayer3D = EncoderConv3DLayer class VanillaDenseVAE(BaseVAE): """ @@ -23,10 +25,10 @@ class VanillaDenseVAE(BaseVAE): def __init__( self, input_size, - latent_space_size=128, - feature_size=1024, - recons_weight=1, - KL_weight=1, + latent_space_size, + feature_size, + recons_weight, + kl_weight, gpu=True, ): n_conv = 4 @@ -59,7 +61,7 @@ def __init__( latent_space_size, gpu=gpu, recons_weight=recons_weight, - KL_weight=KL_weight, + kl_weight=kl_weight, is_3D=False, ) @@ -87,10 +89,10 @@ class VanillaSpatialVAE(BaseVAE): def __init__( self, input_size, - latent_space_size=128, - feature_size=1024, - recons_weight=1, - KL_weight=1, + latent_space_size, + feature_size, + recons_weight, + kl_weight, gpu=True, ): feature_channels = 64 @@ -129,7 +131,7 @@ def __init__( latent_space_size, gpu=gpu, recons_weight=recons_weight, - KL_weight=KL_weight, + kl_weight=kl_weight, is_3D=False, ) @@ -146,7 +148,7 @@ def get_task(): return ["reconstruction"] -class Vanilla3DVAE(BaseVAE): +class Vanilla3DspacialVAE(BaseVAE): """ This network is a 3D convolutional variational autoencoder with a spacial latent space. @@ -157,10 +159,10 @@ class Vanilla3DVAE(BaseVAE): def __init__( self, input_size, - latent_space_size=256, - feature_size=1024, - recons_weight=1, - KL_weight=1, + latent_space_size, + feature_size, + recons_weight, + kl_weight, gpu=True, ): n_conv = 4 @@ -242,7 +244,7 @@ def __init__( DecoderLayer3D( last_layer_channels * 2 ** (i), last_layer_channels * 2 ** (i - 1), - output_padding=decoder_output_padding[-i], + output_padding=decoder_output_padding[i], ) ) decoder_layers.append( @@ -261,7 +263,7 @@ def __init__( ) decoder = nn.Sequential(*decoder_layers) - super(Vanilla3DVAE, self).__init__( + super(Vanilla3DspacialVAE, self).__init__( encoder, decoder, mu_layer, @@ -269,7 +271,7 @@ def __init__( latent_space_size, gpu=gpu, recons_weight=recons_weight, - KL_weight=KL_weight, + kl_weight=kl_weight, is_3D=False, ) @@ -296,28 +298,33 @@ class Vanilla3DdenseVAE(BaseVAE): def __init__( self, - input_size, + size_reduction_factor, latent_space_size=256, feature_size=1024, n_conv=4, io_layer_channels=8, recons_weight=1, - KL_weight=1, + kl_weight=1, gpu=True, ): first_layer_channels = io_layer_channels last_layer_channels = io_layer_channels # automatically compute padding - decoder_output_padding = [ - # [1, 0, 0], - # [0, 0, 0], - # [0, 0, 1], - ] - - input_c = input_size[0] - input_d = input_size[1] - input_h = input_size[2] - input_w = input_size[3] + decoder_output_padding = [] + + if size_reduction_factor == 2: + self.input_size = [1, 80, 96, 80] + elif size_reduction_factor == 3: + self.input_size = [1, 56, 64, 56] + elif size_reduction_factor == 4: + self.input_size = [1, 40, 48, 40] + elif size_reduction_factor == 5: + self.input_size = [1, 32, 40, 32] + + input_c = self.input_size[0] + input_d = self.input_size[1] + input_h = self.input_size[2] + input_w = self.input_size[3] d, h, w = input_d, input_h, input_w # ENCODER @@ -336,6 +343,7 @@ def __init__( # Construct output paddings decoder_output_padding.append([d % 2, h % 2, w % 2]) d, h, w = d // 2, h // 2, w // 2 + # Compute size of the feature space n_pix = ( first_layer_channels * 2 ** (n_conv - 1) @@ -422,17 +430,5 @@ def __init__( gpu=gpu, is_3D=False, recons_weight=recons_weight, - KL_weight=KL_weight, + kl_weight=kl_weight, ) - - @staticmethod - def get_input_size(): - return "1@128x128x128" - - @staticmethod - def get_dimension(): - return "3D" - - @staticmethod - def get_task(): - return ["reconstruction"] diff --git a/clinicadl/utils/preprocessing.py b/clinicadl/utils/preprocessing.py old mode 100644 new mode 100755 index 7ee9af062..b00f55363 --- a/clinicadl/utils/preprocessing.py +++ b/clinicadl/utils/preprocessing.py @@ -1,6 +1,7 @@ import errno import json import os +from pathlib import Path from typing import Any, Dict @@ -23,7 +24,7 @@ def write_preprocessing(preprocessing_dict: Dict[str, Any], caps_directory: str) def read_preprocessing(json_path: str) -> Dict[str, Any]: - if not json_path.endswith(".json"): + if not Path(json_path).name.endswith(".json"): json_path += ".json" if not os.path.isfile(json_path): diff --git a/clinicadl/utils/pytorch_fsim.py b/clinicadl/utils/pytorch_fsim.py new file mode 100644 index 000000000..a87940fb1 --- /dev/null +++ b/clinicadl/utils/pytorch_fsim.py @@ -0,0 +1,409 @@ +### from https://github.com/mikhailiuk/pytorch-fsim + +import torch as pt +import torch.nn as nn +from torch import optim +from torch.autograd import Variable +import torch.nn.functional as F + +import numpy as np +import math +import imageio +from scipy.io import loadmat + +''' +This code is a direct pytorch implementation of the original FSIM code provided by +Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang in Matlab. For the original version +please see: + +https://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm + +''' + + +class FSIM_base(nn.Module): + + def __init__(self): + nn.Module.__init__(self) + self.cuda_computation = False + self.nscale = 4 # Number of wavelet scales + self.norient = 4 # Number of filter orientations + self.k = 2.0 # No of standard deviations of the noise + # energy beyond the mean at which we set the + # noise threshold point. + # below which phase congruency values get + # penalized. + + self.epsilon = .0001 # Used to prevent division by zero + self.pi = math.pi + + minWaveLength = 6 # Wavelength of smallest scale filter + mult = 2 # Scaling factor between successive filters + sigmaOnf = 0.55 # Ratio of the standard deviation of the + # Gaussian describing the log Gabor filter's + # transfer function in the frequency domain + # to the filter center frequency. + dThetaOnSigma = 1.2 # Ratio of angular interval between filter orientations + # and the standard deviation of the angular Gaussian + # function used to construct filters in the + # freq. plane. + + self.thetaSigma = self.pi/self.norient/dThetaOnSigma # Calculate the standard deviation of the + # angular Gaussian function used to + # construct filters in the freq. plane. + + + self.fo = (1.0/(minWaveLength*pt.pow(mult,(pt.arange(0,self.nscale,dtype=pt.float64))))).unsqueeze(0) # Centre frequency of filter + self.den = 2*(math.log(sigmaOnf))**2 + self.dx = -pt.tensor([[[[3, 0, -3], [10, 0,-10], [3,0,-3]]]])/16.0 + self.dy = -pt.tensor([[[[3, 10, 3], [0, 0, 0], [-3 ,-10, -3]]]])/16.0 + self.T1 = 0.85 + self.T2 = 160 + self.T3 = 200; + self.T4 = 200; + self.lambdac = 0.03 + + def set_arrays_to_cuda(self): + self.cuda_computation = True + self.fo = self.fo.cuda() + self.dx = self.dx.cuda() + self.dy = self.dy.cuda() + + def forward_gradloss(self,imgr,imgd): + I1,Q1,Y1 = self.process_image_channels(imgr) + I2,Q2,Y2 = self.process_image_channels(imgd) + + + #PCSimMatrix,PCm = self.calculate_phase_score(PC1,PC2) + gradientMap1 = self.calculate_gradient_map(Y1) + gradientMap2 = self.calculate_gradient_map(Y2) + + gradientSimMatrix = self.calculate_gradient_sim(gradientMap1,gradientMap2) + #gradientSimMatrix= gradientSimMatrix.view(PCSimMatrix.size()) + gradloss = pt.sum(pt.sum(pt.sum(gradientSimMatrix,1),1)) + return gradloss + + def calculate_fsim(self,gradientSimMatrix,PCSimMatrix,PCm): + SimMatrix = gradientSimMatrix * PCSimMatrix * PCm + FSIM = pt.sum(pt.sum(SimMatrix,1),1) / pt.sum(pt.sum(PCm,1),1) + return FSIM + + def calculate_fsimc(self, I1,Q1,I2,Q2,gradientSimMatrix,PCSimMatrix,PCm): + + ISimMatrix = (2*I1*I2 + self.T3) / (pt.pow(I1,2) + pt.pow(I2,2) + self.T3) + QSimMatrix = (2*Q1*Q2 + self.T4) / (pt.pow(Q1,2) + pt.pow(Q2,2) + self.T4) + SimMatrixC = gradientSimMatrix*PCSimMatrix*(pt.pow(pt.abs(ISimMatrix*QSimMatrix),self.lambdac))*PCm + FSIMc = pt.sum(pt.sum(SimMatrixC,1),1)/pt.sum(pt.sum(PCm,1),1) + + return FSIMc + + def lowpassfilter(self, rows, cols): + cutoff = .45 + n = 15 + x, y = self.create_meshgrid(cols,rows) + radius = pt.sqrt(pt.pow(x,2) + pt.pow(y,2)).unsqueeze(0) + f = self.ifftshift2d( 1 / (1.0 + pt.pow(pt.div(radius,cutoff),2*n)) ) + return f + + def calculate_gradient_sim(self,gradientMap1,gradientMap2): + + gradientSimMatrix = (2*gradientMap1*gradientMap2 + self.T2) /(pt.pow(gradientMap1,2) + pt.pow(gradientMap2,2) + self.T2) + return gradientSimMatrix + + def calculate_gradient_map(self,Y): + IxY = F.conv2d(Y,self.dx, padding=1) + IyY = F.conv2d(Y,self.dy, padding=1) + gradientMap1 = pt.sqrt(pt.pow(IxY,2) + pt.pow(IyY,2)) + return gradientMap1 + + def calculate_phase_score(self,PC1,PC2): + PCSimMatrix = (2 * PC1 * PC2 + self.T1) / (pt.pow(PC1,2) + pt.pow(PC2,2) + self.T1) + PCm = pt.where(PC1>PC2, PC1,PC2) + return PCSimMatrix,PCm + + def roll_1(self,x, n): + return pt.cat((x[:,-n:,:,:,:], x[:,:-n,:,:,:]), dim=1) + + def ifftshift(self,tens,var_axis): + len11 = int(tens.size()[var_axis]/2) + len12 = tens.size()[var_axis]-len11 + return pt.cat((tens.narrow(var_axis,len11,len12),tens.narrow(var_axis,0,len11)),axis=var_axis) + + def ifftshift2d(self,tens): + return self.ifftshift(self.ifftshift(tens,1),2) + + def create_meshgrid(self,cols,rows): + ''' + Set up X and Y matrices with ranges normalised to +/- 0.5 + The following code adjusts things appropriately for odd and even values + of rows and columns. + ''' + + if cols%2: + xrange = pt.arange(start = -(cols-1)/2, end = (cols-1)/2+1, step = 1, requires_grad=False)/(cols-1) + else: + xrange = pt.arange(-(cols)/2, (cols)/2, step = 1, requires_grad=False)/(cols) + + if rows%2: + yrange = pt.arange(-(rows-1)/2, (rows-1)/2+1, step = 1, requires_grad=False)/(rows-1) + else: + yrange = pt.arange(-(rows)/2, (rows)/2, step = 1, requires_grad=False)/(rows) + + x, y = pt.meshgrid([xrange, yrange]) + + if self.cuda_computation: + x, y = x.cuda(), y.cuda() + + return x.T, y.T + + def process_image_channels(self,img): + + + batch, rows, cols = img.shape[0],img.shape[2],img.shape[3] + + minDimension = min(rows,cols) + + Ycoef = pt.tensor([[0.299,0.587,0.114]]) + Icoef = pt.tensor([[0.596,-0.274,-0.322]]) + Qcoef = pt.tensor([[0.211,-0.523,0.312]]) + + if self.cuda_computation: + Ycoef, Icoef, Qcoef = Ycoef.cuda(), Icoef.cuda(), Qcoef.cuda() + + Yfilt=pt.cat(batch*[pt.cat(rows*cols*[Ycoef.unsqueeze(2)],dim=2).view(1,3,rows,cols)],0) + Ifilt=pt.cat(batch*[pt.cat(rows*cols*[Icoef.unsqueeze(2)],dim=2).view(1,3,rows,cols)],0) + Qfilt=pt.cat(batch*[pt.cat(rows*cols*[Qcoef.unsqueeze(2)],dim=2).view(1,3,rows,cols)],0) + + # If images have three chanels + if img.size()[1]==3: + Y = pt.sum(Yfilt*img,1).unsqueeze(1) + I = pt.sum(Ifilt*img,1).unsqueeze(1) + Q = pt.sum(Qfilt*img,1).unsqueeze(1) + else: + Y = pt.mean(img,1).unsqueeze(1) + I = pt.ones(Y.size(),dtype=pt.float64) + Q = pt.ones(Y.size(),dtype=pt.float64) + + F = max(1,round(minDimension / 256)) + + aveKernel = nn.AvgPool2d(kernel_size = F, stride = F, padding =0)# max(0, math.floor(F/2))) + if self.cuda_computation: + aveKernel = aveKernel.cuda() + + # Make sure that the dimension of the returned image is the same as the input + I = aveKernel(I) + Q = aveKernel(Q) + Y = aveKernel(Y) + return I,Q,Y + + + def phasecong2(self,img): + ''' + % Filters are constructed in terms of two components. + % 1) The radial component, which controls the frequency band that the filter + % responds to + % 2) The angular component, which controls the orientation that the filter + % responds to. + % The two components are multiplied together to construct the overall filter. + + % Construct the radial filter components... + + % First construct a low-pass filter that is as large as possible, yet falls + % away to zero at the boundaries. All log Gabor filters are multiplied by + % this to ensure no extra frequencies at the 'corners' of the FFT are + % incorporated as this seems to upset the normalisation process when + % calculating phase congrunecy. + ''' + + batch, rows, cols = img.shape[0],img.shape[2],img.shape[3] + + imagefft = pt.rfft(img,signal_ndim=2,onesided=False) + + x, y = self.create_meshgrid(cols,rows) + + radius = pt.cat(batch*[pt.sqrt(pt.pow(x,2) + pt.pow(y,2)).unsqueeze(0)],0) + theta = pt.cat(batch*[pt.atan2(-y,x).unsqueeze(0)],0) + + radius = self.ifftshift2d(radius) # Matrix values contain *normalised* radius from centre + theta = self.ifftshift2d(theta) # Matrix values contain polar angle. + # (note -ve y is used to give +ve + # anti-clockwise angles) + + radius[:,0,0] = 1 + + sintheta = pt.sin(theta) + costheta = pt.cos(theta) + + lp = self.lowpassfilter(rows,cols) # Radius .45, 'sharpness' 15 + lp = pt.cat(batch*[lp.unsqueeze(0)],0) + + term1 = pt.cat(rows*cols*[self.fo.unsqueeze(2)],dim=2).view(-1,self.nscale,rows,cols) + term1 = pt.cat(batch*[term1.unsqueeze(0)],0).view(-1,self.nscale,rows,cols) + + term2 = pt.log(pt.cat(self.nscale*[radius.unsqueeze(1)],1)/term1) + # Apply low-pass filter + logGabor = pt.exp(-pt.pow(term2,2)/self.den) + logGabor = logGabor*lp + logGabor[:,:,0,0] = 0 # Set the value at the 0 frequency point of the filter + # back to zero (undo the radius fudge). + + # Then construct the angular filter components... + + # For each point in the filter matrix calculate the angular distance from + # the specified filter orientation. To overcome the angular wrap-around + # problem sine difference and cosine difference values are first computed + # and then the atan2 function is used to determine angular distance. + angl = pt.arange(0,self.norient,dtype=pt.float64)/self.norient*self.pi + + if self.cuda_computation: + angl = angl.cuda() + ds_t1 = pt.cat(self.norient*[sintheta.unsqueeze(1)],1)*pt.cos(angl).view(-1,self.norient,1,1) + ds_t2 = pt.cat(self.norient*[costheta.unsqueeze(1)],1)*pt.sin(angl).view(-1,self.norient,1,1) + dc_t1 = pt.cat(self.norient*[costheta.unsqueeze(1)],1)*pt.cos(angl).view(-1,self.norient,1,1) + dc_t2 = pt.cat(self.norient*[sintheta.unsqueeze(1)],1)*pt.sin(angl).view(-1,self.norient,1,1) + ds = ds_t1-ds_t2 # Difference in sine. + dc = dc_t1+dc_t2 # Difference in cosine. + dtheta = pt.abs(pt.atan2(ds,dc)) # Absolute angular distance. + spread = pt.exp(-pt.pow(dtheta,2)/(2*self.thetaSigma**2)) # Calculate the + # angular filter component. + + logGabor_rep = pt.repeat_interleave(logGabor,self.norient,1).view(-1,self.nscale,self.norient,rows,cols) + + # Batch size, scale, orientation, pixels, pixels + spread_rep = pt.cat(self.nscale*[spread]).view(-1,self.nscale,self.norient,rows,cols) + filter_log_spread = logGabor_rep*spread_rep + array_of_zeros = pt.zeros(filter_log_spread.unsqueeze(5).size(),dtype=pt.float64) + if self.cuda_computation: + array_of_zeros = array_of_zeros.cuda() + filter_log_spread_zero = pt.cat((filter_log_spread.unsqueeze(5),array_of_zeros), dim=5) + ifftFilterArray = pt.ifft(filter_log_spread_zero,signal_ndim =2).select(5,0)*math.sqrt(rows*cols) + + imagefft_repeat = pt.cat(self.nscale*self.norient*[imagefft],dim=1).view(-1,self.nscale,self.norient,rows,cols,2) + filter_log_spread_repeat = pt.cat(2*[filter_log_spread.unsqueeze(5)],dim=5) + # Convolve image with even and odd filters returning the result in EO + EO = pt.ifft(filter_log_spread_repeat*imagefft_repeat,signal_ndim=2) + + E = EO.select(5, 0) + O = EO.select(5, 1) + An = pt.sqrt(pt.pow(E,2)+pt.pow(O,2)) + sumAn_ThisOrient = pt.sum(An,1) + sumE_ThisOrient = pt.sum(E,1) # Sum of even filter convolution results + sumO_ThisOrient = pt.sum(O,1) # Sum of odd filter convolution results. + + # Get weighted mean filter response vector, this gives the weighted mean + # phase angle. + XEnergy = pt.sqrt(pt.pow(sumE_ThisOrient,2) + pt.pow(sumO_ThisOrient,2)) + self.epsilon + MeanE = sumE_ThisOrient / XEnergy + MeanO = sumO_ThisOrient / XEnergy + + MeanO = pt.cat(self.nscale*[MeanO.unsqueeze(1)],1) + MeanE = pt.cat(self.nscale*[MeanE.unsqueeze(1)],1) + + + # Now calculate An(cos(phase_deviation) - | sin(phase_deviation)) | by + # using dot and cross products between the weighted mean filter response + # vector and the individual filter response vectors at each scale. This + # quantity is phase congruency multiplied by An, which we call energy. + Energy = pt.sum( E*MeanE+O*MeanO - pt.abs(E*MeanO-O*MeanE),1) + abs_EO = pt.sqrt(pt.pow(E,2) + pt.pow(O,2)) + + # % Compensate for noise + # We estimate the noise power from the energy squared response at the + # smallest scale. If the noise is Gaussian the energy squared will have a + # Chi-squared 2DOF pdf. We calculate the median energy squared response + # as this is a robust statistic. From this we estimate the mean. + # The estimate of noise power is obtained by dividing the mean squared + # energy value by the mean squared filter value + medianE2n = pt.pow(abs_EO.select(1,0),2).view(-1,self.norient,rows*cols).median(2).values + + EM_n = pt.sum(pt.sum(pt.pow(filter_log_spread.select(1,0),2),3),2) + noisePower = -(medianE2n/math.log(0.5))/EM_n + + # Now estimate the total energy^2 due to noise + # Estimate for sum(An^2) + sum(Ai.*Aj.*(cphi.*cphj + sphi.*sphj)) + EstSumAn2 = pt.sum(pt.pow(ifftFilterArray,2),1) + + sumEstSumAn2 = pt.sum(pt.sum(EstSumAn2,2),2) + roll_t1 = ifftFilterArray*self.roll_1(ifftFilterArray,1) + roll_t2 = ifftFilterArray*self.roll_1(ifftFilterArray,2) + roll_t3 = ifftFilterArray*self.roll_1(ifftFilterArray,3) + rolling_mult = roll_t1+roll_t2+roll_t3 + EstSumAiAj = pt.sum(rolling_mult,1)/2 + sumEstSumAiAj = pt.sum(pt.sum(EstSumAiAj,2),2) + + EstNoiseEnergy2 = 2*noisePower*sumEstSumAn2+4*noisePower*sumEstSumAiAj + tau = pt.sqrt(EstNoiseEnergy2/2) + EstNoiseEnergy = tau*math.sqrt(self.pi/2) + EstNoiseEnergySigma = pt.sqrt( (2-self.pi/2)*pt.pow(tau,2)) + + + # The estimated noise effect calculated above is only valid for the PC_1 measure. + # The PC_2 measure does not lend itself readily to the same analysis. However + # empirically it seems that the noise effect is overestimated roughly by a factor + # of 1.7 for the filter parameters used here. + T = (EstNoiseEnergy + self.k*EstNoiseEnergySigma)/1.7 # Noise threshold + + T_exp = pt.cat(rows*cols*[T.unsqueeze(2)],dim=2).view(-1,self.norient,rows,cols) + AnAll = pt.sum(sumAn_ThisOrient,1) + array_of_zeros_energy = pt.zeros(Energy.size(),dtype=pt.float64) + if self.cuda_computation: + array_of_zeros_energy =array_of_zeros_energy.cuda() + + EnergyAll = pt.sum(pt.where((Energy - T_exp)<0.0, array_of_zeros_energy,Energy - T_exp ),1) + ResultPC = EnergyAll/AnAll + + return ResultPC + +class FSIM(FSIM_base): + ''' + Note, the input is expected to be from 0 to 255 + ''' + + def __init__(self): + super().__init__() + + def forward(self,imgr,imgd): + if imgr.is_cuda: + self.set_arrays_to_cuda() + + I1,Q1,Y1 = self.process_image_channels(imgr) + I2,Q2,Y2 = self.process_image_channels(imgd) + PC1 = self.phasecong2(Y1) + PC2 = self.phasecong2(Y2) + + PCSimMatrix,PCm = self.calculate_phase_score(PC1,PC2) + gradientMap1 = self.calculate_gradient_map(Y1) + gradientMap2 = self.calculate_gradient_map(Y2) + + gradientSimMatrix = self.calculate_gradient_sim(gradientMap1,gradientMap2) + gradientSimMatrix= gradientSimMatrix.view(PCSimMatrix.size()) + FSIM = self.calculate_fsim(gradientSimMatrix,PCSimMatrix,PCm) + + return FSIM.mean() + +class FSIMc(FSIM_base, nn.Module): + ''' + Note, the input is expected to be from 0 to 255 + ''' + def __init__(self): + super().__init__() + + def forward(self,imgr,imgd): + if imgr.is_cuda: + self.set_arrays_to_cuda() + + + I1,Q1,Y1 = self.process_image_channels(imgr) + I2,Q2,Y2 = self.process_image_channels(imgd) + PC1 = self.phasecong2(Y1) + PC2 = self.phasecong2(Y2) + + PCSimMatrix,PCm = self.calculate_phase_score(PC1,PC2) + gradientMap1 = self.calculate_gradient_map(Y1) + gradientMap2 = self.calculate_gradient_map(Y2) + + gradientSimMatrix = self.calculate_gradient_sim(gradientMap1,gradientMap2) + gradientSimMatrix= gradientSimMatrix.view(PCSimMatrix.size()) + FSIMc = self.calculate_fsimc(I1.squeeze(),Q1.squeeze(),I2.squeeze(),Q2.squeeze(),gradientSimMatrix,PCSimMatrix,PCm) + + return FSIMc.mean() \ No newline at end of file diff --git a/clinicadl/utils/pytorch_msssim.py b/clinicadl/utils/pytorch_msssim.py new file mode 100644 index 000000000..d7b8178ab --- /dev/null +++ b/clinicadl/utils/pytorch_msssim.py @@ -0,0 +1,133 @@ +# Code coming from https://github.com/jorge-pessoa/pytorch-msssim/blob/dev/pytorch_msssim/ + +import numpy as np +import torch +import torch.nn.functional as F + + +class MSSSIM(torch.nn.Module): + def __init__(self, window_size=11): + super(MSSSIM, self).__init__() + self.window_size = window_size + + def _gaussian(self, sigma): + gauss = torch.Tensor( + [ + np.exp(-((x - self.window_size // 2) ** 2) / float(2 * sigma**2)) + for x in range(self.window_size) + ] + ) + return gauss / gauss.sum() + + def _create_window(self, n_dim, channel=1): + _1D_window = self._gaussian(1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float() + + _3D_window = ( + torch.stack([_2D_window * x for x in _1D_window], dim=2) + .float() + .unsqueeze(0) + .unsqueeze(0) + ) + _2D_window = _2D_window.unsqueeze(0).unsqueeze(0) + + if n_dim == 3: + return _3D_window.expand( + channel, 1, self.window_size, self.window_size, self.window_size + ).contiguous() + else: + return _2D_window.expand( + channel, 1, self.window_size, self.window_size + ).contiguous() + + def ssim(self, img1: torch.Tensor, img2: torch.Tensor): + n_dim = len(img1.shape) - 2 + padd = int(self.window_size / 2) + + if n_dim == 2: + (_, channel, height, width) = img1.size() + convFunction = F.conv2d + elif n_dim == 3: + (_, channel, height, width, depth) = img1.size() + convFunction = F.conv3d + + window = self._create_window(n_dim=n_dim, channel=channel).to(img1.device) + + mu1 = convFunction(img1, window, padding=padd, groups=channel) + mu2 = convFunction(img2, window, padding=padd, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = ( + convFunction(img1 * img1, window, padding=padd, groups=channel) - mu1_sq + ) + sigma2_sq = ( + convFunction(img2 * img2, window, padding=padd, groups=channel) - mu2_sq + ) + sigma12 = ( + convFunction(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2 + ) + + L = 1 # data already in [0, 1] + C1 = (0.01 * L) ** 2 + C2 = (0.03 * L) ** 2 + + luminance = torch.mean((2 * mu1 * mu2 + C1) / (mu1_sq + mu2_sq + C1)) + contrastxstructure = torch.mean((2.0 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2)) + + return luminance, contrastxstructure + + def forward(self, y, y_pred): + img1 = torch.from_numpy(y)[None, ...] + img2 = torch.from_numpy(y_pred)[None, ...] + + if torch.cuda.is_available(): + img1 = img1.to(device="cuda") + img2 = img2.to(device="cuda") + + n_dim = len(img1.shape) - 2 + if min(img1.shape[-n_dim:]) < 4: + weights = torch.FloatTensor([1.0]).to(img1.device) + + elif min(img1.shape[-n_dim:]) < 8: + weights = torch.FloatTensor([0.3222, 0.6778]).to(img1.device) + + elif min(img1.shape[-n_dim:]) < 16: + weights = torch.FloatTensor([0.4558, 0.1633, 0.3809]).to(img1.device) + + elif min(img1.shape[-n_dim:]) < 32: + weights = torch.FloatTensor([0.3117, 0.3384, 0.2675, 0.0824]).to( + img1.device + ) + else: + weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to( + img1.device + ) + + levels = weights.size()[0] + ml = [] + mcs = [] + + pool_size = [2] * n_dim + if n_dim == 2: + pool_function = F.avg_pool2d + elif n_dim == 3: + pool_function = F.avg_pool3d + + for _ in range(levels): + l, cs = self.ssim(img1, img2) + ml.append(l) + mcs.append(cs) + + img1 = pool_function(img1, pool_size) + img2 = pool_function(img2, pool_size) + + ml = torch.stack(ml) + mcs = torch.stack(mcs) + + prod = torch.prod(mcs**weights) + + output = prod * (ml[-1]**weights[-1]) + return output \ No newline at end of file diff --git a/clinicadl/utils/pytorch_ssim.py b/clinicadl/utils/pytorch_ssim.py new file mode 100755 index 000000000..e9b07da30 --- /dev/null +++ b/clinicadl/utils/pytorch_ssim.py @@ -0,0 +1,197 @@ +## From https://github.com/jinh0park/pytorch-ssim-3D + +from math import exp + +import torch +import torch.nn.functional as F +from torch.autograd import Variable + + +def gaussian(window_size, sigma): + gauss = torch.Tensor( + [ + exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) + for x in range(window_size) + ] + ) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable( + _2D_window.expand(channel, 1, window_size, window_size).contiguous() + ) + return window + + +def create_window_3D(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()) + _3D_window = ( + _1D_window.mm(_2D_window.reshape(1, -1)) + .reshape(window_size, window_size, window_size) + .float() + .unsqueeze(0) + .unsqueeze(0) + ) + window = Variable( + _3D_window.expand( + channel, 1, window_size, window_size, window_size + ).contiguous() + ) + return window + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = ( + F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + ) + sigma2_sq = ( + F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + ) + sigma12 = ( + F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) + - mu1_mu2 + ) + + C1 = 0.01**2 + C2 = 0.03**2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) + ) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + + +def _ssim_3D(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv3d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv3d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + + mu1_mu2 = mu1 * mu2 + + sigma1_sq = ( + F.conv3d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + ) + sigma2_sq = ( + F.conv3d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + ) + sigma12 = ( + F.conv3d(img1 * img2, window, padding=window_size // 2, groups=channel) + - mu1_mu2 + ) + + C1 = 0.01**2 + C2 = 0.03**2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) + ) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + + +class SSIM(torch.nn.Module): + def __init__(self, window_size=11, size_average=True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window(window_size, self.channel) + + def forward(self, img1, img2): + (_, channel, _, _) = img1.shape + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = create_window(self.window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return _ssim(img1, img2, window, self.window_size, channel, self.size_average) + + +class SSIM3D(torch.nn.Module): + def __init__(self, window_size=11, size_average=True): + super(SSIM3D, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window_3D(window_size, self.channel) + + def forward(self, img1, img2): + (_, channel, _, _, _) = img1.shape + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = create_window_3D(self.window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return _ssim_3D( + img1, img2, window, self.window_size, channel, self.size_average + ) + + +def ssim(y, y_pred, window_size=11, size_average=True): + img1 = torch.from_numpy(y)[None, ...] + img2 = torch.from_numpy(y_pred)[None, ...] + (_, channel, _, _) = img1.shape + window = create_window(window_size, channel) + + if torch.cuda.is_available(): + img1 = img1.to(device="cuda", memory_format=torch.channels_last) + img2 = img2.to(device="cuda", memory_format=torch.channels_last) + + if img1.is_cuda: + window = window.to(device=img1.device, memory_format=torch.channels_last) + window = window.type_as(img1) + + return _ssim(img1, img2, window, window_size, channel, size_average) + + +def ssim3D(y, y_pred, window_size=11, size_average=True): + img1 = torch.from_numpy(y)[None, :, :, :, :] # add a dimension + img2 = torch.from_numpy(y_pred)[None, :, :, :, :] + (_, channel, _, _, _) = img1.shape + window = create_window_3D(window_size, channel) + + if torch.cuda.is_available(): + img1 = img1.cuda() + img2 = img2.cuda() + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + return _ssim_3D(img1, img2, window, window_size, channel, size_average) \ No newline at end of file diff --git a/clinicadl/utils/seed.py b/clinicadl/utils/seed.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/split_manager/__init__.py b/clinicadl/utils/split_manager/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/split_manager/kfold.py b/clinicadl/utils/split_manager/kfold.py old mode 100644 new mode 100755 index 0481d5008..8407e6cb8 --- a/clinicadl/utils/split_manager/kfold.py +++ b/clinicadl/utils/split_manager/kfold.py @@ -1,4 +1,4 @@ -from os import path +from pathlib import Path from clinicadl.utils.split_manager.split_manager import SplitManager @@ -38,11 +38,7 @@ def split_iterator(self): else: return self.split_list - def _get_tsv_paths(self, cohort_path, split): - train_path = path.join( - cohort_path, f"train_splits-{self.n_splits}", f"split-{split}" - ) - valid_path = path.join( - cohort_path, f"validation_splits-{self.n_splits}", f"split-{split}" - ) + def _get_tsv_paths(self, cohort_path: Path, split): + train_path = cohort_path / f"split-{split}" + valid_path = cohort_path / f"split-{split}" return train_path, valid_path diff --git a/clinicadl/utils/split_manager/single_split.py b/clinicadl/utils/split_manager/single_split.py old mode 100644 new mode 100755 index 89c44f430..95b4881a0 --- a/clinicadl/utils/split_manager/single_split.py +++ b/clinicadl/utils/split_manager/single_split.py @@ -1,4 +1,4 @@ -from os import path +from pathlib import Path from clinicadl.utils.split_manager.split_manager import SplitManager @@ -30,7 +30,8 @@ def allowed_splits_list(self): def split_iterator(self): return range(1) - def _get_tsv_paths(self, cohort_path, split): - train_path = path.join(cohort_path, "train") - valid_path = path.join(cohort_path, "validation") + def _get_tsv_paths(self, cohort_path: Path, split=None): + train_path = cohort_path + valid_path = cohort_path + return train_path, valid_path diff --git a/clinicadl/utils/split_manager/split_manager.py b/clinicadl/utils/split_manager/split_manager.py old mode 100644 new mode 100755 index e26750a39..a139fd123 --- a/clinicadl/utils/split_manager/split_manager.py +++ b/clinicadl/utils/split_manager/split_manager.py @@ -1,29 +1,44 @@ import abc from logging import getLogger -from os import path +from pathlib import Path import pandas as pd -from clinica.utils.inputs import check_caps_folder +from clinicadl.utils.clinica_utils import check_caps_folder from clinicadl.utils.exceptions import ( ClinicaDLArgumentError, ClinicaDLConfigurationError, ClinicaDLTSVError, ) -logger = getLogger("clinicadl") +logger = getLogger("clinicadl.split_manager") class SplitManager: def __init__( self, - caps_directory, - tsv_path, + caps_directory: Path, + tsv_path: Path, diagnoses, baseline=False, multi_cohort=False, split_list=None, ): + """ + Parameters + ---------- + caps_director: str (path) + Path to the caps directory + tsv_path: str + Path to the tsv that is going to be split + diagonoses: List[str] + List of diagnosis + baseline: bool + if True, split only on baseline sessions + multi-cohort: bool + split_list: List[str] + """ + tsv_path = Path(tsv_path) self._check_tsv_path(tsv_path, multi_cohort) self.tsv_path = tsv_path self.caps_dict = self._create_caps_dict(caps_directory, multi_cohort) @@ -46,7 +61,6 @@ def __len__(self): def allowed_splits_list(self): """ List of possible splits if no restriction was applied - Returns: list[int]: list of all possible splits """ @@ -55,7 +69,6 @@ def allowed_splits_list(self): def __getitem__(self, item): """ Returns a dictionary of DataFrames with train and validation data. - Args: item (int): Index of the split wanted. Returns: @@ -76,6 +89,7 @@ def __getitem__(self, item): ) if bool(set(cohort_diagnoses) & set(self.diagnoses)): target_diagnoses = list(set(cohort_diagnoses) & set(self.diagnoses)) + cohort_train_df, cohort_valid_df = self.concatenate_diagnoses( item, cohort_path=cohort_path, cohort_diagnoses=target_diagnoses ) @@ -104,35 +118,80 @@ def __getitem__(self, item): "validation": valid_df, } - def concatenate_diagnoses(self, split, cohort_path=None, cohort_diagnoses=None): + def concatenate_diagnoses( + self, split, cohort_path: Path = None, cohort_diagnoses=None + ): """Concatenated the diagnoses needed to form the train and validation sets.""" - train_df, valid_df = pd.DataFrame(), pd.DataFrame() - train_path, valid_path = self._get_tsv_paths( - split=split, cohort_path=cohort_path if cohort_path is not None else self.tsv_path, + split=split, ) logger.debug(f"Training data loaded at {train_path}") logger.debug(f"Validation data loaded at {valid_path}") if cohort_diagnoses is None: cohort_diagnoses = self.diagnoses - for diagnosis in cohort_diagnoses: - if self.baseline: - train_diagnosis_path = path.join( - train_path, diagnosis + "_baseline.tsv" - ) - else: - train_diagnosis_path = path.join(train_path, diagnosis + ".tsv") + if self.baseline: + train_path = train_path / "train_baseline.tsv" + else: + train_path = train_path / "train.tsv" + + valid_path = valid_path / "validation_baseline.tsv" - valid_diagnosis_path = path.join(valid_path, diagnosis + "_baseline.tsv") + train_df = pd.read_csv(train_path, sep="\t") + valid_df = pd.read_csv(valid_path, sep="\t") - train_diagnosis_df = pd.read_csv(train_diagnosis_path, sep="\t") - valid_diagnosis_df = pd.read_csv(valid_diagnosis_path, sep="\t") + list_columns = train_df.columns.values - train_df = pd.concat([train_df, train_diagnosis_df]) - valid_df = pd.concat([valid_df, valid_diagnosis_df]) + if ( + "diagnosis" + not in list_columns + # or "age" not in list_columns + # or "sex" not in list_columns + ): + parents_path = train_path.resolve().parent + while ( + not (parents_path / "labels.tsv").is_file() + and ((parents_path / "kfold.json").is_file()) + or (parents_path / "split.json").is_file() + ): + parents_path = parents_path.parent + try: + labels_df = pd.read_csv(parents_path / "labels.tsv", sep="\t") + train_df = pd.merge( + train_df, + labels_df, + how="inner", + on=["participant_id", "session_id"], + ) + except: + pass + + list_columns = valid_df.columns.values + if ( + "diagnosis" + not in list_columns + # or "age" not in list_columns + # or "sex" not in list_columns + ): + parents_path = valid_path.resolve().parent + while ( + not (parents_path / "labels.tsv").is_file() + and ((parents_path / "kfold.json").is_file()) + or (parents_path / "split.json").is_file() + ): + parents_path = parents_path.parent + try: + labels_df = pd.read_csv(parents_path / "labels.tsv", sep="\t") + valid_df = pd.merge( + valid_df, + labels_df, + how="inner", + on=["participant_id", "session_id"], + ) + except: + pass train_df.reset_index(inplace=True, drop=True) valid_df.reset_index(inplace=True, drop=True) @@ -143,7 +202,6 @@ def concatenate_diagnoses(self, split, cohort_path=None, cohort_diagnoses=None): def _get_tsv_paths(self, cohort_path, split): """ Computes the paths to the TSV files needed depending on the split structure. - Args: cohort_path (str): path to the split structure of a cohort. split (int): Index of the split. @@ -165,9 +223,9 @@ def _check_item(self, item): ) @staticmethod - def _create_caps_dict(caps_directory, multi_cohort): + def _create_caps_dict(caps_directory: Path, multi_cohort): if multi_cohort: - if not caps_directory.endswith(".tsv"): + if not caps_directory.suffix == ".tsv": raise ClinicaDLArgumentError( "If multi_cohort is given, the CAPS_DIRECTORY argument should be a path to a TSV file." ) @@ -189,7 +247,7 @@ def _create_caps_dict(caps_directory, multi_cohort): @staticmethod def _check_tsv_path(tsv_path, multi_cohort): if multi_cohort: - if not tsv_path.endswith(".tsv"): + if not tsv_path.suffix == ".tsv": raise ClinicaDLArgumentError( "If multi_cohort is given, the TSV_DIRECTORY argument should be a path to a TSV file." ) @@ -197,7 +255,7 @@ def _check_tsv_path(tsv_path, multi_cohort): tsv_df = pd.read_csv(tsv_path, sep="\t") SplitManager._check_multi_cohort_tsv(tsv_df, "labels") else: - if tsv_path.endswith(".tsv"): + if tsv_path.suffix == ".tsv": raise ClinicaDLConfigurationError( f"You gave the path to a TSV file in tsv_path {tsv_path}. " f"To use multi-cohort framework, please add 'multi_cohort=true' to the configuration file or the --multi_cohort flag." diff --git a/clinicadl/utils/task_manager/__init__.py b/clinicadl/utils/task_manager/__init__.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/task_manager/classification.py b/clinicadl/utils/task_manager/classification.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/task_manager/reconstruction.py b/clinicadl/utils/task_manager/reconstruction.py old mode 100644 new mode 100755 index 6107c3448..d09deb252 --- a/clinicadl/utils/task_manager/reconstruction.py +++ b/clinicadl/utils/task_manager/reconstruction.py @@ -4,6 +4,7 @@ from clinicadl.utils.exceptions import ClinicaDLArgumentError from clinicadl.utils.task_manager.task_manager import TaskManager +import pandas as pd class ReconstructionManager(TaskManager): def __init__( @@ -21,14 +22,14 @@ def columns(self): @property def evaluation_metrics(self): - return ["MSE", "MAE", "PSNR"] + return ["MSE", "MAE", "PSNR", "SSIM", "MSSSIM"] @property def save_outputs(self): return True def generate_test_row(self, idx, data, outputs): - y = data["image"][idx] + y = data["data"][idx] y_pred = outputs[idx].cpu() metrics = self.metrics_module.apply(y, y_pred) row = [ @@ -39,12 +40,53 @@ def generate_test_row(self, idx, data, outputs): for metric in self.evaluation_metrics: row.append(metrics[metric]) return [row] + + def generate_test_row_sample_latent(self, idx, sample_latent_idx, data, outputs): + y = data["data"][idx] + y_pred = outputs[idx].cpu() + metrics = self.metrics_module.apply(y, y_pred) + row = [ + data["participant_id"][idx], + data["session_id"][idx], + data[f"{self.mode}_id"][idx].item(), + sample_latent_idx, + ] + for metric in self.evaluation_metrics: + row.append(metrics[metric]) + return [row] def compute_metrics(self, results_df): - metrics = dict() + # metrics = dict() + # for metric in self.evaluation_metrics: + # metrics[metric] = results_df[metric].mean() + # return metrics + return results_df.describe() + + def compute_metrics_sample_latent(self, sample_latent_results_df): + grouped = sample_latent_results_df.groupby(['participant_id', 'session_id', f'{self.mode}_id']) + + metrics_data = { + 'participant_id': [], + 'session_id': [], + f'{self.mode}_id': [] + } + for metric in self.evaluation_metrics: - metrics[metric] = results_df[metric].mean() - return metrics + metrics_data[f'{metric}_mean'] = [] + metrics_data[f'{metric}_std'] = [] + + for name, group in grouped: + participant_id, session_id, image_id = name + + metrics_data['participant_id'].append(participant_id) + metrics_data['session_id'].append(session_id) + metrics_data['image_id'].append(image_id) + + for metric in self.evaluation_metrics: + metrics_data[f'{metric}_mean'].append(group[metric].mean()) + metrics_data[f'{metric}_std'].append(group[metric].std()) + + return pd.DataFrame(metrics_data) @staticmethod def output_size(input_size, df, label): @@ -102,6 +144,9 @@ def get_criterion(criterion=None): "BCEWithLogitsLoss", "HuberLoss", "SmoothL1Loss", + "VAEGaussianLoss", + "VAEBernoulliLoss", + "VAEContinuousBernoulliLoss", ] if criterion is None: return nn.MSELoss() @@ -109,6 +154,18 @@ def get_criterion(criterion=None): raise ClinicaDLArgumentError( f"Reconstruction loss must be chosen in {compatible_losses}." ) + if criterion == "VAEGaussianLoss": + from clinicadl.utils.network.vae.vae_utils import VAEGaussianLoss + + return VAEGaussianLoss + elif criterion == "VAEBernoulliLoss": + from clinicadl.utils.network.vae.vae_utils import VAEBernoulliLoss + + return VAEBernoulliLoss + elif criterion == "VAEContinuousBernoulliLoss": + from clinicadl.utils.network.vae.vae_utils import VAEContinuousBernoulliLoss + + return VAEContinuousBernoulliLoss return getattr(nn, criterion)() @staticmethod diff --git a/clinicadl/utils/task_manager/regression.py b/clinicadl/utils/task_manager/regression.py old mode 100644 new mode 100755 diff --git a/clinicadl/utils/task_manager/task_manager.py b/clinicadl/utils/task_manager/task_manager.py old mode 100644 new mode 100755 index 8262033f9..c13be9e4c --- a/clinicadl/utils/task_manager/task_manager.py +++ b/clinicadl/utils/task_manager/task_manager.py @@ -11,9 +11,11 @@ from clinicadl.utils.metric_module import MetricModule from clinicadl.utils.network.network import Network +from os import makedirs, path + # TODO: add function to check that the output size of the network corresponds to what is expected to -# perform the task +# perform the task class TaskManager: def __init__(self, mode: str, n_classes: int = None): self.mode = mode @@ -21,7 +23,7 @@ def __init__(self, mode: str, n_classes: int = None): @property @abstractmethod - def columns(self): + def columns(self, **kwargs): """ List of the columns' names in the TSV file containing the predictions. """ @@ -166,6 +168,215 @@ def get_default_network() -> Network: """Returns the default network to use when no architecture is specified.""" pass + def test_pythae( + self, + model: Network, + dataloader: DataLoader, + criterion: _Loss, + use_labels: bool = True, + save_reconstruction_tensor=False, + save_reconstruction_nifti=False, + save_latent_tensor=False, + save_caps=False, + tensor_path=None, + nifti_path=None, + latent_tensor_path=None, + caps_path=None, + sample_latent=0, + seed=None, + ) -> Tuple[pd.DataFrame, Dict[str, float]]: + """ + Computes the predictions and evaluation metrics. + + Args: + model: the model trained. + dataloader: wrapper of a CapsDataset. + criterion: function to calculate the loss. + use_labels: If True the true_label will be written in output DataFrame + and metrics dict will be created. + Returns: + the results and metrics on the image level. + """ + import nibabel as nib + from numpy import eye + from os import path + + model.eval() + dataloader.dataset.eval() + + results_df = pd.DataFrame(columns=self.columns) + sample_latent_results_df = None + with torch.no_grad(): + for data in dataloader: + + outputs = model.predict(data) + + # Generate detailed DataFrame + for idx in range(len(data["participant_id"])): + row = self.generate_test_row(idx, data, outputs["recon_x"]) + row_df = pd.DataFrame(row, columns=self.columns) + results_df = pd.concat([results_df, row_df]) + + image = data["data"][idx] + data["data"][idx] = data["data"][idx].unsqueeze(0) + participant_id = data["participant_id"][idx] + session_id = data["session_id"][idx] + mode_id = data[f"{self.mode}_id"][idx] + + # Save reconstruction tensor + if save_reconstruction_tensor: + reconstruction = outputs["recon_x"][idx].squeeze(0).cpu() + input_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_input.pt" + ) + output_nii_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output.pt" + ) + torch.save(image, path.join(tensor_path, input_filename)) + torch.save(reconstruction, path.join(tensor_path, output_nii_filename)) + + # Save reconstruction nifti + if save_reconstruction_nifti: + reconstruction = outputs["recon_x"][idx].squeeze(0).cpu() + input_nii = nib.Nifti1Image(image[0].numpy(), eye(4)) + output_nii = nib.Nifti1Image(reconstruction[0].numpy(), eye(4)) + # Create file name according to participant and session id + input_filename = f"{participant_id}_{session_id}_image_input.nii.gz" + output_nii_filename = f"{participant_id}_{session_id}_image_output.nii.gz" + nib.save(input_nii, path.join(nifti_path, input_filename)) + nib.save(output_nii, path.join(nifti_path, output_nii_filename)) + + # Save latent tensor + if save_latent_tensor: + latent = outputs["embedding"][idx].squeeze(0).cpu() + latent_pt_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent.pt" + ) + torch.save(latent, path.join(latent_tensor_path, latent_pt_filename)) + + # Save as CAPS + if save_caps: + reconstruction = outputs["recon_x"][idx].squeeze(0).cpu() + + input_pt = image[0].detach() + output_pt = reconstruction.detach() + latent_pt = outputs["embedding"][idx].squeeze(0).cpu() + + input_nii = nib.Nifti1Image(input_pt.numpy(), eye(4)) + output_nii = nib.Nifti1Image(output_pt.numpy(), eye(4)) + + input_nii_filename = ( + f"{participant_id}_{session_id}_input.nii.gz" + ) + output_nii_filename = ( + f"{participant_id}_{session_id}_output.nii.gz" + ) + + input_pt_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_input.pt" + ) + output_pt_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output.pt" + ) + latent_pt_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent.pt" + ) + + caps_sub_ses_pt_path = path.join( + caps_path, + "subjects", + participant_id, + session_id, + "deeplearning_prepare_data", + f"{self.mode}_based", + "custom", + ) + caps_sub_ses_nii_path = path.join( + caps_path, + "subjects", + participant_id, + session_id, + "custom", + ) + makedirs(caps_sub_ses_pt_path, exist_ok=True) + makedirs(caps_sub_ses_nii_path, exist_ok=True) + + nib.save(input_nii, path.join(caps_sub_ses_nii_path, input_nii_filename)) + nib.save(output_nii, path.join(caps_sub_ses_nii_path, output_nii_filename)) + + torch.save(input_pt, path.join(caps_sub_ses_pt_path, input_pt_filename)) + torch.save(output_pt, path.join(caps_sub_ses_pt_path, output_pt_filename)) + torch.save(latent_pt, path.join(caps_sub_ses_pt_path, latent_pt_filename)) + + + if sample_latent > 0: + + sample_latent_results_df = pd.DataFrame(columns=self.columns[:3]+["sample_latent_idx"]+self.columns[3:]) + sample_latent_outputs = model.predict(data, sample_latent=sample_latent, seed=seed) + + for i in range(sample_latent): + + output = sample_latent_outputs[i] + reconstruction = output["recon_x"].squeeze(0).cpu() + + if save_reconstruction_tensor: + output_pt_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output-{i}.pt" + ) + torch.save(reconstruction, path.join(tensor_path, output_pt_filename)) + + if save_reconstruction_nifti: + output_nii = nib.Nifti1Image(reconstruction[0].detach().numpy(), eye(4)) + # Create file name according to participant and session id + output_nii_filename = f"{participant_id}_{session_id}_image_output-{i}.nii.gz" + nib.save(output_nii, path.join(nifti_path, output_nii_filename)) + + if save_latent_tensor: + latent = output["embedding"].squeeze(0).cpu() + latent_filename = ( + f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent-{i}.pt" + ) + torch.save(latent, path.join(latent_tensor_path, latent_filename)) + + if save_caps: + output_pt = reconstruction.detach() + output_nii = nib.Nifti1Image(output_pt.numpy(), eye(4)) + latent_pt = output["embedding"].squeeze(0).cpu() + + output_pt_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_output-{i}.pt" + output_nii_filename = f"{participant_id}_{session_id}_output-{i}.nii.gz" + latent_pt_filename = f"{participant_id}_{session_id}_{self.mode}-{mode_id}_latent-{i}.pt" + + nib.save(output_nii, path.join(caps_sub_ses_nii_path, output_nii_filename)) + torch.save(output_pt, path.join(caps_sub_ses_pt_path, output_pt_filename)) + torch.save(latent_pt, path.join(caps_sub_ses_pt_path, latent_pt_filename)) + + + row = self.generate_test_row_sample_latent(idx, i, data, output["recon_x"]) + row_df = pd.DataFrame(row, columns=sample_latent_results_df.columns) + sample_latent_results_df = pd.concat([sample_latent_results_df, row_df]) + + del sample_latent_outputs + + del outputs + + results_df.reset_index(inplace=True, drop=True) + results_df[self.evaluation_metrics] = results_df[ + self.evaluation_metrics + ].apply(pd.to_numeric, axis=1) + + if not use_labels: + metrics_df = None + sample_latent_metrics_df = None + else: + metrics_df = self.compute_metrics(results_df) + sample_latent_metrics_df = None + if sample_latent > 0: + sample_latent_metrics_df = self.compute_metrics_sample_latent(sample_latent_results_df) + torch.cuda.empty_cache() + + return results_df, metrics_df, sample_latent_results_df, sample_latent_metrics_df + def test( self, model: Network, @@ -189,13 +400,24 @@ def test( dataloader.dataset.eval() results_df = pd.DataFrame(columns=self.columns) - total_loss = 0 + total_loss = {} with torch.no_grad(): for i, data in enumerate(dataloader): - outputs, loss_dict = model.compute_outputs_and_loss( - data, criterion, use_labels=use_labels - ) - total_loss += loss_dict["loss"].item() + # initialize the loss list to save the loss components + if i == 0: + outputs, loss_dict = model.compute_outputs_and_loss( + data, criterion, use_labels=use_labels + ) + for loss_component in loss_dict.keys(): + total_loss[loss_component] = 0 + for loss_component in total_loss.keys(): + total_loss[loss_component] += loss_dict[loss_component].item() + else: + outputs, loss_dict = model.compute_outputs_and_loss( + data, criterion, use_labels=use_labels + ) + for loss_component in total_loss.keys(): + total_loss[loss_component] += loss_dict[loss_component].item() # Generate detailed DataFrame for idx in range(len(data["participant_id"])): @@ -210,7 +432,8 @@ def test( metrics_dict = None else: metrics_dict = self.compute_metrics(results_df) - metrics_dict["loss"] = total_loss + for loss_component in total_loss.keys(): + metrics_dict[loss_component] = total_loss[loss_component] torch.cuda.empty_cache() return results_df, metrics_dict diff --git a/clinicadl/utils/tsvtools_utils.py b/clinicadl/utils/tsvtools_utils.py old mode 100644 new mode 100755 diff --git a/docs/Contribute/Custom.md b/docs/Contribute/Custom.md old mode 100644 new mode 100755 diff --git a/docs/Contribute/Newcomers.md b/docs/Contribute/Newcomers.md old mode 100644 new mode 100755 diff --git a/docs/Contribute/Test.md b/docs/Contribute/Test.md old mode 100644 new mode 100755 diff --git a/docs/Installation.md b/docs/Installation.md old mode 100644 new mode 100755 diff --git a/docs/Interpret.md b/docs/Interpret.md old mode 100644 new mode 100755 index 2d90402c3..0534a5b4f --- a/docs/Interpret.md +++ b/docs/Interpret.md @@ -52,7 +52,7 @@ Optional arguments: - `--gpu / --no-gpu` (bool) Uses GPU acceleration or not. Default behaviour is to try to use a GPU. If not available an error is raised. Use the option `--no-gpu` if running in CPU. - `--n_proc` (int) is the number of workers used by the DataLoader. Default: `2`. - - `--batch_size` (int) is the size of the batch used in the DataLoader. Default: `2`. + - `--batch_size` (int) is the size of the batch used in the DataLoader. Default: `8`. - **Model selection** - `--selection_metrics` (List[str]) is a list of metrics to find the best models to evaluate. Default will predict the results for best model based on the loss only. diff --git a/docs/Introduction.md b/docs/Introduction.md old mode 100644 new mode 100755 diff --git a/docs/Predict.md b/docs/Predict.md old mode 100644 new mode 100755 index bdc00886f..37e524927 --- a/docs/Predict.md +++ b/docs/Predict.md @@ -45,7 +45,7 @@ Optional arguments: - `--gpu / --no-gpu` (bool) Uses GPU acceleration or not. Default behaviour is to try to use a GPU. If not available an error is raised. Use the option `--no-gpu` if running in CPU. - `--n_proc` (int) is the number of workers used by the DataLoader. Default: `2`. - - `--batch_size` (int) is the size of the batch used in the DataLoader. Default: `2`. + - `--batch_size` (int) is the size of the batch used in the DataLoader. Default: `8`. - **Reconstruction** This tool allows to save the output tensors of a whole [data group](./Introduction.md), associated with the tensor corresponding to their input. This can be useful for the `reconstruction` task, for which the user may want to perform extra analyses directly on the images reconstructed by a trained network, or simply visualize them for a qualitative check. diff --git a/docs/Preprocessing/Extract.md b/docs/Preprocessing/Extract.md old mode 100644 new mode 100755 diff --git a/docs/Preprocessing/Generate.md b/docs/Preprocessing/Generate.md old mode 100644 new mode 100755 diff --git a/docs/Preprocessing/Introduction.md b/docs/Preprocessing/Introduction.md old mode 100644 new mode 100755 diff --git a/docs/Preprocessing/QualityCheck.md b/docs/Preprocessing/QualityCheck.md old mode 100644 new mode 100755 diff --git a/docs/RandomSearch.md b/docs/RandomSearch.md old mode 100644 new mode 100755 diff --git a/docs/TSVTools.md b/docs/TSVTools.md old mode 100644 new mode 100755 diff --git a/docs/Train/Details.md b/docs/Train/Details.md old mode 100644 new mode 100755 diff --git a/docs/Train/Introduction.md b/docs/Train/Introduction.md old 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a/docs/images/logo.png b/docs/images/logo.png old mode 100644 new mode 100755 diff --git a/docs/images/patches.png b/docs/images/patches.png old mode 100644 new mode 100755 diff --git a/docs/images/random1.png b/docs/images/random1.png old mode 100644 new mode 100755 diff --git a/docs/images/random2.png b/docs/images/random2.png old mode 100644 new mode 100755 diff --git a/docs/images/transfer_learning.png b/docs/images/transfer_learning.png old mode 100644 new mode 100755 diff --git a/docs/index.md b/docs/index.md old mode 100644 new mode 100755 diff --git a/docs/overrides/main.html b/docs/overrides/main.html old mode 100644 new mode 100755 diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css old mode 100644 new mode 100755 diff --git a/environment.yml b/environment.yml old mode 100644 new mode 100755 index 0c21b6388..c3101af3e --- a/environment.yml +++ b/environment.yml @@ -3,6 +3,5 @@ channels: - defaults - conda-forge dependencies: - - python=3.8 - - poetry + - 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the pipeline and the + different functions available in ClinicaDL +""" + +import pytest + + +def pytest_addoption(parser): + parser.addoption( + "--input_data_directory", + action="store", + help="Directory for (only-read) inputs for tests", + ) + + +@pytest.fixture +def cmdopt(request): + config_param = {} + config_param["input"] = request.config.getoption("--input_data_directory") + return config_param diff --git a/tests/data/random_search.toml b/tests/data/random_search.toml deleted file mode 100644 index 66b345d09..000000000 --- a/tests/data/random_search.toml +++ /dev/null @@ -1,17 +0,0 @@ -[Random_Search] -caps_directory = "data/dataset/random_example" -tsv_path = "data/labels_list" -preprocessing_json = "extract_roi.json" -network_task = "classification" -n_convblocks = [3, 5] -first_conv_width = [1, 3] -n_fcblocks = [1, 2] - -[Cross_validation] -n_splits = 2 -split = [0] - -[Optimization] -epochs = 1 -patience = 0 -tolerance = 0.0 diff --git a/tests/data/reproducibility_config.toml b/tests/data/reproducibility_config.toml deleted file mode 100644 index 5dce205d5..000000000 --- a/tests/data/reproducibility_config.toml +++ /dev/null @@ -1,14 +0,0 @@ -# CONFIG FILE FOR TESTS -[Model] -architecture = "Stride_Conv5_FC3" - -[Reproducibility] -seed = 42 -deterministic = true - -[Cross_validation] -n_splits = 2 -split = [0] - -[Optimization] -epochs = 1 diff --git a/tests/data/train_config.toml b/tests/data/train_config.toml deleted file mode 100644 index a3ca13b02..000000000 --- a/tests/data/train_config.toml +++ /dev/null @@ -1,7 +0,0 @@ -# CONFIG FILE FOR TESTS -[Cross_validation] -n_splits = 2 -split = [0] - -[Optimization] -epochs = 1 diff --git a/tests/data/tsvtool/anonymous_BIDS.tsv b/tests/data/tsvtool/anonymous_BIDS.tsv deleted file mode 100644 index d9cfd8b4b..000000000 --- a/tests/data/tsvtool/anonymous_BIDS.tsv +++ /dev/null @@ -1,13681 +0,0 @@ -participant_id session_id diagnosis adni_diagnosis_change age sex site 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64.2 M 2.0 23.0 0.5 -sub-CLNC0003 ses-M36 64.2 M 2.0 23.0 0.5 -sub-CLNC0004 ses-M00 MCI 2.0 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M03 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M06 MCI 2.0 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M12 MCI 2.0 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M30 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M36 5.0 85.41 M 5.0 26.0 0.5 -sub-CLNC0004 ses-M42 85.41 M 5.0 26.0 0.5 -sub-CLNC0005 ses-M00 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M03 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M06 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M12 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M18 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M24 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M30 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M36 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0005 ses-M48 MCI 2.0 68.73 F 8.0 26.0 0.5 -sub-CLNC0006 ses-M00 AD 3.0 82.9 M 4.0 26.0 1.0 -sub-CLNC0006 ses-M03 82.9 M 4.0 26.0 1.0 -sub-CLNC0006 ses-M06 AD 3.0 82.9 M 4.0 26.0 1.0 -sub-CLNC0006 ses-M12 AD 3.0 82.9 M 4.0 26.0 1.0 -sub-CLNC0006 ses-M24 AD 3.0 82.9 M 4.0 26.0 1.0 -sub-CLNC0007 ses-M00 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M06 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M12 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M18 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M24 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M30 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M36 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M42 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M48 CN 81.13 M 9.0 20.0 2.0 -sub-CLNC0007 ses-M54 81.13 M 9.0 20.0 2.0 -sub-CLNC0008 ses-M00 MCI 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M06 MCI 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M12 AD 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M18 AD 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M24 AD 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M30 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M42 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M48 AD 3.0 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M54 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M60 AD 3.0 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M66 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M72 66.98 M 8.0 29.0 2.0 -sub-CLNC0008 ses-M78 66.98 M 8.0 29.0 2.0 -sub-CLNC0009 ses-M00 CN 1.0 67.93 F 8.0 28.0 3.0 -sub-CLNC0009 ses-M03 67.93 F 8.0 28.0 3.0 -sub-CLNC0009 ses-M06 CN 1.0 67.93 F 8.0 28.0 3.0 -sub-CLNC0009 ses-M12 CN 1.0 67.93 F 8.0 28.0 3.0 -sub-CLNC0009 ses-M24 CN 1.0 67.93 F 8.0 28.0 3.0 -sub-CLNC0009 ses-M48 67.93 F 8.0 28.0 3.0 -sub-CLNC0010 ses-M00 CN 1.0 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M03 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M06 CN 1.0 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M12 CN 1.0 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M18 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M24 CN 1.0 72.46 M 5.0 23.0 3.0 -sub-CLNC0010 ses-M36 CN 1.0 72.46 M 5.0 23.0 3.0 -sub-CLNC0011 ses-M00 CN 1.0 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M03 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M06 CN 1.0 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M12 CN 1.0 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M18 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M24 CN 1.0 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M30 64.1 M 1.0 25.0 2.0 -sub-CLNC0011 ses-M48 CN 1.0 64.1 M 1.0 25.0 2.0 -sub-CLNC0012 ses-M00 AD 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M06 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M12 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M18 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M24 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M30 68.58 M 3.0 24.0 0.5 -sub-CLNC0012 ses-M36 68.58 M 3.0 24.0 0.5 -sub-CLNC0013 ses-M00 MCI 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M06 MCI 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M12 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M18 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M24 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M30 68.55 M 1.0 28.0 0.5 -sub-CLNC0013 ses-M36 68.55 M 1.0 28.0 0.5 -sub-CLNC0014 ses-M00 CN 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M06 CN 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M12 CN 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M18 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M24 CN 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M30 85.51 F 7.0 27.0 3.0 -sub-CLNC0014 ses-M36 MCI 85.51 F 7.0 27.0 3.0 -sub-CLNC0015 ses-M00 AD 3.0 78.52 M 6.0 28.0 0.5 -sub-CLNC0015 ses-M03 78.52 M 6.0 28.0 0.5 -sub-CLNC0016 ses-M00 MCI 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M06 MCI 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M12 AD 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M18 AD 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M24 AD 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M30 70.24 M 8.0 28.0 1.0 -sub-CLNC0016 ses-M36 AD 70.24 M 8.0 28.0 1.0 -sub-CLNC0017 ses-M00 CN 1.0 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M03 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M06 CN 1.0 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M12 CN 1.0 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M18 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M24 CN 1.0 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M30 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M36 CN 1.0 57.82 F 2.0 27.0 2.0 -sub-CLNC0017 ses-M42 57.82 F 2.0 27.0 2.0 -sub-CLNC0018 ses-M00 CN 1.0 66.44 F 2.0 27.0 3.0 -sub-CLNC0018 ses-M03 66.44 F 2.0 27.0 3.0 -sub-CLNC0018 ses-M06 CN 1.0 66.44 F 2.0 27.0 3.0 -sub-CLNC0018 ses-M12 CN 1.0 66.44 F 2.0 27.0 3.0 -sub-CLNC0018 ses-M24 CN 1.0 66.44 F 2.0 27.0 3.0 -sub-CLNC0019 ses-M00 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M06 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M108 MCI 2.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M12 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M120 MCI 2.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M18 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M24 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M30 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M36 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M42 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M48 CN 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M54 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M60 CN 1.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M66 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M72 CN 1.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M78 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M84 4.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M90 73.73 M 3.0 23.0 3.0 -sub-CLNC0019 ses-M96 MCI 2.0 73.73 M 3.0 23.0 3.0 -sub-CLNC0020 ses-M00 CN 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M06 CN 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M12 CN 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M18 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M24 CN 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M30 65.61 F 6.0 21.0 1.0 -sub-CLNC0020 ses-M36 CN 65.61 F 6.0 21.0 1.0 -sub-CLNC0021 ses-M00 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M03 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M06 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M12 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M18 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M24 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M30 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M36 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M48 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0021 ses-M60 MCI 2.0 86.62 F 3.0 21.0 1.0 -sub-CLNC0022 ses-M00 MCI 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M06 MCI 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M12 MCI 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M18 MCI 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M24 MCI 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M30 84.91 M 7.0 24.0 2.0 -sub-CLNC0022 ses-M36 84.91 M 7.0 24.0 2.0 -sub-CLNC0023 ses-M00 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M03 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M06 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M12 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M18 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M24 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M30 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M36 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M48 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0023 ses-M60 MCI 2.0 72.25 F 2.0 26.0 2.0 -sub-CLNC0024 ses-M00 CN 1.0 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M03 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M06 CN 1.0 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M12 CN 1.0 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M18 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M24 MCI 2.0 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M30 76.64 F 3.0 22.0 1.0 -sub-CLNC0024 ses-M36 CN 1.0 76.64 F 3.0 22.0 1.0 -sub-CLNC0025 ses-M00 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M06 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M12 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M18 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M24 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M30 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M36 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M42 64.23 M 4.0 23.0 3.0 -sub-CLNC0025 ses-M48 AD 64.23 M 4.0 23.0 3.0 -sub-CLNC0026 ses-M00 MCI 2.0 83.74 M 5.0 21.0 0.5 -sub-CLNC0026 ses-M03 83.74 M 5.0 21.0 0.5 -sub-CLNC0026 ses-M06 MCI 2.0 83.74 M 5.0 21.0 0.5 -sub-CLNC0026 ses-M12 MCI 2.0 83.74 M 5.0 21.0 0.5 -sub-CLNC0027 ses-M00 MCI 2.0 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M03 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M06 4.0 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M12 MCI 2.0 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M18 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M24 MCI 2.0 60.12 F 8.0 29.0 3.0 -sub-CLNC0027 ses-M36 60.12 F 8.0 29.0 3.0 -sub-CLNC0028 ses-M00 AD 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M06 AD 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M12 AD 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M18 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M24 AD 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M30 76.41 F 2.0 22.0 0.0 -sub-CLNC0028 ses-M36 76.41 F 2.0 22.0 0.0 -sub-CLNC0029 ses-M00 CN 1.0 63.43 M 6.0 26.0 3.0 -sub-CLNC0029 ses-M03 63.43 M 6.0 26.0 3.0 -sub-CLNC0029 ses-M06 CN 1.0 63.43 M 6.0 26.0 3.0 -sub-CLNC0029 ses-M12 CN 1.0 63.43 M 6.0 26.0 3.0 -sub-CLNC0029 ses-M24 CN 1.0 63.43 M 6.0 26.0 3.0 -sub-CLNC0029 ses-M36 63.43 M 6.0 26.0 3.0 -sub-CLNC0030 ses-M00 AD 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M06 AD 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M12 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M18 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M24 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M30 65.88 M 5.0 23.0 0.5 -sub-CLNC0030 ses-M36 65.88 M 5.0 23.0 0.5 -sub-CLNC0031 ses-M00 MCI 2.0 69.47 F 4.0 23.0 3.0 -sub-CLNC0031 ses-M03 69.47 F 4.0 23.0 3.0 -sub-CLNC0032 ses-M00 MCI 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M06 CN 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M12 CN 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M18 MCI 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M24 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M30 81.93 F 8.0 27.0 1.0 -sub-CLNC0032 ses-M36 81.93 F 8.0 27.0 1.0 -sub-CLNC0033 ses-M00 CN 1.0 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M03 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M06 CN 1.0 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M12 CN 1.0 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M18 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M24 4.0 65.45 M 6.0 21.0 2.0 -sub-CLNC0033 ses-M48 5.0 65.45 M 6.0 21.0 2.0 -sub-CLNC0034 ses-M00 AD 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M06 AD 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M12 AD 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M18 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M24 AD 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M30 90.9 F 9.0 24.0 0.5 -sub-CLNC0034 ses-M36 90.9 F 9.0 24.0 0.5 -sub-CLNC0035 ses-M00 MCI 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M06 MCI 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M12 MCI 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M18 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M24 MCI 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M30 78.0 M 5.0 28.0 3.0 -sub-CLNC0035 ses-M36 MCI 78.0 M 5.0 28.0 3.0 -sub-CLNC0036 ses-M00 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M06 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M12 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M18 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M24 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M30 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M36 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M42 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M48 MCI 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M54 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M60 MCI 2.0 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M66 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M72 MCI 2.0 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M78 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M84 MCI 2.0 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M90 72.6 F 5.0 28.0 3.0 -sub-CLNC0036 ses-M96 MCI 2.0 72.6 F 5.0 28.0 3.0 -sub-CLNC0037 ses-M00 AD 3.0 73.25 F 1.0 28.0 2.0 -sub-CLNC0037 ses-M03 73.25 F 1.0 28.0 2.0 -sub-CLNC0037 ses-M06 AD 3.0 73.25 F 1.0 28.0 2.0 -sub-CLNC0037 ses-M12 AD 3.0 73.25 F 1.0 28.0 2.0 -sub-CLNC0037 ses-M18 73.25 F 1.0 28.0 2.0 -sub-CLNC0037 ses-M24 AD 3.0 73.25 F 1.0 28.0 2.0 -sub-CLNC0038 ses-M00 MCI 2.0 71.27 F 5.0 20.0 0.5 -sub-CLNC0038 ses-M03 71.27 F 5.0 20.0 0.5 -sub-CLNC0038 ses-M06 MCI 2.0 71.27 F 5.0 20.0 0.5 -sub-CLNC0038 ses-M12 MCI 2.0 71.27 F 5.0 20.0 0.5 -sub-CLNC0038 ses-M18 71.27 F 5.0 20.0 0.5 -sub-CLNC0039 ses-M00 MCI 2.0 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M03 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M06 MCI 2.0 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M12 5.0 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M18 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M24 AD 3.0 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M30 69.32 F 7.0 25.0 0.5 -sub-CLNC0039 ses-M36 69.32 F 7.0 25.0 0.5 -sub-CLNC0040 ses-M00 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M06 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M108 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M12 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M18 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M24 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M30 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M36 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M42 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M48 CN 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M60 CN 1.0 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M66 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M72 CN 1.0 76.26 M 5.0 25.0 2.0 -sub-CLNC0040 ses-M84 CN 1.0 76.26 M 5.0 25.0 2.0 -sub-CLNC0041 ses-M00 CN 1.0 92.48 F 2.0 25.0 2.0 -sub-CLNC0042 ses-M00 AD 3.0 86.03 M 4.0 26.0 0.5 -sub-CLNC0042 ses-M03 86.03 M 4.0 26.0 0.5 -sub-CLNC0042 ses-M06 AD 3.0 86.03 M 4.0 26.0 0.5 -sub-CLNC0042 ses-M18 86.03 M 4.0 26.0 0.5 -sub-CLNC0042 ses-M24 AD 3.0 86.03 M 4.0 26.0 0.5 -sub-CLNC0042 ses-M30 86.03 M 4.0 26.0 0.5 -sub-CLNC0043 ses-M00 MCI 69.99 M 3.0 24.0 3.0 -sub-CLNC0043 ses-M06 MCI 69.99 M 3.0 24.0 3.0 -sub-CLNC0043 ses-M12 MCI 69.99 M 3.0 24.0 3.0 -sub-CLNC0044 ses-M00 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M03 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M06 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M12 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M24 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M30 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M36 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M48 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0044 ses-M60 MCI 2.0 54.94 M 2.0 20.0 3.0 -sub-CLNC0045 ses-M00 AD 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M06 AD 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M12 AD 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M18 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M24 AD 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M30 64.03 M 6.0 29.0 2.0 -sub-CLNC0045 ses-M36 64.03 M 6.0 29.0 2.0 -sub-CLNC0046 ses-M00 AD 3.0 68.57 M 6.0 29.0 0.0 -sub-CLNC0046 ses-M03 68.57 M 6.0 29.0 0.0 -sub-CLNC0046 ses-M06 AD 3.0 68.57 M 6.0 29.0 0.0 -sub-CLNC0047 ses-M00 MCI 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M06 AD 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M12 AD 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M18 AD 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M24 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M30 65.95 F 3.0 20.0 1.0 -sub-CLNC0047 ses-M36 65.95 F 3.0 20.0 1.0 -sub-CLNC0048 ses-M00 CN 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M06 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M12 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M18 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M24 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M30 75.3 F 6.0 28.0 3.0 -sub-CLNC0048 ses-M36 75.3 F 6.0 28.0 3.0 -sub-CLNC0049 ses-M00 MCI 2.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M03 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M06 MCI 2.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M12 MCI 2.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M18 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M24 MCI 2.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M30 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M36 5.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M48 AD 3.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0049 ses-M60 AD 3.0 71.18 M 9.0 26.0 1.0 -sub-CLNC0050 ses-M00 CN 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M06 CN 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M12 CN 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M18 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M24 CN 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M30 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M36 CN 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M42 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M48 CN 1.0 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M54 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M60 CN 1.0 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M66 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M72 CN 1.0 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M78 87.3 M 7.0 20.0 1.0 -sub-CLNC0050 ses-M90 87.3 M 7.0 20.0 1.0 -sub-CLNC0051 ses-M00 MCI 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M06 MCI 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M12 AD 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M18 AD 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M24 AD 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M30 78.0 M 2.0 29.0 2.0 -sub-CLNC0051 ses-M36 78.0 M 2.0 29.0 2.0 -sub-CLNC0052 ses-M00 MCI 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M06 MCI 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M12 MCI 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M18 MCI 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M24 MCI 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M30 78.72 M 9.0 22.0 2.0 -sub-CLNC0052 ses-M36 78.72 M 9.0 22.0 2.0 -sub-CLNC0053 ses-M00 AD 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M06 AD 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M12 AD 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M18 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M24 AD 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M30 60.5 M 1.0 25.0 1.0 -sub-CLNC0053 ses-M36 60.5 M 1.0 25.0 1.0 -sub-CLNC0054 ses-M00 MCI 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M06 MCI 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M12 MCI 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M18 MCI 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M24 MCI 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M30 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M36 AD 72.5 F 1.0 21.0 1.0 -sub-CLNC0054 ses-M42 72.5 F 1.0 21.0 1.0 -sub-CLNC0055 ses-M00 MCI 2.0 74.91 F 2.0 28.0 2.0 -sub-CLNC0055 ses-M03 74.91 F 2.0 28.0 2.0 -sub-CLNC0055 ses-M06 5.0 74.91 F 2.0 28.0 2.0 -sub-CLNC0055 ses-M12 AD 3.0 74.91 F 2.0 28.0 2.0 -sub-CLNC0055 ses-M24 AD 3.0 74.91 F 2.0 28.0 2.0 -sub-CLNC0055 ses-M36 AD 3.0 74.91 F 2.0 28.0 2.0 -sub-CLNC0056 ses-M00 CN 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M06 CN 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M12 CN 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M18 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M24 CN 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M30 79.97 M 3.0 25.0 0.0 -sub-CLNC0056 ses-M36 CN 79.97 M 3.0 25.0 0.0 -sub-CLNC0057 ses-M00 MCI 2.0 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M03 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M06 MCI 2.0 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M12 MCI 2.0 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M18 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M24 MCI 2.0 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M30 71.48 M 8.0 27.0 0.0 -sub-CLNC0057 ses-M36 MCI 2.0 71.48 M 8.0 27.0 0.0 -sub-CLNC0058 ses-M00 MCI 2.0 66.22 M 2.0 25.0 1.0 -sub-CLNC0058 ses-M03 66.22 M 2.0 25.0 1.0 -sub-CLNC0058 ses-M06 MCI 2.0 66.22 M 2.0 25.0 1.0 -sub-CLNC0059 ses-M00 AD 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M06 AD 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M12 AD 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M18 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M24 AD 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M30 80.86 M 8.0 20.0 3.0 -sub-CLNC0059 ses-M36 80.86 M 8.0 20.0 3.0 -sub-CLNC0060 ses-M00 MCI 2.0 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M03 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M06 MCI 2.0 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M12 5.0 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M18 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M24 AD 3.0 81.83 F 1.0 22.0 0.5 -sub-CLNC0060 ses-M36 AD 3.0 81.83 F 1.0 22.0 0.5 -sub-CLNC0061 ses-M00 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M03 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M06 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M12 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M24 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M36 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0061 ses-M48 MCI 2.0 70.27 M 6.0 21.0 2.0 -sub-CLNC0062 ses-M00 AD 3.0 74.39 F 9.0 21.0 0.5 -sub-CLNC0063 ses-M00 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M06 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M12 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M18 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M24 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M30 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M36 MCI 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M72 MCI 2.0 83.07 F 1.0 24.0 1.0 -sub-CLNC0063 ses-M78 83.07 F 1.0 24.0 1.0 -sub-CLNC0064 ses-M00 CN 1.0 78.8 F 4.0 21.0 0.0 -sub-CLNC0064 ses-M03 78.8 F 4.0 21.0 0.0 -sub-CLNC0064 ses-M06 CN 1.0 78.8 F 4.0 21.0 0.0 -sub-CLNC0064 ses-M12 CN 1.0 78.8 F 4.0 21.0 0.0 -sub-CLNC0064 ses-M18 78.8 F 4.0 21.0 0.0 -sub-CLNC0064 ses-M24 CN 1.0 78.8 F 4.0 21.0 0.0 -sub-CLNC0065 ses-M00 AD 79.91 F 7.0 25.0 0.0 -sub-CLNC0065 ses-M06 AD 79.91 F 7.0 25.0 0.0 -sub-CLNC0065 ses-M12 AD 79.91 F 7.0 25.0 0.0 -sub-CLNC0065 ses-M18 79.91 F 7.0 25.0 0.0 -sub-CLNC0065 ses-M24 AD 79.91 F 7.0 25.0 0.0 -sub-CLNC0066 ses-M00 CN 1.0 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M03 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M06 CN 1.0 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M12 CN 1.0 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M18 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M24 CN 1.0 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M30 77.54 F 7.0 21.0 0.5 -sub-CLNC0066 ses-M48 CN 1.0 77.54 F 7.0 21.0 0.5 -sub-CLNC0067 ses-M00 MCI 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M06 MCI 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M12 MCI 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M18 MCI 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M24 AD 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M30 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M36 AD 68.08 F 8.0 26.0 2.0 -sub-CLNC0067 ses-M60 AD 3.0 68.08 F 8.0 26.0 2.0 -sub-CLNC0068 ses-M00 AD 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M06 AD 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M12 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M18 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M24 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M30 73.43 M 3.0 26.0 3.0 -sub-CLNC0068 ses-M36 73.43 M 3.0 26.0 3.0 -sub-CLNC0069 ses-M00 MCI 2.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M03 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M06 MCI 2.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M12 MCI 2.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M18 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M24 MCI 2.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M36 5.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0069 ses-M48 MCI 2.0 69.97 M 1.0 25.0 0.5 -sub-CLNC0070 ses-M00 CN 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M06 CN 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M12 CN 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M18 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M24 CN 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M30 87.01 M 7.0 29.0 1.0 -sub-CLNC0070 ses-M36 CN 87.01 M 7.0 29.0 1.0 -sub-CLNC0071 ses-M00 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M06 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M12 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M18 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M24 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M30 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M36 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0071 ses-M48 CN 85.72 F 2.0 27.0 0.5 -sub-CLNC0072 ses-M00 MCI 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M06 MCI 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M12 MCI 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M18 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M24 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M30 65.18 M 7.0 20.0 1.0 -sub-CLNC0072 ses-M36 65.18 M 7.0 20.0 1.0 -sub-CLNC0073 ses-M00 CN 1.0 70.55 M 6.0 28.0 0.0 -sub-CLNC0073 ses-M03 70.55 M 6.0 28.0 0.0 -sub-CLNC0073 ses-M06 CN 1.0 70.55 M 6.0 28.0 0.0 -sub-CLNC0073 ses-M24 CN 1.0 70.55 M 6.0 28.0 0.0 -sub-CLNC0073 ses-M36 70.55 M 6.0 28.0 0.0 -sub-CLNC0074 ses-M00 MCI 2.0 88.55 M 8.0 26.0 2.0 -sub-CLNC0074 ses-M03 88.55 M 8.0 26.0 2.0 -sub-CLNC0074 ses-M06 5.0 88.55 M 8.0 26.0 2.0 -sub-CLNC0074 ses-M12 MCI 2.0 88.55 M 8.0 26.0 2.0 -sub-CLNC0074 ses-M24 88.55 M 8.0 26.0 2.0 -sub-CLNC0075 ses-M00 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M06 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M12 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M18 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M24 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M30 80.9 F 1.0 29.0 3.0 -sub-CLNC0075 ses-M36 MCI 80.9 F 1.0 29.0 3.0 -sub-CLNC0076 ses-M00 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M03 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M06 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M12 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M18 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M24 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M30 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M36 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M48 MCI 2.0 78.26 F 8.0 21.0 0.5 -sub-CLNC0076 ses-M60 78.26 F 8.0 21.0 0.5 -sub-CLNC0077 ses-M00 CN 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M06 CN 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M108 CN 1.0 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M12 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M18 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M24 CN 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M30 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M36 CN 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M42 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M48 CN 1.0 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M54 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M60 CN 1.0 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M66 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M72 CN 1.0 81.54 M 6.0 26.0 0.0 -sub-CLNC0077 ses-M84 CN 1.0 81.54 M 6.0 26.0 0.0 -sub-CLNC0078 ses-M00 MCI 2.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M03 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M06 MCI 2.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M12 5.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M18 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M24 AD 3.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M36 AD 3.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0078 ses-M48 AD 3.0 76.0 F 3.0 27.0 0.0 -sub-CLNC0079 ses-M00 CN 1.0 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M03 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M06 CN 1.0 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M12 CN 1.0 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M18 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M24 CN 1.0 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M30 69.42 M 5.0 29.0 0.5 -sub-CLNC0079 ses-M48 CN 1.0 69.42 M 5.0 29.0 0.5 -sub-CLNC0080 ses-M00 CN 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M06 CN 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M12 CN 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M18 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M24 CN 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M30 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M36 CN 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M48 CN 1.0 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M54 77.06 M 3.0 21.0 0.5 -sub-CLNC0080 ses-M66 77.06 M 3.0 21.0 0.5 -sub-CLNC0081 ses-M00 CN 1.0 72.22 M 6.0 21.0 2.0 -sub-CLNC0081 ses-M03 72.22 M 6.0 21.0 2.0 -sub-CLNC0081 ses-M06 CN 1.0 72.22 M 6.0 21.0 2.0 -sub-CLNC0081 ses-M24 CN 1.0 72.22 M 6.0 21.0 2.0 -sub-CLNC0082 ses-M00 MCI 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M06 MCI 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M12 MCI 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M18 MCI 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M24 AD 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M30 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M36 MCI 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M42 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M48 5.0 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M54 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M60 5.0 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M66 72.31 F 8.0 25.0 0.0 -sub-CLNC0082 ses-M72 AD 3.0 72.31 F 8.0 25.0 0.0 -sub-CLNC0083 ses-M00 MCI 2.0 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M03 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M06 MCI 2.0 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M12 MCI 2.0 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M18 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M24 MCI 2.0 79.58 F 2.0 20.0 3.0 -sub-CLNC0083 ses-M36 MCI 2.0 79.58 F 2.0 20.0 3.0 -sub-CLNC0084 ses-M00 CN 1.0 79.23 M 1.0 26.0 3.0 -sub-CLNC0084 ses-M03 79.23 M 1.0 26.0 3.0 -sub-CLNC0084 ses-M06 CN 1.0 79.23 M 1.0 26.0 3.0 -sub-CLNC0084 ses-M12 CN 1.0 79.23 M 1.0 26.0 3.0 -sub-CLNC0084 ses-M18 79.23 M 1.0 26.0 3.0 -sub-CLNC0084 ses-M30 79.23 M 1.0 26.0 3.0 -sub-CLNC0085 ses-M00 CN 1.0 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M03 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M06 CN 1.0 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M12 CN 1.0 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M18 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M24 CN 1.0 79.23 F 5.0 29.0 0.0 -sub-CLNC0085 ses-M48 79.23 F 5.0 29.0 0.0 -sub-CLNC0086 ses-M00 MCI 2.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M03 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M06 MCI 2.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M12 MCI 2.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M18 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M24 MCI 2.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M30 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M36 MCI 2.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M48 7.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0086 ses-M60 4.0 65.4 F 4.0 26.0 3.0 -sub-CLNC0087 ses-M00 CN 1.0 61.31 F 9.0 28.0 0.0 -sub-CLNC0087 ses-M06 CN 1.0 61.31 F 9.0 28.0 0.0 -sub-CLNC0087 ses-M24 4.0 61.31 F 9.0 28.0 0.0 -sub-CLNC0088 ses-M00 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M06 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M108 AD 3.0 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M12 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M18 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M24 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M30 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M36 MCI 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M42 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M48 MCI 2.0 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M60 MCI 2.0 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M78 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M84 MCI 2.0 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M90 71.03 M 9.0 21.0 2.0 -sub-CLNC0088 ses-M96 5.0 71.03 M 9.0 21.0 2.0 -sub-CLNC0089 ses-M00 CN 1.0 83.48 F 5.0 24.0 2.0 -sub-CLNC0089 ses-M03 83.48 F 5.0 24.0 2.0 -sub-CLNC0089 ses-M06 CN 1.0 83.48 F 5.0 24.0 2.0 -sub-CLNC0089 ses-M12 CN 1.0 83.48 F 5.0 24.0 2.0 -sub-CLNC0089 ses-M24 CN 1.0 83.48 F 5.0 24.0 2.0 -sub-CLNC0090 ses-M00 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M06 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M12 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M18 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M24 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M30 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M36 MCI 72.27 F 3.0 27.0 2.0 -sub-CLNC0090 ses-M42 72.27 F 3.0 27.0 2.0 -sub-CLNC0091 ses-M00 AD 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M06 AD 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M12 AD 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M18 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M24 AD 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M30 76.03 F 7.0 29.0 3.0 -sub-CLNC0091 ses-M36 AD 76.03 F 7.0 29.0 3.0 -sub-CLNC0092 ses-M00 MCI 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M06 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M12 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M18 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M24 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M30 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M36 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M42 78.73 M 4.0 22.0 1.0 -sub-CLNC0092 ses-M48 AD 78.73 M 4.0 22.0 1.0 -sub-CLNC0093 ses-M00 CN 1.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M03 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M06 CN 1.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M12 4.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M18 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M24 MCI 2.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M30 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M36 MCI 2.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M48 MCI 2.0 68.4 F 2.0 25.0 0.0 -sub-CLNC0093 ses-M60 68.4 F 2.0 25.0 0.0 -sub-CLNC0094 ses-M00 AD 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M06 AD 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M12 AD 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M18 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M24 AD 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M30 77.55 F 8.0 21.0 2.0 -sub-CLNC0094 ses-M36 77.55 F 8.0 21.0 2.0 -sub-CLNC0095 ses-M00 CN 1.0 82.71 M 1.0 26.0 2.0 -sub-CLNC0095 ses-M03 82.71 M 1.0 26.0 2.0 -sub-CLNC0095 ses-M06 CN 1.0 82.71 M 1.0 26.0 2.0 -sub-CLNC0095 ses-M24 CN 1.0 82.71 M 1.0 26.0 2.0 -sub-CLNC0096 ses-M00 CN 1.0 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M03 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M06 CN 1.0 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M12 CN 1.0 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M18 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M24 CN 1.0 78.37 F 2.0 27.0 0.0 -sub-CLNC0096 ses-M48 CN 1.0 78.37 F 2.0 27.0 0.0 -sub-CLNC0097 ses-M00 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M03 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M06 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M12 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M18 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M24 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M36 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0097 ses-M48 MCI 2.0 73.04 M 1.0 29.0 0.0 -sub-CLNC0098 ses-M00 CN 1.0 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M03 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M06 CN 1.0 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M12 CN 1.0 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M18 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M24 CN 1.0 61.11 M 4.0 27.0 0.0 -sub-CLNC0098 ses-M48 CN 1.0 61.11 M 4.0 27.0 0.0 -sub-CLNC0099 ses-M00 MCI 2.0 67.81 F 7.0 26.0 0.0 -sub-CLNC0099 ses-M03 67.81 F 7.0 26.0 0.0 -sub-CLNC0099 ses-M06 5.0 67.81 F 7.0 26.0 0.0 -sub-CLNC0099 ses-M12 AD 3.0 67.81 F 7.0 26.0 0.0 -sub-CLNC0099 ses-M18 67.81 F 7.0 26.0 0.0 -sub-CLNC0099 ses-M24 67.81 F 7.0 26.0 0.0 -sub-CLNC0100 ses-M00 CN 1.0 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M03 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M06 CN 1.0 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M12 CN 1.0 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M18 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M24 CN 1.0 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M30 80.08 F 3.0 22.0 1.0 -sub-CLNC0100 ses-M48 CN 1.0 80.08 F 3.0 22.0 1.0 -sub-CLNC0101 ses-M00 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M06 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M108 AD 3.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M12 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M18 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M24 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M30 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M36 MCI 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M42 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M48 MCI 2.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M54 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M60 MCI 2.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M66 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M72 MCI 2.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M78 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M84 MCI 2.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M90 71.02 M 5.0 22.0 0.0 -sub-CLNC0101 ses-M96 5.0 71.02 M 5.0 22.0 0.0 -sub-CLNC0102 ses-M00 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M06 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M12 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M18 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M24 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M30 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M36 MCI 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M48 MCI 2.0 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M54 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M60 MCI 2.0 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M66 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M72 MCI 2.0 77.78 F 2.0 26.0 2.0 -sub-CLNC0102 ses-M78 77.78 F 2.0 26.0 2.0 -sub-CLNC0103 ses-M00 MCI 2.0 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M03 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M06 MCI 2.0 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M12 MCI 2.0 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M18 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M24 MCI 2.0 69.58 M 6.0 24.0 2.0 -sub-CLNC0103 ses-M36 MCI 2.0 69.58 M 6.0 24.0 2.0 -sub-CLNC0104 ses-M00 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M03 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M06 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M12 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M18 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M24 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M36 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0104 ses-M48 MCI 2.0 78.88 F 1.0 24.0 2.0 -sub-CLNC0105 ses-M00 AD 69.55 F 4.0 25.0 1.0 -sub-CLNC0105 ses-M06 AD 69.55 F 4.0 25.0 1.0 -sub-CLNC0105 ses-M12 AD 69.55 F 4.0 25.0 1.0 -sub-CLNC0105 ses-M18 69.55 F 4.0 25.0 1.0 -sub-CLNC0105 ses-M24 AD 69.55 F 4.0 25.0 1.0 -sub-CLNC0106 ses-M00 MCI 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M06 MCI 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M12 AD 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M18 AD 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M24 AD 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M30 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M36 AD 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M42 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M48 AD 3.0 84.3 F 7.0 23.0 3.0 -sub-CLNC0106 ses-M54 84.3 F 7.0 23.0 3.0 -sub-CLNC0107 ses-M00 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M06 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M12 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M18 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M24 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M30 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M36 MCI 68.14 F 5.0 28.0 0.0 -sub-CLNC0107 ses-M42 68.14 F 5.0 28.0 0.0 -sub-CLNC0108 ses-M00 AD 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M06 AD 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M12 AD 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M18 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M24 AD 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M30 76.34 F 3.0 26.0 1.0 -sub-CLNC0108 ses-M36 76.34 F 3.0 26.0 1.0 -sub-CLNC0109 ses-M00 CN 1.0 77.36 F 3.0 21.0 0.5 -sub-CLNC0109 ses-M03 77.36 F 3.0 21.0 0.5 -sub-CLNC0109 ses-M06 CN 1.0 77.36 F 3.0 21.0 0.5 -sub-CLNC0109 ses-M12 CN 1.0 77.36 F 3.0 21.0 0.5 -sub-CLNC0109 ses-M36 77.36 F 3.0 21.0 0.5 -sub-CLNC0110 ses-M00 AD 72.53 F 6.0 29.0 1.0 -sub-CLNC0110 ses-M06 AD 72.53 F 6.0 29.0 1.0 -sub-CLNC0111 ses-M00 MCI 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M06 MCI 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M12 MCI 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M18 MCI 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M24 AD 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M30 78.21 M 8.0 26.0 0.0 -sub-CLNC0111 ses-M36 78.21 M 8.0 26.0 0.0 -sub-CLNC0112 ses-M00 MCI 2.0 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M03 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M06 MCI 2.0 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M12 MCI 2.0 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M18 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M24 MCI 2.0 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M30 82.44 F 5.0 24.0 3.0 -sub-CLNC0112 ses-M36 5.0 82.44 F 5.0 24.0 3.0 -sub-CLNC0113 ses-M00 CN 1.0 67.4 M 8.0 25.0 2.0 -sub-CLNC0113 ses-M03 67.4 M 8.0 25.0 2.0 -sub-CLNC0113 ses-M12 CN 1.0 67.4 M 8.0 25.0 2.0 -sub-CLNC0113 ses-M36 CN 1.0 67.4 M 8.0 25.0 2.0 -sub-CLNC0114 ses-M00 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M06 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M12 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M18 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M24 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M30 66.54 M 5.0 27.0 1.0 -sub-CLNC0114 ses-M36 MCI 66.54 M 5.0 27.0 1.0 -sub-CLNC0115 ses-M00 MCI 2.0 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M03 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M06 MCI 2.0 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M12 MCI 2.0 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M18 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M24 5.0 77.5 M 4.0 24.0 2.0 -sub-CLNC0115 ses-M30 77.5 M 4.0 24.0 2.0 -sub-CLNC0116 ses-M00 MCI 2.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M03 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M06 5.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M12 5.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M18 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M24 AD 3.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M36 AD 3.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0116 ses-M48 AD 3.0 76.82 M 2.0 20.0 0.0 -sub-CLNC0117 ses-M00 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M06 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M12 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M18 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M24 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M30 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M36 MCI 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M42 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M48 MCI 2.0 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M54 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M60 MCI 2.0 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M66 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M72 MCI 2.0 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M78 67.85 M 2.0 28.0 0.5 -sub-CLNC0117 ses-M84 MCI 2.0 67.85 M 2.0 28.0 0.5 -sub-CLNC0118 ses-M00 MCI 2.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M03 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M06 MCI 2.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M12 MCI 2.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M18 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M24 5.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M30 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M36 AD 3.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M48 AD 3.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0118 ses-M60 AD 3.0 61.57 F 6.0 22.0 3.0 -sub-CLNC0119 ses-M00 MCI 2.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M03 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M06 MCI 2.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M12 MCI 2.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M18 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M24 MCI 2.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M36 5.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0119 ses-M48 AD 3.0 76.17 M 4.0 22.0 1.0 -sub-CLNC0120 ses-M00 MCI 2.0 82.39 M 6.0 20.0 1.0 -sub-CLNC0120 ses-M03 82.39 M 6.0 20.0 1.0 -sub-CLNC0120 ses-M06 MCI 2.0 82.39 M 6.0 20.0 1.0 -sub-CLNC0120 ses-M12 MCI 2.0 82.39 M 6.0 20.0 1.0 -sub-CLNC0120 ses-M18 82.39 M 6.0 20.0 1.0 -sub-CLNC0120 ses-M24 5.0 82.39 M 6.0 20.0 1.0 -sub-CLNC0121 ses-M00 MCI 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M06 MCI 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M12 MCI 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M18 MCI 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M24 AD 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M30 68.75 M 2.0 22.0 1.0 -sub-CLNC0121 ses-M36 68.75 M 2.0 22.0 1.0 -sub-CLNC0122 ses-M00 MCI 2.0 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M03 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M06 MCI 2.0 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M12 MCI 2.0 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M18 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M24 MCI 2.0 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M30 69.3 F 5.0 24.0 2.0 -sub-CLNC0122 ses-M60 MCI 2.0 69.3 F 5.0 24.0 2.0 -sub-CLNC0123 ses-M00 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M03 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M06 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M12 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M18 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M24 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M36 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0123 ses-M48 MCI 2.0 72.23 F 2.0 27.0 0.5 -sub-CLNC0124 ses-M00 MCI 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M06 AD 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M12 AD 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M18 AD 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M24 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M30 68.9 M 4.0 20.0 3.0 -sub-CLNC0124 ses-M36 68.9 M 4.0 20.0 3.0 -sub-CLNC0125 ses-M00 MCI 2.0 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M03 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M06 MCI 2.0 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M12 MCI 2.0 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M18 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M24 MCI 2.0 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M30 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M36 7.0 81.53 F 4.0 20.0 0.5 -sub-CLNC0125 ses-M48 81.53 F 4.0 20.0 0.5 -sub-CLNC0126 ses-M00 CN 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M06 CN 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M12 CN 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M18 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M24 CN 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M30 73.88 M 3.0 27.0 0.5 -sub-CLNC0126 ses-M36 CN 73.88 M 3.0 27.0 0.5 -sub-CLNC0127 ses-M00 CN 1.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M03 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M06 CN 1.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M12 CN 1.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M18 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M24 CN 1.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M36 4.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0127 ses-M48 MCI 2.0 72.38 M 9.0 27.0 0.0 -sub-CLNC0128 ses-M00 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M03 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M06 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M12 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M18 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M24 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M36 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0128 ses-M48 MCI 2.0 79.81 F 6.0 24.0 0.5 -sub-CLNC0129 ses-M00 CN 1.0 82.24 M 8.0 25.0 3.0 -sub-CLNC0129 ses-M03 82.24 M 8.0 25.0 3.0 -sub-CLNC0129 ses-M24 4.0 82.24 M 8.0 25.0 3.0 -sub-CLNC0129 ses-M36 MCI 2.0 82.24 M 8.0 25.0 3.0 -sub-CLNC0130 ses-M00 AD 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M06 AD 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M12 AD 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M18 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M24 AD 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M30 76.67 F 5.0 20.0 0.0 -sub-CLNC0130 ses-M36 76.67 F 5.0 20.0 0.0 -sub-CLNC0131 ses-M00 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M06 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M108 5.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M12 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M18 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M24 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M30 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M36 MCI 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M42 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M48 MCI 2.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M60 7.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M66 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M72 4.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M78 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M84 MCI 2.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0131 ses-M96 MCI 2.0 88.46 M 4.0 28.0 0.5 -sub-CLNC0132 ses-M00 MCI 2.0 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M03 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M06 MCI 2.0 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M12 7.0 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M18 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M24 CN 1.0 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M36 CN 1.0 76.53 M 5.0 20.0 2.0 -sub-CLNC0132 ses-M48 76.53 M 5.0 20.0 2.0 -sub-CLNC0133 ses-M00 CN 1.0 79.72 M 8.0 25.0 1.0 -sub-CLNC0133 ses-M03 79.72 M 8.0 25.0 1.0 -sub-CLNC0133 ses-M06 CN 1.0 79.72 M 8.0 25.0 1.0 -sub-CLNC0133 ses-M24 CN 1.0 79.72 M 8.0 25.0 1.0 -sub-CLNC0134 ses-M00 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M06 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M108 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M114 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M12 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M18 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M24 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M30 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M36 MCI 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M60 MCI 2.0 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M72 MCI 2.0 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M78 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M90 83.36 M 8.0 22.0 3.0 -sub-CLNC0134 ses-M96 MCI 2.0 83.36 M 8.0 22.0 3.0 -sub-CLNC0135 ses-M00 CN 1.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M03 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M06 CN 1.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M12 CN 1.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M18 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M24 4.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M30 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M36 MCI 2.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0135 ses-M48 MCI 2.0 73.32 F 1.0 27.0 1.0 -sub-CLNC0136 ses-M00 MCI 2.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M03 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M06 MCI 2.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M12 MCI 2.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M18 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M24 7.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M36 CN 1.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0136 ses-M48 CN 1.0 73.55 F 4.0 24.0 0.5 -sub-CLNC0137 ses-M00 AD 3.0 76.16 F 8.0 28.0 1.0 -sub-CLNC0137 ses-M03 76.16 F 8.0 28.0 1.0 -sub-CLNC0137 ses-M06 AD 3.0 76.16 F 8.0 28.0 1.0 -sub-CLNC0138 ses-M00 CN 1.0 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M03 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M06 CN 1.0 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M12 CN 1.0 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M18 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M24 CN 1.0 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M30 81.28 F 2.0 22.0 0.0 -sub-CLNC0138 ses-M48 CN 1.0 81.28 F 2.0 22.0 0.0 -sub-CLNC0139 ses-M00 MCI 2.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M03 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M06 MCI 2.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M12 5.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M18 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M24 AD 3.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M36 AD 3.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0139 ses-M48 AD 3.0 66.09 M 2.0 28.0 0.5 -sub-CLNC0140 ses-M00 AD 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M06 AD 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M12 AD 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M18 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M24 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M30 80.51 F 6.0 28.0 2.0 -sub-CLNC0140 ses-M36 80.51 F 6.0 28.0 2.0 -sub-CLNC0141 ses-M00 AD 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M06 AD 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M12 AD 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M18 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M24 AD 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M30 85.57 F 8.0 23.0 1.0 -sub-CLNC0141 ses-M36 85.57 F 8.0 23.0 1.0 -sub-CLNC0142 ses-M00 CN 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M06 CN 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M12 CN 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M18 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M24 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M30 60.8 F 7.0 24.0 0.0 -sub-CLNC0142 ses-M36 60.8 F 7.0 24.0 0.0 -sub-CLNC0143 ses-M00 CN 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M06 MCI 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M12 MCI 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M18 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M24 MCI 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M30 67.49 M 6.0 26.0 0.0 -sub-CLNC0143 ses-M36 AD 67.49 M 6.0 26.0 0.0 -sub-CLNC0144 ses-M00 MCI 2.0 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M03 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M06 MCI 2.0 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M12 MCI 2.0 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M18 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M24 MCI 2.0 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M36 72.64 M 9.0 27.0 2.0 -sub-CLNC0144 ses-M48 MCI 2.0 72.64 M 9.0 27.0 2.0 -sub-CLNC0145 ses-M00 AD 3.0 68.68 F 5.0 27.0 1.0 -sub-CLNC0145 ses-M03 68.68 F 5.0 27.0 1.0 -sub-CLNC0145 ses-M06 AD 3.0 68.68 F 5.0 27.0 1.0 -sub-CLNC0145 ses-M12 AD 3.0 68.68 F 5.0 27.0 1.0 -sub-CLNC0145 ses-M18 68.68 F 5.0 27.0 1.0 -sub-CLNC0145 ses-M24 AD 3.0 68.68 F 5.0 27.0 1.0 -sub-CLNC0146 ses-M00 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M03 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M06 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M12 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M18 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M24 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M30 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M36 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0146 ses-M48 MCI 2.0 75.9 M 2.0 20.0 0.5 -sub-CLNC0147 ses-M00 CN 1.0 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M03 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M06 CN 1.0 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M12 CN 1.0 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M18 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M24 CN 1.0 85.32 F 6.0 22.0 1.0 -sub-CLNC0147 ses-M48 CN 1.0 85.32 F 6.0 22.0 1.0 -sub-CLNC0148 ses-M00 MCI 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M06 MCI 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M12 MCI 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M18 MCI 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M24 AD 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M30 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M36 AD 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M42 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M48 AD 3.0 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M54 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M60 AD 3.0 70.01 F 2.0 23.0 3.0 -sub-CLNC0148 ses-M66 70.01 F 2.0 23.0 3.0 -sub-CLNC0149 ses-M00 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M06 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M108 AD 3.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M12 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M18 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M24 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M30 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M36 MCI 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M48 5.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M54 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M60 AD 3.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M66 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M72 AD 3.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M78 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M84 AD 3.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0149 ses-M96 AD 3.0 75.94 F 2.0 24.0 1.0 -sub-CLNC0150 ses-M00 CN 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M06 CN 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M108 MCI 2.0 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M12 CN 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M18 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M24 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M30 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M36 CN 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M48 4.0 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M54 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M72 MCI 2.0 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M78 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M84 MCI 2.0 74.82 F 7.0 22.0 2.0 -sub-CLNC0150 ses-M96 MCI 2.0 74.82 F 7.0 22.0 2.0 -sub-CLNC0151 ses-M00 MCI 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M06 MCI 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M12 AD 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M18 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M24 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M30 79.72 M 3.0 28.0 2.0 -sub-CLNC0151 ses-M36 79.72 M 3.0 28.0 2.0 -sub-CLNC0152 ses-M00 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M06 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M12 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M18 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M24 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M30 67.13 M 6.0 23.0 2.0 -sub-CLNC0152 ses-M36 MCI 67.13 M 6.0 23.0 2.0 -sub-CLNC0153 ses-M00 MCI 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M06 MCI 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M12 MCI 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M18 AD 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M24 AD 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M30 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M36 AD 3.0 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M42 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M48 AD 3.0 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M54 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M60 AD 3.0 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M66 79.94 M 4.0 27.0 1.0 -sub-CLNC0153 ses-M72 AD 3.0 79.94 M 4.0 27.0 1.0 -sub-CLNC0154 ses-M00 MCI 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M06 MCI 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M12 MCI 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M18 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M24 MCI 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M30 60.65 F 3.0 21.0 1.0 -sub-CLNC0154 ses-M36 AD 60.65 F 3.0 21.0 1.0 -sub-CLNC0155 ses-M00 AD 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M06 AD 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M12 AD 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M18 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M24 AD 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M30 65.86 F 5.0 29.0 2.0 -sub-CLNC0155 ses-M36 65.86 F 5.0 29.0 2.0 -sub-CLNC0156 ses-M00 MCI 2.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M03 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M06 MCI 2.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M12 5.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M18 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M24 8.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M36 MCI 2.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0156 ses-M48 CN 1.0 62.08 F 3.0 27.0 0.0 -sub-CLNC0157 ses-M00 MCI 2.0 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M03 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M06 MCI 2.0 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M12 MCI 2.0 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M18 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M24 MCI 2.0 76.58 M 9.0 20.0 0.0 -sub-CLNC0157 ses-M36 MCI 2.0 76.58 M 9.0 20.0 0.0 -sub-CLNC0158 ses-M00 MCI 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M06 MCI 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M12 MCI 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M18 CN 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M24 CN 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M30 60.34 F 9.0 24.0 1.0 -sub-CLNC0158 ses-M36 CN 60.34 F 9.0 24.0 1.0 -sub-CLNC0159 ses-M00 CN 1.0 64.36 F 8.0 26.0 1.0 -sub-CLNC0159 ses-M03 64.36 F 8.0 26.0 1.0 -sub-CLNC0159 ses-M06 CN 1.0 64.36 F 8.0 26.0 1.0 -sub-CLNC0159 ses-M12 CN 1.0 64.36 F 8.0 26.0 1.0 -sub-CLNC0159 ses-M24 CN 1.0 64.36 F 8.0 26.0 1.0 -sub-CLNC0160 ses-M00 CN 1.0 72.85 F 5.0 21.0 3.0 -sub-CLNC0160 ses-M03 72.85 F 5.0 21.0 3.0 -sub-CLNC0160 ses-M06 CN 1.0 72.85 F 5.0 21.0 3.0 -sub-CLNC0160 ses-M12 4.0 72.85 F 5.0 21.0 3.0 -sub-CLNC0160 ses-M18 72.85 F 5.0 21.0 3.0 -sub-CLNC0161 ses-M00 CN 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M06 CN 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M12 CN 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M120 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M18 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M24 CN 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M30 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M36 CN 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M42 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M48 CN 1.0 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M54 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M60 CN 1.0 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M72 CN 1.0 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M78 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M84 CN 1.0 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M90 66.77 F 5.0 24.0 1.0 -sub-CLNC0161 ses-M96 CN 1.0 66.77 F 5.0 24.0 1.0 -sub-CLNC0162 ses-M00 AD 3.0 67.67 M 3.0 23.0 2.0 -sub-CLNC0162 ses-M03 67.67 M 3.0 23.0 2.0 -sub-CLNC0162 ses-M06 AD 3.0 67.67 M 3.0 23.0 2.0 -sub-CLNC0162 ses-M12 AD 3.0 67.67 M 3.0 23.0 2.0 -sub-CLNC0163 ses-M00 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M06 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M12 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M18 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M24 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M30 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M36 MCI 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M48 MCI 2.0 66.04 F 4.0 22.0 2.0 -sub-CLNC0163 ses-M54 66.04 F 4.0 22.0 2.0 -sub-CLNC0164 ses-M00 MCI 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M06 MCI 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M12 MCI 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M18 AD 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M24 AD 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M30 84.1 M 6.0 22.0 3.0 -sub-CLNC0164 ses-M36 AD 84.1 M 6.0 22.0 3.0 -sub-CLNC0165 ses-M00 AD 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M06 AD 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M12 AD 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M18 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M24 AD 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M30 73.76 F 3.0 28.0 2.0 -sub-CLNC0165 ses-M36 73.76 F 3.0 28.0 2.0 -sub-CLNC0166 ses-M00 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M06 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M12 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M18 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M24 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M30 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M36 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M42 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M48 CN 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M60 CN 1.0 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M66 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M72 CN 1.0 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M78 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M84 CN 1.0 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M90 83.87 F 7.0 26.0 2.0 -sub-CLNC0166 ses-M96 CN 1.0 83.87 F 7.0 26.0 2.0 -sub-CLNC0167 ses-M00 AD 3.0 87.23 M 6.0 26.0 0.0 -sub-CLNC0167 ses-M03 87.23 M 6.0 26.0 0.0 -sub-CLNC0167 ses-M06 AD 3.0 87.23 M 6.0 26.0 0.0 -sub-CLNC0168 ses-M00 CN 1.0 71.1 M 2.0 22.0 3.0 -sub-CLNC0168 ses-M06 CN 1.0 71.1 M 2.0 22.0 3.0 -sub-CLNC0168 ses-M12 CN 1.0 71.1 M 2.0 22.0 3.0 -sub-CLNC0168 ses-M18 71.1 M 2.0 22.0 3.0 -sub-CLNC0168 ses-M24 CN 1.0 71.1 M 2.0 22.0 3.0 -sub-CLNC0168 ses-M48 71.1 M 2.0 22.0 3.0 -sub-CLNC0169 ses-M00 AD 3.0 67.56 M 8.0 21.0 0.0 -sub-CLNC0169 ses-M03 67.56 M 8.0 21.0 0.0 -sub-CLNC0169 ses-M06 AD 3.0 67.56 M 8.0 21.0 0.0 -sub-CLNC0169 ses-M12 AD 3.0 67.56 M 8.0 21.0 0.0 -sub-CLNC0169 ses-M18 67.56 M 8.0 21.0 0.0 -sub-CLNC0169 ses-M30 67.56 M 8.0 21.0 0.0 -sub-CLNC0170 ses-M00 AD 3.0 80.06 M 8.0 24.0 0.5 -sub-CLNC0170 ses-M06 AD 3.0 80.06 M 8.0 24.0 0.5 -sub-CLNC0171 ses-M00 MCI 2.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M03 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M06 MCI 2.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M12 5.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M24 AD 3.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M30 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M36 AD 3.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M48 AD 3.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0171 ses-M60 MCI 2.0 79.7 F 2.0 20.0 3.0 -sub-CLNC0172 ses-M00 MCI 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M06 MCI 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M12 AD 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M18 AD 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M24 AD 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M30 77.28 M 5.0 21.0 0.0 -sub-CLNC0172 ses-M36 77.28 M 5.0 21.0 0.0 -sub-CLNC0173 ses-M00 AD 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M06 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M12 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M18 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M24 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M30 68.65 M 1.0 20.0 0.0 -sub-CLNC0173 ses-M36 68.65 M 1.0 20.0 0.0 -sub-CLNC0174 ses-M00 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M03 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M06 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M12 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M18 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M24 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M30 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M36 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0174 ses-M48 MCI 2.0 61.37 F 8.0 20.0 3.0 -sub-CLNC0175 ses-M00 MCI 2.0 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M03 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M06 MCI 2.0 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M12 MCI 2.0 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M18 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M24 80.02 F 5.0 29.0 2.0 -sub-CLNC0175 ses-M36 MCI 2.0 80.02 F 5.0 29.0 2.0 -sub-CLNC0176 ses-M00 MCI 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M06 MCI 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M12 MCI 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M18 MCI 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M24 MCI 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M30 75.45 F 5.0 26.0 1.0 -sub-CLNC0176 ses-M36 AD 75.45 F 5.0 26.0 1.0 -sub-CLNC0177 ses-M00 AD 3.0 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M03 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M06 AD 3.0 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M12 AD 3.0 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M18 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M30 77.79 F 6.0 28.0 2.0 -sub-CLNC0177 ses-M42 77.79 F 6.0 28.0 2.0 -sub-CLNC0178 ses-M00 MCI 2.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M03 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M06 5.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M12 MCI 2.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M18 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M24 5.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M36 AD 3.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0178 ses-M48 AD 3.0 73.02 M 3.0 28.0 3.0 -sub-CLNC0179 ses-M00 MCI 2.0 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M03 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M12 MCI 2.0 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M18 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M24 MCI 2.0 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M30 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M36 MCI 2.0 82.68 F 2.0 23.0 0.5 -sub-CLNC0179 ses-M48 MCI 2.0 82.68 F 2.0 23.0 0.5 -sub-CLNC0180 ses-M00 CN 1.0 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M03 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M06 CN 1.0 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M12 CN 1.0 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M18 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M24 CN 1.0 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M30 78.54 M 8.0 25.0 2.0 -sub-CLNC0180 ses-M48 CN 1.0 78.54 M 8.0 25.0 2.0 -sub-CLNC0181 ses-M00 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M06 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M108 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M12 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M120 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M18 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M24 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M30 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M36 MCI 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M42 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M48 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M54 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M60 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M66 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M72 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M78 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M84 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M90 77.39 M 9.0 24.0 1.0 -sub-CLNC0181 ses-M96 MCI 2.0 77.39 M 9.0 24.0 1.0 -sub-CLNC0182 ses-M00 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M03 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M06 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M12 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M18 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M24 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M30 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M36 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M48 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0182 ses-M60 MCI 2.0 65.95 F 2.0 21.0 0.0 -sub-CLNC0183 ses-M00 AD 3.0 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M03 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M06 AD 3.0 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M12 AD 3.0 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M18 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M24 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M30 81.34 M 9.0 25.0 0.5 -sub-CLNC0183 ses-M42 81.34 M 9.0 25.0 0.5 -sub-CLNC0184 ses-M00 MCI 2.0 84.19 F 1.0 28.0 3.0 -sub-CLNC0184 ses-M03 84.19 F 1.0 28.0 3.0 -sub-CLNC0184 ses-M06 MCI 2.0 84.19 F 1.0 28.0 3.0 -sub-CLNC0184 ses-M12 MCI 2.0 84.19 F 1.0 28.0 3.0 -sub-CLNC0185 ses-M00 CN 1.0 75.87 F 1.0 24.0 0.5 -sub-CLNC0186 ses-M00 MCI 78.23 M 3.0 26.0 2.0 -sub-CLNC0186 ses-M06 MCI 78.23 M 3.0 26.0 2.0 -sub-CLNC0186 ses-M12 AD 78.23 M 3.0 26.0 2.0 -sub-CLNC0186 ses-M18 AD 78.23 M 3.0 26.0 2.0 -sub-CLNC0187 ses-M00 MCI 2.0 65.02 M 5.0 22.0 1.0 -sub-CLNC0187 ses-M03 65.02 M 5.0 22.0 1.0 -sub-CLNC0187 ses-M06 MCI 2.0 65.02 M 5.0 22.0 1.0 -sub-CLNC0187 ses-M12 MCI 2.0 65.02 M 5.0 22.0 1.0 -sub-CLNC0187 ses-M18 65.02 M 5.0 22.0 1.0 -sub-CLNC0188 ses-M00 MCI 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M06 MCI 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M108 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M12 MCI 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M18 MCI 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M24 MCI 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M30 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M36 CN 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M42 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M48 CN 1.0 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M54 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M60 CN 1.0 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M66 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M72 7.0 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M78 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M84 CN 1.0 68.72 M 3.0 27.0 1.0 -sub-CLNC0188 ses-M96 CN 1.0 68.72 M 3.0 27.0 1.0 -sub-CLNC0189 ses-M00 CN 67.89 F 4.0 23.0 0.5 -sub-CLNC0189 ses-M06 CN 67.89 F 4.0 23.0 0.5 -sub-CLNC0189 ses-M12 CN 67.89 F 4.0 23.0 0.5 -sub-CLNC0189 ses-M18 67.89 F 4.0 23.0 0.5 -sub-CLNC0189 ses-M24 CN 67.89 F 4.0 23.0 0.5 -sub-CLNC0189 ses-M30 67.89 F 4.0 23.0 0.5 -sub-CLNC0190 ses-M00 CN 1.0 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M03 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M06 CN 1.0 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M12 CN 1.0 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M18 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M24 CN 1.0 70.42 M 9.0 28.0 2.0 -sub-CLNC0190 ses-M48 CN 1.0 70.42 M 9.0 28.0 2.0 -sub-CLNC0191 ses-M00 MCI 2.0 56.51 M 1.0 29.0 3.0 -sub-CLNC0191 ses-M03 56.51 M 1.0 29.0 3.0 -sub-CLNC0192 ses-M00 MCI 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M06 MCI 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M12 MCI 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M18 AD 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M24 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M30 58.65 F 4.0 28.0 3.0 -sub-CLNC0192 ses-M36 58.65 F 4.0 28.0 3.0 -sub-CLNC0193 ses-M00 CN 1.0 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M03 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M06 CN 1.0 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M12 CN 1.0 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M18 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M24 CN 1.0 60.44 F 4.0 25.0 3.0 -sub-CLNC0193 ses-M48 60.44 F 4.0 25.0 3.0 -sub-CLNC0194 ses-M00 MCI 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M06 AD 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M12 AD 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M18 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M24 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M30 76.45 M 6.0 22.0 0.5 -sub-CLNC0194 ses-M36 76.45 M 6.0 22.0 0.5 -sub-CLNC0195 ses-M00 MCI 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M06 MCI 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M12 MCI 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M18 AD 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M24 AD 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M30 81.17 M 1.0 24.0 0.5 -sub-CLNC0195 ses-M36 81.17 M 1.0 24.0 0.5 -sub-CLNC0196 ses-M00 AD 3.0 76.42 M 8.0 20.0 2.0 -sub-CLNC0196 ses-M03 76.42 M 8.0 20.0 2.0 -sub-CLNC0196 ses-M06 AD 3.0 76.42 M 8.0 20.0 2.0 -sub-CLNC0196 ses-M18 76.42 M 8.0 20.0 2.0 -sub-CLNC0197 ses-M00 CN 71.27 F 5.0 28.0 3.0 -sub-CLNC0198 ses-M00 CN 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M06 CN 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M108 CN 1.0 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M12 CN 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M18 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M24 CN 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M30 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M36 CN 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M42 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M48 CN 1.0 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M54 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M60 CN 1.0 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M66 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M72 CN 1.0 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M78 66.3 F 2.0 25.0 1.0 -sub-CLNC0198 ses-M84 CN 1.0 66.3 F 2.0 25.0 1.0 -sub-CLNC0199 ses-M00 AD 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M06 AD 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M12 AD 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M18 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M24 AD 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M30 79.31 F 2.0 26.0 2.0 -sub-CLNC0199 ses-M36 79.31 F 2.0 26.0 2.0 -sub-CLNC0200 ses-M00 MCI 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M06 MCI 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M108 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M12 MCI 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M18 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M24 CN 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M30 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M36 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M42 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M54 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M60 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M66 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M72 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M78 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M84 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0200 ses-M96 MCI 2.0 73.32 F 9.0 29.0 1.0 -sub-CLNC0201 ses-M00 MCI 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M06 MCI 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M12 MCI 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M18 AD 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M24 AD 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M30 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M36 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M60 AD 3.0 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M66 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M72 AD 3.0 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M78 78.32 F 3.0 27.0 3.0 -sub-CLNC0201 ses-M84 AD 3.0 78.32 F 3.0 27.0 3.0 -sub-CLNC0202 ses-M00 CN 1.0 70.26 F 4.0 21.0 2.0 -sub-CLNC0203 ses-M00 MCI 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M06 MCI 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M12 MCI 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M18 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M24 AD 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M30 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M36 74.97 F 1.0 21.0 0.5 -sub-CLNC0203 ses-M60 74.97 F 1.0 21.0 0.5 -sub-CLNC0204 ses-M00 AD 3.0 74.18 F 9.0 28.0 3.0 -sub-CLNC0204 ses-M03 74.18 F 9.0 28.0 3.0 -sub-CLNC0204 ses-M06 AD 3.0 74.18 F 9.0 28.0 3.0 -sub-CLNC0204 ses-M12 AD 3.0 74.18 F 9.0 28.0 3.0 -sub-CLNC0205 ses-M00 CN 1.0 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M03 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M06 CN 1.0 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M12 CN 1.0 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M18 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M24 CN 1.0 82.22 M 4.0 29.0 0.0 -sub-CLNC0205 ses-M48 CN 1.0 82.22 M 4.0 29.0 0.0 -sub-CLNC0206 ses-M00 CN 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M06 CN 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M12 CN 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M18 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M24 CN 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M30 69.79 M 5.0 29.0 1.0 -sub-CLNC0206 ses-M36 CN 69.79 M 5.0 29.0 1.0 -sub-CLNC0207 ses-M00 CN 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M06 CN 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M12 CN 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M18 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M24 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M30 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M36 CN 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M48 CN 1.0 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M60 CN 1.0 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M66 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M72 CN 1.0 68.48 F 6.0 22.0 3.0 -sub-CLNC0207 ses-M78 68.48 F 6.0 22.0 3.0 -sub-CLNC0208 ses-M00 MCI 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M06 AD 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M12 AD 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M18 AD 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M24 AD 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M30 65.39 M 8.0 25.0 1.0 -sub-CLNC0208 ses-M36 AD 65.39 M 8.0 25.0 1.0 -sub-CLNC0209 ses-M00 AD 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M06 AD 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M12 AD 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M18 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M24 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M30 71.43 F 7.0 20.0 0.0 -sub-CLNC0209 ses-M36 71.43 F 7.0 20.0 0.0 -sub-CLNC0210 ses-M00 MCI 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M06 MCI 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M12 MCI 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M18 MCI 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M24 AD 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M30 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M36 AD 64.08 M 2.0 20.0 0.0 -sub-CLNC0210 ses-M42 64.08 M 2.0 20.0 0.0 -sub-CLNC0211 ses-M00 MCI 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M06 MCI 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M12 MCI 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M18 MCI 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M24 MCI 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M30 90.66 M 4.0 23.0 0.0 -sub-CLNC0211 ses-M36 AD 90.66 M 4.0 23.0 0.0 -sub-CLNC0212 ses-M00 AD 3.0 75.47 M 2.0 22.0 2.0 -sub-CLNC0212 ses-M03 75.47 M 2.0 22.0 2.0 -sub-CLNC0212 ses-M06 AD 3.0 75.47 M 2.0 22.0 2.0 -sub-CLNC0212 ses-M12 AD 3.0 75.47 M 2.0 22.0 2.0 -sub-CLNC0213 ses-M00 CN 1.0 63.7 M 2.0 28.0 3.0 -sub-CLNC0213 ses-M03 63.7 M 2.0 28.0 3.0 -sub-CLNC0213 ses-M06 CN 1.0 63.7 M 2.0 28.0 3.0 -sub-CLNC0213 ses-M12 CN 1.0 63.7 M 2.0 28.0 3.0 -sub-CLNC0213 ses-M24 CN 1.0 63.7 M 2.0 28.0 3.0 -sub-CLNC0214 ses-M00 AD 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M06 AD 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M12 AD 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M18 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M24 AD 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M30 73.79 M 1.0 22.0 2.0 -sub-CLNC0214 ses-M36 73.79 M 1.0 22.0 2.0 -sub-CLNC0215 ses-M00 MCI 2.0 69.14 F 7.0 28.0 2.0 -sub-CLNC0216 ses-M00 MCI 2.0 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M03 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M06 MCI 2.0 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M12 MCI 2.0 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M18 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M24 MCI 2.0 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M30 74.54 F 3.0 29.0 1.0 -sub-CLNC0216 ses-M36 5.0 74.54 F 3.0 29.0 1.0 -sub-CLNC0217 ses-M00 MCI 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M06 MCI 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M12 MCI 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M18 MCI 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M24 MCI 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M30 69.55 M 6.0 27.0 0.5 -sub-CLNC0217 ses-M36 69.55 M 6.0 27.0 0.5 -sub-CLNC0218 ses-M00 AD 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M06 AD 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M12 AD 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M18 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M24 AD 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M30 81.48 F 9.0 22.0 2.0 -sub-CLNC0218 ses-M36 81.48 F 9.0 22.0 2.0 -sub-CLNC0219 ses-M00 AD 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M06 AD 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M12 AD 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M18 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M24 AD 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M30 73.8 M 9.0 29.0 0.0 -sub-CLNC0219 ses-M36 73.8 M 9.0 29.0 0.0 -sub-CLNC0220 ses-M00 AD 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M06 AD 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M12 AD 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M18 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M24 AD 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M30 68.35 F 2.0 25.0 2.0 -sub-CLNC0220 ses-M36 68.35 F 2.0 25.0 2.0 -sub-CLNC0221 ses-M00 MCI 2.0 69.0 F 9.0 26.0 3.0 -sub-CLNC0221 ses-M03 69.0 F 9.0 26.0 3.0 -sub-CLNC0221 ses-M06 MCI 2.0 69.0 F 9.0 26.0 3.0 -sub-CLNC0221 ses-M12 MCI 2.0 69.0 F 9.0 26.0 3.0 -sub-CLNC0221 ses-M18 69.0 F 9.0 26.0 3.0 -sub-CLNC0222 ses-M00 MCI 2.0 70.63 F 9.0 24.0 3.0 -sub-CLNC0222 ses-M03 70.63 F 9.0 24.0 3.0 -sub-CLNC0222 ses-M12 MCI 2.0 70.63 F 9.0 24.0 3.0 -sub-CLNC0222 ses-M18 70.63 F 9.0 24.0 3.0 -sub-CLNC0222 ses-M24 MCI 2.0 70.63 F 9.0 24.0 3.0 -sub-CLNC0222 ses-M30 70.63 F 9.0 24.0 3.0 -sub-CLNC0223 ses-M00 CN 78.6 F 8.0 25.0 3.0 -sub-CLNC0223 ses-M06 CN 78.6 F 8.0 25.0 3.0 -sub-CLNC0224 ses-M00 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M06 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M12 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M18 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M24 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M30 72.35 M 4.0 26.0 1.0 -sub-CLNC0224 ses-M36 MCI 72.35 M 4.0 26.0 1.0 -sub-CLNC0225 ses-M00 CN 1.0 82.68 M 3.0 26.0 0.0 -sub-CLNC0225 ses-M03 82.68 M 3.0 26.0 0.0 -sub-CLNC0225 ses-M06 CN 1.0 82.68 M 3.0 26.0 0.0 -sub-CLNC0226 ses-M00 AD 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M06 AD 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M12 AD 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M18 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M24 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M30 84.57 M 4.0 27.0 1.0 -sub-CLNC0226 ses-M36 84.57 M 4.0 27.0 1.0 -sub-CLNC0227 ses-M00 CN 1.0 84.71 M 6.0 22.0 3.0 -sub-CLNC0227 ses-M06 CN 1.0 84.71 M 6.0 22.0 3.0 -sub-CLNC0227 ses-M24 CN 1.0 84.71 M 6.0 22.0 3.0 -sub-CLNC0228 ses-M00 CN 1.0 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M03 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M06 CN 1.0 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M12 CN 1.0 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M18 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M24 CN 1.0 71.89 F 6.0 29.0 3.0 -sub-CLNC0228 ses-M48 CN 1.0 71.89 F 6.0 29.0 3.0 -sub-CLNC0229 ses-M00 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M06 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M12 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M18 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M24 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M30 69.25 F 5.0 24.0 0.5 -sub-CLNC0229 ses-M36 MCI 69.25 F 5.0 24.0 0.5 -sub-CLNC0230 ses-M00 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M06 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M12 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M18 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M24 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M30 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M36 MCI 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M60 MCI 2.0 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M66 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M72 5.0 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M78 69.48 M 3.0 20.0 0.5 -sub-CLNC0230 ses-M84 AD 3.0 69.48 M 3.0 20.0 0.5 -sub-CLNC0231 ses-M00 AD 3.0 68.15 M 8.0 23.0 2.0 -sub-CLNC0231 ses-M03 68.15 M 8.0 23.0 2.0 -sub-CLNC0231 ses-M06 AD 3.0 68.15 M 8.0 23.0 2.0 -sub-CLNC0231 ses-M12 AD 3.0 68.15 M 8.0 23.0 2.0 -sub-CLNC0231 ses-M18 68.15 M 8.0 23.0 2.0 -sub-CLNC0232 ses-M00 CN 1.0 65.49 F 5.0 20.0 1.0 -sub-CLNC0232 ses-M06 CN 1.0 65.49 F 5.0 20.0 1.0 -sub-CLNC0232 ses-M24 CN 1.0 65.49 F 5.0 20.0 1.0 -sub-CLNC0233 ses-M00 MCI 2.0 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M03 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M06 MCI 2.0 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M12 MCI 2.0 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M18 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M24 MCI 2.0 76.1 F 5.0 26.0 2.0 -sub-CLNC0233 ses-M36 MCI 2.0 76.1 F 5.0 26.0 2.0 -sub-CLNC0234 ses-M00 MCI 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M06 MCI 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M12 MCI 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M18 MCI 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M24 MCI 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M30 58.63 M 7.0 22.0 3.0 -sub-CLNC0234 ses-M36 58.63 M 7.0 22.0 3.0 -sub-CLNC0235 ses-M00 CN 1.0 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M03 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M06 CN 1.0 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M12 CN 1.0 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M18 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M24 CN 1.0 66.8 F 4.0 22.0 0.0 -sub-CLNC0235 ses-M48 4.0 66.8 F 4.0 22.0 0.0 -sub-CLNC0236 ses-M00 AD 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M06 AD 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M12 AD 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M18 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M24 AD 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M30 73.94 F 5.0 28.0 3.0 -sub-CLNC0236 ses-M36 73.94 F 5.0 28.0 3.0 -sub-CLNC0237 ses-M00 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M06 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M12 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M18 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M24 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M30 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M36 MCI 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M42 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M48 AD 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M54 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M60 AD 3.0 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M66 75.32 M 3.0 21.0 3.0 -sub-CLNC0237 ses-M78 75.32 M 3.0 21.0 3.0 -sub-CLNC0238 ses-M00 AD 3.0 75.0 M 8.0 22.0 0.5 -sub-CLNC0238 ses-M03 75.0 M 8.0 22.0 0.5 -sub-CLNC0238 ses-M06 AD 3.0 75.0 M 8.0 22.0 0.5 -sub-CLNC0238 ses-M12 AD 3.0 75.0 M 8.0 22.0 0.5 -sub-CLNC0238 ses-M18 75.0 M 8.0 22.0 0.5 -sub-CLNC0239 ses-M00 CN 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M06 CN 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M12 CN 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M120 4.0 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M18 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M24 CN 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M30 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M36 CN 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M72 MCI 2.0 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M78 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M84 MCI 2.0 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M90 77.48 F 1.0 22.0 3.0 -sub-CLNC0239 ses-M96 CN 1.0 77.48 F 1.0 22.0 3.0 -sub-CLNC0240 ses-M00 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M06 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M12 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M18 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M24 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M30 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M36 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M48 MCI 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M60 MCI 2.0 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M72 MCI 2.0 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M78 69.33 F 4.0 28.0 0.0 -sub-CLNC0240 ses-M84 5.0 69.33 F 4.0 28.0 0.0 -sub-CLNC0241 ses-M00 AD 3.0 69.58 F 5.0 23.0 1.0 -sub-CLNC0241 ses-M03 69.58 F 5.0 23.0 1.0 -sub-CLNC0241 ses-M06 AD 3.0 69.58 F 5.0 23.0 1.0 -sub-CLNC0241 ses-M12 AD 3.0 69.58 F 5.0 23.0 1.0 -sub-CLNC0241 ses-M18 69.58 F 5.0 23.0 1.0 -sub-CLNC0242 ses-M00 MCI 2.0 61.07 F 8.0 25.0 1.0 -sub-CLNC0242 ses-M03 61.07 F 8.0 25.0 1.0 -sub-CLNC0242 ses-M06 MCI 2.0 61.07 F 8.0 25.0 1.0 -sub-CLNC0242 ses-M12 MCI 2.0 61.07 F 8.0 25.0 1.0 -sub-CLNC0242 ses-M24 MCI 2.0 61.07 F 8.0 25.0 1.0 -sub-CLNC0242 ses-M36 MCI 2.0 61.07 F 8.0 25.0 1.0 -sub-CLNC0243 ses-M00 AD 3.0 75.48 F 4.0 24.0 1.0 -sub-CLNC0243 ses-M03 75.48 F 4.0 24.0 1.0 -sub-CLNC0243 ses-M06 AD 3.0 75.48 F 4.0 24.0 1.0 -sub-CLNC0243 ses-M12 AD 3.0 75.48 F 4.0 24.0 1.0 -sub-CLNC0243 ses-M18 75.48 F 4.0 24.0 1.0 -sub-CLNC0244 ses-M00 MCI 2.0 70.72 M 9.0 28.0 1.0 -sub-CLNC0244 ses-M03 70.72 M 9.0 28.0 1.0 -sub-CLNC0245 ses-M00 MCI 2.0 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M03 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M06 MCI 2.0 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M12 MCI 2.0 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M18 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M24 MCI 2.0 79.16 M 9.0 26.0 3.0 -sub-CLNC0245 ses-M36 MCI 2.0 79.16 M 9.0 26.0 3.0 -sub-CLNC0246 ses-M00 MCI 2.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M03 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M06 MCI 2.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M12 7.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M18 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M24 CN 1.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M30 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M36 CN 1.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0246 ses-M48 CN 1.0 58.01 F 2.0 20.0 2.0 -sub-CLNC0247 ses-M00 MCI 2.0 81.0 F 7.0 22.0 1.0 -sub-CLNC0247 ses-M03 81.0 F 7.0 22.0 1.0 -sub-CLNC0247 ses-M06 MCI 2.0 81.0 F 7.0 22.0 1.0 -sub-CLNC0247 ses-M12 MCI 2.0 81.0 F 7.0 22.0 1.0 -sub-CLNC0247 ses-M18 81.0 F 7.0 22.0 1.0 -sub-CLNC0247 ses-M30 81.0 F 7.0 22.0 1.0 -sub-CLNC0248 ses-M00 AD 3.0 87.28 F 7.0 28.0 1.0 -sub-CLNC0248 ses-M03 87.28 F 7.0 28.0 1.0 -sub-CLNC0248 ses-M06 AD 3.0 87.28 F 7.0 28.0 1.0 -sub-CLNC0249 ses-M00 AD 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M06 AD 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M12 AD 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M18 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M24 AD 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M30 79.73 F 4.0 25.0 3.0 -sub-CLNC0249 ses-M36 79.73 F 4.0 25.0 3.0 -sub-CLNC0250 ses-M00 AD 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M06 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M12 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M18 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M24 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M30 82.15 F 4.0 24.0 1.0 -sub-CLNC0250 ses-M36 82.15 F 4.0 24.0 1.0 -sub-CLNC0251 ses-M00 CN 1.0 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M03 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M06 CN 1.0 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M12 CN 1.0 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M18 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M24 CN 1.0 66.81 F 2.0 27.0 0.0 -sub-CLNC0251 ses-M48 4.0 66.81 F 2.0 27.0 0.0 -sub-CLNC0252 ses-M00 MCI 2.0 76.04 F 7.0 20.0 3.0 -sub-CLNC0253 ses-M00 CN 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M06 CN 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M12 MCI 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M18 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M24 CN 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M30 69.4 F 1.0 20.0 0.0 -sub-CLNC0253 ses-M36 69.4 F 1.0 20.0 0.0 -sub-CLNC0254 ses-M00 MCI 67.27 M 3.0 22.0 0.5 -sub-CLNC0254 ses-M06 MCI 67.27 M 3.0 22.0 0.5 -sub-CLNC0255 ses-M00 CN 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M06 CN 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M12 CN 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M18 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M24 CN 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M30 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M36 MCI 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M42 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M48 4.0 78.93 M 6.0 21.0 3.0 -sub-CLNC0255 ses-M54 78.93 M 6.0 21.0 3.0 -sub-CLNC0256 ses-M00 CN 1.0 71.46 M 8.0 27.0 0.0 -sub-CLNC0256 ses-M03 71.46 M 8.0 27.0 0.0 -sub-CLNC0256 ses-M06 CN 1.0 71.46 M 8.0 27.0 0.0 -sub-CLNC0256 ses-M12 CN 1.0 71.46 M 8.0 27.0 0.0 -sub-CLNC0257 ses-M00 CN 1.0 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M03 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M06 CN 1.0 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M12 CN 1.0 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M18 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M24 CN 1.0 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M30 67.0 M 4.0 28.0 0.0 -sub-CLNC0257 ses-M48 4.0 67.0 M 4.0 28.0 0.0 -sub-CLNC0258 ses-M00 MCI 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M06 CN 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M12 MCI 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M18 CN 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M24 CN 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M30 57.99 F 6.0 26.0 2.0 -sub-CLNC0258 ses-M36 CN 57.99 F 6.0 26.0 2.0 -sub-CLNC0259 ses-M00 AD 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M06 AD 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M12 AD 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M18 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M24 AD 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M30 67.0 M 7.0 23.0 0.0 -sub-CLNC0259 ses-M36 67.0 M 7.0 23.0 0.0 -sub-CLNC0260 ses-M00 CN 1.0 65.6 M 7.0 24.0 0.5 -sub-CLNC0260 ses-M03 65.6 M 7.0 24.0 0.5 -sub-CLNC0260 ses-M06 CN 1.0 65.6 M 7.0 24.0 0.5 -sub-CLNC0260 ses-M12 CN 1.0 65.6 M 7.0 24.0 0.5 -sub-CLNC0260 ses-M18 65.6 M 7.0 24.0 0.5 -sub-CLNC0260 ses-M24 CN 1.0 65.6 M 7.0 24.0 0.5 -sub-CLNC0261 ses-M00 MCI 2.0 89.03 F 3.0 29.0 1.0 -sub-CLNC0261 ses-M03 89.03 F 3.0 29.0 1.0 -sub-CLNC0261 ses-M06 MCI 2.0 89.03 F 3.0 29.0 1.0 -sub-CLNC0261 ses-M12 MCI 2.0 89.03 F 3.0 29.0 1.0 -sub-CLNC0261 ses-M18 89.03 F 3.0 29.0 1.0 -sub-CLNC0261 ses-M24 89.03 F 3.0 29.0 1.0 -sub-CLNC0262 ses-M00 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M06 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M108 CN 1.0 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M12 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M18 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M24 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M30 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M36 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M42 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M48 CN 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M54 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M60 CN 1.0 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M66 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M72 CN 1.0 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M78 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M84 CN 1.0 88.37 F 1.0 26.0 1.0 -sub-CLNC0262 ses-M90 88.37 F 1.0 26.0 1.0 -sub-CLNC0263 ses-M00 MCI 2.0 59.73 M 5.0 29.0 1.0 -sub-CLNC0263 ses-M03 59.73 M 5.0 29.0 1.0 -sub-CLNC0264 ses-M00 MCI 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M06 MCI 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M12 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M18 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M24 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M30 72.0 F 4.0 26.0 2.0 -sub-CLNC0264 ses-M36 72.0 F 4.0 26.0 2.0 -sub-CLNC0265 ses-M00 AD 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M06 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M12 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M18 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M24 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M30 74.24 F 3.0 24.0 0.5 -sub-CLNC0265 ses-M36 74.24 F 3.0 24.0 0.5 -sub-CLNC0266 ses-M00 MCI 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M06 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M12 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M18 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M24 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M30 81.49 F 3.0 27.0 0.5 -sub-CLNC0266 ses-M36 81.49 F 3.0 27.0 0.5 -sub-CLNC0267 ses-M00 CN 1.0 78.87 M 1.0 20.0 1.0 -sub-CLNC0267 ses-M03 78.87 M 1.0 20.0 1.0 -sub-CLNC0267 ses-M06 CN 1.0 78.87 M 1.0 20.0 1.0 -sub-CLNC0267 ses-M24 78.87 M 1.0 20.0 1.0 -sub-CLNC0268 ses-M00 MCI 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M06 MCI 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M12 MCI 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M18 MCI 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M24 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M30 72.35 M 1.0 23.0 1.0 -sub-CLNC0268 ses-M36 72.35 M 1.0 23.0 1.0 -sub-CLNC0269 ses-M00 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M06 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M12 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M18 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M24 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M30 83.58 F 9.0 25.0 0.5 -sub-CLNC0269 ses-M36 MCI 83.58 F 9.0 25.0 0.5 -sub-CLNC0270 ses-M00 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M06 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M12 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M18 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M24 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M30 67.1 F 3.0 26.0 0.0 -sub-CLNC0270 ses-M36 MCI 67.1 F 3.0 26.0 0.0 -sub-CLNC0271 ses-M00 AD 3.0 81.58 F 5.0 22.0 3.0 -sub-CLNC0271 ses-M03 81.58 F 5.0 22.0 3.0 -sub-CLNC0272 ses-M00 AD 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M06 AD 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M12 AD 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M18 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M24 AD 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M30 83.14 F 8.0 26.0 0.5 -sub-CLNC0272 ses-M36 83.14 F 8.0 26.0 0.5 -sub-CLNC0273 ses-M00 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M06 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M108 5.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M12 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M120 AD 3.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M18 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M24 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M30 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M36 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M42 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M48 CN 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M54 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M60 CN 1.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M66 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M72 CN 1.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M78 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M84 4.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M90 75.38 M 9.0 26.0 0.0 -sub-CLNC0273 ses-M96 5.0 75.38 M 9.0 26.0 0.0 -sub-CLNC0274 ses-M00 MCI 2.0 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M03 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M06 MCI 2.0 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M12 MCI 2.0 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M18 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M24 MCI 2.0 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M36 MCI 2.0 71.79 F 1.0 26.0 0.5 -sub-CLNC0274 ses-M48 71.79 F 1.0 26.0 0.5 -sub-CLNC0275 ses-M00 CN 1.0 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M03 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M06 CN 1.0 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M12 CN 1.0 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M18 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M24 MCI 2.0 74.76 M 4.0 28.0 0.5 -sub-CLNC0275 ses-M30 74.76 M 4.0 28.0 0.5 -sub-CLNC0276 ses-M00 CN 1.0 80.31 F 5.0 28.0 3.0 -sub-CLNC0276 ses-M06 CN 1.0 80.31 F 5.0 28.0 3.0 -sub-CLNC0277 ses-M00 AD 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M06 AD 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M12 AD 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M18 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M24 AD 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M30 73.74 M 7.0 27.0 0.0 -sub-CLNC0277 ses-M36 73.74 M 7.0 27.0 0.0 -sub-CLNC0278 ses-M00 MCI 2.0 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M03 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M06 MCI 2.0 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M12 MCI 2.0 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M18 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M24 5.0 77.46 F 8.0 25.0 0.0 -sub-CLNC0278 ses-M36 AD 3.0 77.46 F 8.0 25.0 0.0 -sub-CLNC0279 ses-M00 CN 1.0 73.19 F 5.0 25.0 2.0 -sub-CLNC0279 ses-M03 73.19 F 5.0 25.0 2.0 -sub-CLNC0279 ses-M06 CN 1.0 73.19 F 5.0 25.0 2.0 -sub-CLNC0279 ses-M12 CN 1.0 73.19 F 5.0 25.0 2.0 -sub-CLNC0279 ses-M18 73.19 F 5.0 25.0 2.0 -sub-CLNC0279 ses-M24 CN 1.0 73.19 F 5.0 25.0 2.0 -sub-CLNC0280 ses-M00 CN 1.0 71.26 M 3.0 27.0 3.0 -sub-CLNC0280 ses-M24 71.26 M 3.0 27.0 3.0 -sub-CLNC0281 ses-M00 MCI 2.0 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M03 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M06 MCI 2.0 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M12 MCI 2.0 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M18 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M24 MCI 2.0 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M36 MCI 2.0 72.85 F 2.0 28.0 0.5 -sub-CLNC0281 ses-M48 72.85 F 2.0 28.0 0.5 -sub-CLNC0282 ses-M00 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M03 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M06 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M12 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M18 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M24 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M30 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M36 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0282 ses-M48 MCI 2.0 86.78 F 2.0 22.0 1.0 -sub-CLNC0283 ses-M00 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M03 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M06 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M12 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M18 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M24 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M36 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0283 ses-M48 MCI 2.0 82.09 F 3.0 29.0 0.0 -sub-CLNC0284 ses-M00 AD 3.0 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M03 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M06 AD 3.0 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M12 AD 3.0 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M18 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M24 AD 3.0 63.98 F 1.0 23.0 0.5 -sub-CLNC0284 ses-M30 63.98 F 1.0 23.0 0.5 -sub-CLNC0285 ses-M00 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M03 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M06 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M12 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M18 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M24 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M30 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M36 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M48 MCI 2.0 84.37 M 7.0 24.0 3.0 -sub-CLNC0285 ses-M60 84.37 M 7.0 24.0 3.0 -sub-CLNC0286 ses-M00 AD 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M06 AD 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M12 AD 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M18 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M24 AD 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M30 71.34 M 8.0 27.0 3.0 -sub-CLNC0286 ses-M36 71.34 M 8.0 27.0 3.0 -sub-CLNC0287 ses-M00 MCI 2.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M03 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M06 MCI 2.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M12 MCI 2.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M18 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M24 MCI 2.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M30 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M36 MCI 2.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M48 7.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0287 ses-M60 CN 1.0 76.08 F 8.0 28.0 0.0 -sub-CLNC0288 ses-M00 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M03 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M06 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M12 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M18 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M24 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M30 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M36 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0288 ses-M48 MCI 2.0 73.25 F 8.0 21.0 1.0 -sub-CLNC0289 ses-M00 MCI 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M06 MCI 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M12 MCI 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M18 MCI 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M24 MCI 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M30 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M36 AD 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M42 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M48 AD 3.0 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M54 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M60 AD 3.0 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M66 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M72 AD 3.0 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M78 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M84 73.24 F 2.0 27.0 0.0 -sub-CLNC0289 ses-M96 73.24 F 2.0 27.0 0.0 -sub-CLNC0290 ses-M00 MCI 2.0 75.93 M 4.0 28.0 2.0 -sub-CLNC0290 ses-M03 75.93 M 4.0 28.0 2.0 -sub-CLNC0290 ses-M06 MCI 2.0 75.93 M 4.0 28.0 2.0 -sub-CLNC0290 ses-M12 MCI 2.0 75.93 M 4.0 28.0 2.0 -sub-CLNC0290 ses-M48 MCI 2.0 75.93 M 4.0 28.0 2.0 -sub-CLNC0291 ses-M00 AD 3.0 80.14 F 5.0 21.0 0.5 -sub-CLNC0291 ses-M03 80.14 F 5.0 21.0 0.5 -sub-CLNC0292 ses-M00 AD 3.0 70.81 F 7.0 26.0 0.0 -sub-CLNC0292 ses-M03 70.81 F 7.0 26.0 0.0 -sub-CLNC0292 ses-M06 AD 3.0 70.81 F 7.0 26.0 0.0 -sub-CLNC0292 ses-M12 AD 3.0 70.81 F 7.0 26.0 0.0 -sub-CLNC0292 ses-M18 70.81 F 7.0 26.0 0.0 -sub-CLNC0293 ses-M00 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M03 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M06 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M12 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M18 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M24 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M36 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0293 ses-M48 MCI 2.0 67.82 M 1.0 27.0 3.0 -sub-CLNC0294 ses-M00 MCI 2.0 80.66 F 6.0 22.0 3.0 -sub-CLNC0294 ses-M03 80.66 F 6.0 22.0 3.0 -sub-CLNC0294 ses-M06 MCI 2.0 80.66 F 6.0 22.0 3.0 -sub-CLNC0294 ses-M12 5.0 80.66 F 6.0 22.0 3.0 -sub-CLNC0294 ses-M18 80.66 F 6.0 22.0 3.0 -sub-CLNC0294 ses-M24 5.0 80.66 F 6.0 22.0 3.0 -sub-CLNC0295 ses-M00 MCI 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M06 MCI 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M12 MCI 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M18 MCI 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M24 AD 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M30 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M36 AD 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M42 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M48 AD 3.0 88.34 M 3.0 29.0 2.0 -sub-CLNC0295 ses-M54 88.34 M 3.0 29.0 2.0 -sub-CLNC0296 ses-M00 AD 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M06 AD 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M12 AD 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M18 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M24 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M30 75.07 M 5.0 29.0 1.0 -sub-CLNC0296 ses-M36 75.07 M 5.0 29.0 1.0 -sub-CLNC0297 ses-M00 MCI 2.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M03 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M06 MCI 2.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M12 5.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M18 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M24 MCI 2.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M30 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M36 MCI 2.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M48 AD 3.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0297 ses-M60 AD 3.0 70.81 F 3.0 28.0 1.0 -sub-CLNC0298 ses-M00 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M06 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M108 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M12 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M120 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M18 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M24 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M30 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M36 MCI 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M42 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M48 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M54 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M72 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M78 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M84 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M90 67.19 F 3.0 26.0 3.0 -sub-CLNC0298 ses-M96 MCI 2.0 67.19 F 3.0 26.0 3.0 -sub-CLNC0299 ses-M00 CN 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M06 CN 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M12 CN 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M18 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M24 CN 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M30 74.46 M 7.0 22.0 0.0 -sub-CLNC0299 ses-M36 CN 74.46 M 7.0 22.0 0.0 -sub-CLNC0300 ses-M00 MCI 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M06 MCI 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M12 AD 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M18 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M24 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M30 68.03 F 6.0 21.0 2.0 -sub-CLNC0300 ses-M36 68.03 F 6.0 21.0 2.0 -sub-CLNC0301 ses-M00 AD 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M06 AD 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M12 AD 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M18 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M24 AD 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M30 62.01 M 7.0 27.0 1.0 -sub-CLNC0301 ses-M36 62.01 M 7.0 27.0 1.0 -sub-CLNC0302 ses-M00 MCI 2.0 78.51 M 7.0 21.0 3.0 -sub-CLNC0302 ses-M03 78.51 M 7.0 21.0 3.0 -sub-CLNC0302 ses-M06 5.0 78.51 M 7.0 21.0 3.0 -sub-CLNC0302 ses-M12 AD 3.0 78.51 M 7.0 21.0 3.0 -sub-CLNC0303 ses-M00 MCI 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M06 MCI 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M12 MCI 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M18 MCI 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M24 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M30 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M36 MCI 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M42 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M48 MCI 2.0 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M54 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M60 MCI 2.0 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M66 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M72 MCI 2.0 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M78 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M84 MCI 2.0 78.66 M 9.0 20.0 3.0 -sub-CLNC0303 ses-M96 MCI 2.0 78.66 M 9.0 20.0 3.0 -sub-CLNC0304 ses-M00 AD 3.0 67.48 M 1.0 29.0 2.0 -sub-CLNC0304 ses-M03 67.48 M 1.0 29.0 2.0 -sub-CLNC0304 ses-M12 AD 3.0 67.48 M 1.0 29.0 2.0 -sub-CLNC0304 ses-M18 67.48 M 1.0 29.0 2.0 -sub-CLNC0305 ses-M00 MCI 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M06 MCI 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M12 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M18 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M24 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M30 68.38 M 4.0 22.0 1.0 -sub-CLNC0305 ses-M36 68.38 M 4.0 22.0 1.0 -sub-CLNC0306 ses-M00 MCI 2.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M03 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M06 MCI 2.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M12 MCI 2.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M18 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M24 7.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M36 CN 1.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0306 ses-M48 CN 1.0 71.73 F 9.0 29.0 0.5 -sub-CLNC0307 ses-M00 CN 1.0 75.4 M 4.0 26.0 0.5 -sub-CLNC0307 ses-M06 CN 1.0 75.4 M 4.0 26.0 0.5 -sub-CLNC0307 ses-M12 CN 1.0 75.4 M 4.0 26.0 0.5 -sub-CLNC0307 ses-M18 75.4 M 4.0 26.0 0.5 -sub-CLNC0307 ses-M24 CN 1.0 75.4 M 4.0 26.0 0.5 -sub-CLNC0307 ses-M36 75.4 M 4.0 26.0 0.5 -sub-CLNC0308 ses-M00 CN 1.0 76.19 M 2.0 24.0 0.5 -sub-CLNC0308 ses-M03 76.19 M 2.0 24.0 0.5 -sub-CLNC0308 ses-M06 CN 1.0 76.19 M 2.0 24.0 0.5 -sub-CLNC0308 ses-M12 CN 1.0 76.19 M 2.0 24.0 0.5 -sub-CLNC0308 ses-M24 CN 1.0 76.19 M 2.0 24.0 0.5 -sub-CLNC0308 ses-M48 CN 1.0 76.19 M 2.0 24.0 0.5 -sub-CLNC0309 ses-M00 MCI 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M06 CN 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M108 CN 1.0 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M12 CN 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M120 CN 1.0 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M18 CN 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M24 CN 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M30 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M36 CN 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M72 CN 1.0 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M78 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M84 CN 1.0 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M90 68.9 M 1.0 27.0 3.0 -sub-CLNC0309 ses-M96 CN 1.0 68.9 M 1.0 27.0 3.0 -sub-CLNC0310 ses-M00 MCI 2.0 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M03 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M06 MCI 2.0 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M12 MCI 2.0 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M18 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M24 MCI 2.0 81.47 F 4.0 26.0 0.0 -sub-CLNC0310 ses-M30 81.47 F 4.0 26.0 0.0 -sub-CLNC0311 ses-M00 MCI 2.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M03 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M06 MCI 2.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M12 MCI 2.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M18 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M24 MCI 2.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M30 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M36 7.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M48 CN 1.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0311 ses-M60 CN 1.0 73.3 F 5.0 22.0 2.0 -sub-CLNC0312 ses-M00 MCI 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M06 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M12 MCI 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M18 MCI 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M24 MCI 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M30 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M36 AD 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M60 AD 3.0 70.96 M 9.0 28.0 1.0 -sub-CLNC0312 ses-M66 70.96 M 9.0 28.0 1.0 -sub-CLNC0313 ses-M00 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M06 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M108 MCI 2.0 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M12 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M18 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M24 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M30 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M36 MCI 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M48 MCI 2.0 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M54 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M60 MCI 2.0 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M72 MCI 2.0 79.03 M 6.0 23.0 1.0 -sub-CLNC0313 ses-M84 MCI 2.0 79.03 M 6.0 23.0 1.0 -sub-CLNC0314 ses-M00 CN 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M06 CN 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M108 AD 3.0 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M12 CN 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M18 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M24 CN 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M30 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M36 AD 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M72 AD 3.0 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M78 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M84 AD 3.0 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M90 60.11 F 3.0 24.0 1.0 -sub-CLNC0314 ses-M96 AD 3.0 60.11 F 3.0 24.0 1.0 -sub-CLNC0315 ses-M00 AD 3.0 79.68 F 1.0 26.0 3.0 -sub-CLNC0315 ses-M03 79.68 F 1.0 26.0 3.0 -sub-CLNC0315 ses-M06 AD 3.0 79.68 F 1.0 26.0 3.0 -sub-CLNC0315 ses-M12 AD 3.0 79.68 F 1.0 26.0 3.0 -sub-CLNC0315 ses-M18 79.68 F 1.0 26.0 3.0 -sub-CLNC0316 ses-M00 CN 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M06 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M12 CN 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M18 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M24 CN 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M30 63.01 F 1.0 21.0 1.0 -sub-CLNC0316 ses-M36 CN 63.01 F 1.0 21.0 1.0 -sub-CLNC0317 ses-M00 AD 82.51 M 2.0 24.0 0.5 -sub-CLNC0317 ses-M06 AD 82.51 M 2.0 24.0 0.5 -sub-CLNC0317 ses-M12 AD 82.51 M 2.0 24.0 0.5 -sub-CLNC0317 ses-M18 82.51 M 2.0 24.0 0.5 -sub-CLNC0317 ses-M24 AD 82.51 M 2.0 24.0 0.5 -sub-CLNC0318 ses-M00 AD 3.0 71.0 F 1.0 25.0 3.0 -sub-CLNC0318 ses-M06 AD 3.0 71.0 F 1.0 25.0 3.0 -sub-CLNC0318 ses-M12 71.0 F 1.0 25.0 3.0 -sub-CLNC0319 ses-M00 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M03 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M06 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M12 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M18 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M24 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M30 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M48 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0319 ses-M60 MCI 2.0 57.69 F 2.0 21.0 0.0 -sub-CLNC0320 ses-M00 AD 66.24 M 6.0 24.0 0.5 -sub-CLNC0320 ses-M06 AD 66.24 M 6.0 24.0 0.5 -sub-CLNC0320 ses-M12 AD 66.24 M 6.0 24.0 0.5 -sub-CLNC0320 ses-M18 66.24 M 6.0 24.0 0.5 -sub-CLNC0321 ses-M00 MCI 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M06 MCI 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M12 CN 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M18 CN 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M24 CN 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M30 73.04 F 2.0 28.0 2.0 -sub-CLNC0321 ses-M36 CN 73.04 F 2.0 28.0 2.0 -sub-CLNC0322 ses-M00 AD 3.0 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M03 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M06 AD 3.0 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M12 AD 3.0 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M18 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M24 AD 3.0 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M30 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M36 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M42 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M48 71.38 F 8.0 24.0 3.0 -sub-CLNC0322 ses-M60 71.38 F 8.0 24.0 3.0 -sub-CLNC0323 ses-M00 CN 1.0 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M03 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M06 CN 1.0 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M12 CN 1.0 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M18 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M24 CN 1.0 67.75 F 6.0 24.0 3.0 -sub-CLNC0323 ses-M48 CN 1.0 67.75 F 6.0 24.0 3.0 -sub-CLNC0324 ses-M00 AD 3.0 80.16 F 1.0 21.0 3.0 -sub-CLNC0324 ses-M03 80.16 F 1.0 21.0 3.0 -sub-CLNC0324 ses-M06 AD 3.0 80.16 F 1.0 21.0 3.0 -sub-CLNC0324 ses-M12 AD 3.0 80.16 F 1.0 21.0 3.0 -sub-CLNC0325 ses-M00 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M03 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M06 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M12 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M18 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M24 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M36 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0325 ses-M48 MCI 2.0 73.05 F 9.0 28.0 0.5 -sub-CLNC0326 ses-M00 CN 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M06 CN 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M12 CN 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M18 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M24 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M30 73.61 M 1.0 27.0 3.0 -sub-CLNC0326 ses-M36 CN 73.61 M 1.0 27.0 3.0 -sub-CLNC0327 ses-M00 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M03 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M06 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M12 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M18 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M24 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M36 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0327 ses-M48 MCI 2.0 81.12 F 9.0 24.0 0.0 -sub-CLNC0328 ses-M00 MCI 2.0 73.27 M 2.0 24.0 3.0 -sub-CLNC0328 ses-M03 73.27 M 2.0 24.0 3.0 -sub-CLNC0328 ses-M06 MCI 2.0 73.27 M 2.0 24.0 3.0 -sub-CLNC0328 ses-M12 MCI 2.0 73.27 M 2.0 24.0 3.0 -sub-CLNC0328 ses-M24 5.0 73.27 M 2.0 24.0 3.0 -sub-CLNC0328 ses-M36 AD 3.0 73.27 M 2.0 24.0 3.0 -sub-CLNC0329 ses-M00 MCI 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M06 MCI 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M12 MCI 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M18 AD 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M24 AD 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M30 73.03 F 1.0 25.0 1.0 -sub-CLNC0329 ses-M36 AD 73.03 F 1.0 25.0 1.0 -sub-CLNC0330 ses-M00 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M06 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M108 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M12 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M120 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M18 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M24 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M30 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M36 MCI 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M42 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M48 5.0 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M54 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M72 MCI 2.0 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M78 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M84 5.0 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M90 75.04 F 8.0 29.0 2.0 -sub-CLNC0330 ses-M96 75.04 F 8.0 29.0 2.0 -sub-CLNC0331 ses-M00 AD 3.0 79.39 M 3.0 25.0 0.0 -sub-CLNC0331 ses-M03 79.39 M 3.0 25.0 0.0 -sub-CLNC0331 ses-M06 AD 3.0 79.39 M 3.0 25.0 0.0 -sub-CLNC0331 ses-M12 AD 3.0 79.39 M 3.0 25.0 0.0 -sub-CLNC0331 ses-M18 79.39 M 3.0 25.0 0.0 -sub-CLNC0332 ses-M00 MCI 2.0 90.53 F 7.0 29.0 3.0 -sub-CLNC0333 ses-M00 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M03 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M06 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M12 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M18 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M24 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M36 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0333 ses-M48 MCI 2.0 72.04 M 6.0 25.0 0.0 -sub-CLNC0334 ses-M00 AD 3.0 72.61 M 1.0 22.0 0.5 -sub-CLNC0334 ses-M03 72.61 M 1.0 22.0 0.5 -sub-CLNC0334 ses-M06 AD 3.0 72.61 M 1.0 22.0 0.5 -sub-CLNC0334 ses-M12 AD 3.0 72.61 M 1.0 22.0 0.5 -sub-CLNC0335 ses-M00 MCI 2.0 69.97 F 9.0 23.0 2.0 -sub-CLNC0335 ses-M03 69.97 F 9.0 23.0 2.0 -sub-CLNC0335 ses-M12 MCI 2.0 69.97 F 9.0 23.0 2.0 -sub-CLNC0336 ses-M00 AD 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M06 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M12 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M18 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M24 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M30 78.02 F 6.0 25.0 2.0 -sub-CLNC0336 ses-M36 78.02 F 6.0 25.0 2.0 -sub-CLNC0337 ses-M00 MCI 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M06 MCI 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M12 MCI 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M18 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M24 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M30 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M36 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M72 MCI 2.0 72.25 F 3.0 24.0 1.0 -sub-CLNC0337 ses-M78 72.25 F 3.0 24.0 1.0 -sub-CLNC0338 ses-M00 AD 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M06 AD 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M12 AD 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M18 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M24 AD 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M30 76.75 F 6.0 21.0 3.0 -sub-CLNC0338 ses-M36 76.75 F 6.0 21.0 3.0 -sub-CLNC0339 ses-M00 MCI 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M06 MCI 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M12 MCI 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M18 AD 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M24 AD 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M30 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M36 AD 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M48 AD 84.13 F 7.0 21.0 0.0 -sub-CLNC0339 ses-M60 AD 3.0 84.13 F 7.0 21.0 0.0 -sub-CLNC0340 ses-M00 MCI 2.0 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M03 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M06 MCI 2.0 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M12 MCI 2.0 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M18 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M24 MCI 2.0 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M30 81.99 M 9.0 21.0 3.0 -sub-CLNC0340 ses-M36 MCI 2.0 81.99 M 9.0 21.0 3.0 -sub-CLNC0341 ses-M00 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M03 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M06 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M12 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M18 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M24 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M30 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M36 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0341 ses-M48 MCI 2.0 78.74 M 1.0 29.0 2.0 -sub-CLNC0342 ses-M00 MCI 2.0 67.52 M 4.0 26.0 0.5 -sub-CLNC0342 ses-M06 MCI 2.0 67.52 M 4.0 26.0 0.5 -sub-CLNC0342 ses-M12 MCI 2.0 67.52 M 4.0 26.0 0.5 -sub-CLNC0342 ses-M18 67.52 M 4.0 26.0 0.5 -sub-CLNC0342 ses-M24 MCI 2.0 67.52 M 4.0 26.0 0.5 -sub-CLNC0343 ses-M00 CN 1.0 62.68 M 9.0 25.0 0.0 -sub-CLNC0343 ses-M03 62.68 M 9.0 25.0 0.0 -sub-CLNC0343 ses-M06 4.0 62.68 M 9.0 25.0 0.0 -sub-CLNC0343 ses-M12 MCI 2.0 62.68 M 9.0 25.0 0.0 -sub-CLNC0343 ses-M24 5.0 62.68 M 9.0 25.0 0.0 -sub-CLNC0343 ses-M36 AD 3.0 62.68 M 9.0 25.0 0.0 -sub-CLNC0344 ses-M00 CN 1.0 84.52 F 6.0 26.0 3.0 -sub-CLNC0344 ses-M03 84.52 F 6.0 26.0 3.0 -sub-CLNC0344 ses-M06 CN 1.0 84.52 F 6.0 26.0 3.0 -sub-CLNC0344 ses-M12 CN 1.0 84.52 F 6.0 26.0 3.0 -sub-CLNC0345 ses-M00 MCI 2.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M03 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M06 5.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M12 AD 3.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M18 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M24 AD 3.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M36 AD 3.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0345 ses-M48 AD 3.0 83.06 F 6.0 29.0 0.5 -sub-CLNC0346 ses-M00 MCI 2.0 66.32 F 1.0 21.0 3.0 -sub-CLNC0346 ses-M03 66.32 F 1.0 21.0 3.0 -sub-CLNC0346 ses-M06 MCI 2.0 66.32 F 1.0 21.0 3.0 -sub-CLNC0346 ses-M12 5.0 66.32 F 1.0 21.0 3.0 -sub-CLNC0347 ses-M00 MCI 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M06 MCI 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M12 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M18 MCI 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M24 MCI 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M30 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M36 MCI 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M42 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M48 MCI 2.0 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M54 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M60 MCI 2.0 63.27 F 7.0 24.0 2.0 -sub-CLNC0347 ses-M72 MCI 2.0 63.27 F 7.0 24.0 2.0 -sub-CLNC0348 ses-M00 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M03 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M06 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M12 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M18 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M24 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M30 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M36 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0348 ses-M48 MCI 2.0 72.17 F 3.0 27.0 2.0 -sub-CLNC0349 ses-M00 MCI 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M06 MCI 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M12 AD 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M18 AD 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M24 AD 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M30 73.64 M 2.0 22.0 0.5 -sub-CLNC0349 ses-M36 73.64 M 2.0 22.0 0.5 -sub-CLNC0350 ses-M00 CN 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M06 CN 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M12 CN 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M18 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M24 CN 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M30 78.62 M 9.0 20.0 2.0 -sub-CLNC0350 ses-M36 CN 78.62 M 9.0 20.0 2.0 -sub-CLNC0351 ses-M00 AD 3.0 75.35 M 7.0 25.0 0.5 -sub-CLNC0351 ses-M03 75.35 M 7.0 25.0 0.5 -sub-CLNC0351 ses-M06 AD 3.0 75.35 M 7.0 25.0 0.5 -sub-CLNC0351 ses-M12 75.35 M 7.0 25.0 0.5 -sub-CLNC0352 ses-M00 CN 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M06 CN 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M12 CN 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M18 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M24 CN 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M30 78.19 F 3.0 25.0 0.0 -sub-CLNC0352 ses-M36 CN 78.19 F 3.0 25.0 0.0 -sub-CLNC0353 ses-M00 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M03 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M06 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M12 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M18 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M24 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M30 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M36 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M48 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0353 ses-M60 MCI 2.0 79.21 M 4.0 22.0 1.0 -sub-CLNC0354 ses-M00 MCI 2.0 70.18 F 9.0 25.0 0.5 -sub-CLNC0354 ses-M06 MCI 2.0 70.18 F 9.0 25.0 0.5 -sub-CLNC0354 ses-M12 MCI 2.0 70.18 F 9.0 25.0 0.5 -sub-CLNC0354 ses-M18 70.18 F 9.0 25.0 0.5 -sub-CLNC0354 ses-M24 MCI 2.0 70.18 F 9.0 25.0 0.5 -sub-CLNC0354 ses-M30 70.18 F 9.0 25.0 0.5 -sub-CLNC0355 ses-M00 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M06 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M12 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M18 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M24 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M30 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M36 MCI 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M42 84.82 F 8.0 21.0 0.0 -sub-CLNC0355 ses-M48 AD 84.82 F 8.0 21.0 0.0 -sub-CLNC0356 ses-M00 CN 1.0 70.82 M 6.0 20.0 3.0 -sub-CLNC0356 ses-M03 70.82 M 6.0 20.0 3.0 -sub-CLNC0356 ses-M06 CN 1.0 70.82 M 6.0 20.0 3.0 -sub-CLNC0356 ses-M24 CN 1.0 70.82 M 6.0 20.0 3.0 -sub-CLNC0357 ses-M00 CN 1.0 82.56 M 2.0 24.0 1.0 -sub-CLNC0357 ses-M06 CN 1.0 82.56 M 2.0 24.0 1.0 -sub-CLNC0358 ses-M00 MCI 2.0 88.51 F 5.0 20.0 0.0 -sub-CLNC0358 ses-M03 88.51 F 5.0 20.0 0.0 -sub-CLNC0358 ses-M06 MCI 2.0 88.51 F 5.0 20.0 0.0 -sub-CLNC0358 ses-M12 MCI 2.0 88.51 F 5.0 20.0 0.0 -sub-CLNC0358 ses-M18 88.51 F 5.0 20.0 0.0 -sub-CLNC0359 ses-M00 MCI 2.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M03 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M06 MCI 2.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M12 7.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M18 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M24 CN 1.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M36 MCI 2.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0359 ses-M48 MCI 2.0 73.37 M 2.0 26.0 1.0 -sub-CLNC0360 ses-M00 MCI 2.0 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M03 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M06 MCI 2.0 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M12 MCI 2.0 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M24 MCI 2.0 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M36 MCI 2.0 67.08 M 5.0 23.0 0.5 -sub-CLNC0360 ses-M48 67.08 M 5.0 23.0 0.5 -sub-CLNC0361 ses-M00 AD 3.0 60.69 F 9.0 22.0 0.0 -sub-CLNC0361 ses-M03 60.69 F 9.0 22.0 0.0 -sub-CLNC0361 ses-M06 AD 3.0 60.69 F 9.0 22.0 0.0 -sub-CLNC0361 ses-M12 AD 3.0 60.69 F 9.0 22.0 0.0 -sub-CLNC0361 ses-M18 60.69 F 9.0 22.0 0.0 -sub-CLNC0362 ses-M00 AD 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M06 AD 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M12 AD 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M18 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M24 AD 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M30 72.56 M 1.0 24.0 2.0 -sub-CLNC0362 ses-M36 72.56 M 1.0 24.0 2.0 -sub-CLNC0363 ses-M00 MCI 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M06 MCI 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M12 MCI 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M18 MCI 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M24 MCI 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M30 78.58 F 3.0 21.0 0.5 -sub-CLNC0363 ses-M36 AD 78.58 F 3.0 21.0 0.5 -sub-CLNC0364 ses-M00 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M06 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M12 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M18 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M24 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M30 72.27 F 9.0 28.0 0.0 -sub-CLNC0364 ses-M36 AD 72.27 F 9.0 28.0 0.0 -sub-CLNC0365 ses-M00 CN 1.0 76.96 M 6.0 21.0 1.0 -sub-CLNC0365 ses-M03 76.96 M 6.0 21.0 1.0 -sub-CLNC0365 ses-M06 CN 1.0 76.96 M 6.0 21.0 1.0 -sub-CLNC0365 ses-M24 CN 1.0 76.96 M 6.0 21.0 1.0 -sub-CLNC0366 ses-M00 CN 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M06 CN 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M12 CN 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M18 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M24 CN 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M30 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M36 CN 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M42 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M48 CN 1.0 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M54 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M60 CN 1.0 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M66 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M72 CN 1.0 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M84 CN 1.0 78.89 M 5.0 24.0 3.0 -sub-CLNC0366 ses-M96 CN 1.0 78.89 M 5.0 24.0 3.0 -sub-CLNC0367 ses-M00 MCI 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M06 MCI 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M12 MCI 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M18 MCI 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M24 MCI 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M30 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M36 MCI 2.0 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M42 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M48 MCI 2.0 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M54 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M60 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M66 71.47 F 1.0 23.0 0.5 -sub-CLNC0367 ses-M72 71.47 F 1.0 23.0 0.5 -sub-CLNC0368 ses-M00 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M03 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M06 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M12 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M24 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M30 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M36 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0368 ses-M48 MCI 2.0 74.8 F 2.0 29.0 0.5 -sub-CLNC0369 ses-M00 AD 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M06 AD 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M12 AD 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M18 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M24 AD 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M30 65.54 F 7.0 24.0 2.0 -sub-CLNC0369 ses-M36 65.54 F 7.0 24.0 2.0 -sub-CLNC0370 ses-M00 CN 1.0 72.81 F 2.0 23.0 1.0 -sub-CLNC0370 ses-M06 CN 1.0 72.81 F 2.0 23.0 1.0 -sub-CLNC0370 ses-M24 CN 1.0 72.81 F 2.0 23.0 1.0 -sub-CLNC0371 ses-M00 CN 1.0 70.55 M 7.0 29.0 2.0 -sub-CLNC0371 ses-M03 70.55 M 7.0 29.0 2.0 -sub-CLNC0371 ses-M06 CN 1.0 70.55 M 7.0 29.0 2.0 -sub-CLNC0371 ses-M24 CN 1.0 70.55 M 7.0 29.0 2.0 -sub-CLNC0372 ses-M00 AD 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M06 AD 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M12 AD 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M18 AD 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M24 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M30 67.61 F 5.0 24.0 3.0 -sub-CLNC0372 ses-M36 67.61 F 5.0 24.0 3.0 -sub-CLNC0373 ses-M00 MCI 2.0 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M03 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M06 MCI 2.0 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M12 MCI 2.0 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M18 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M24 MCI 2.0 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M30 75.57 F 9.0 26.0 3.0 -sub-CLNC0373 ses-M48 75.57 F 9.0 26.0 3.0 -sub-CLNC0374 ses-M00 MCI 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M06 MCI 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M12 MCI 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M18 MCI 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M24 AD 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M30 88.18 M 9.0 23.0 2.0 -sub-CLNC0374 ses-M36 AD 88.18 M 9.0 23.0 2.0 -sub-CLNC0375 ses-M00 CN 1.0 76.0 F 7.0 24.0 1.0 -sub-CLNC0375 ses-M06 4.0 76.0 F 7.0 24.0 1.0 -sub-CLNC0375 ses-M12 MCI 2.0 76.0 F 7.0 24.0 1.0 -sub-CLNC0375 ses-M24 76.0 F 7.0 24.0 1.0 -sub-CLNC0376 ses-M00 MCI 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M06 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M12 MCI 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M18 AD 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M24 AD 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M30 83.18 M 6.0 20.0 0.5 -sub-CLNC0376 ses-M36 83.18 M 6.0 20.0 0.5 -sub-CLNC0377 ses-M00 CN 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M06 CN 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M12 CN 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M18 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M24 CN 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M30 69.72 F 3.0 28.0 3.0 -sub-CLNC0377 ses-M36 CN 69.72 F 3.0 28.0 3.0 -sub-CLNC0378 ses-M00 CN 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M06 CN 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M12 CN 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M18 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M24 CN 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M30 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M36 CN 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M48 CN 1.0 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M54 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M60 CN 1.0 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M72 CN 1.0 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M78 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M84 CN 1.0 78.47 F 4.0 26.0 1.0 -sub-CLNC0378 ses-M96 MCI 2.0 78.47 F 4.0 26.0 1.0 -sub-CLNC0379 ses-M00 MCI 2.0 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M03 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M06 MCI 2.0 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M12 MCI 2.0 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M18 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M24 MCI 2.0 80.57 M 7.0 29.0 3.0 -sub-CLNC0379 ses-M36 MCI 2.0 80.57 M 7.0 29.0 3.0 -sub-CLNC0380 ses-M00 MCI 2.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M03 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M06 MCI 2.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M12 MCI 2.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M18 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M24 MCI 2.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M30 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M36 MCI 2.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0380 ses-M48 5.0 70.29 F 9.0 27.0 2.0 -sub-CLNC0381 ses-M00 CN 1.0 72.87 F 3.0 25.0 0.5 -sub-CLNC0381 ses-M06 CN 1.0 72.87 F 3.0 25.0 0.5 -sub-CLNC0381 ses-M24 CN 1.0 72.87 F 3.0 25.0 0.5 -sub-CLNC0382 ses-M00 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M06 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M108 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M12 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M18 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M24 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M30 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M36 MCI 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M42 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M48 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M54 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M60 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M66 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M72 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M78 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M84 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0382 ses-M96 MCI 2.0 79.94 M 3.0 21.0 3.0 -sub-CLNC0383 ses-M00 MCI 2.0 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M03 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M06 MCI 2.0 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M12 MCI 2.0 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M18 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M24 MCI 2.0 89.63 F 1.0 25.0 3.0 -sub-CLNC0383 ses-M36 89.63 F 1.0 25.0 3.0 -sub-CLNC0384 ses-M00 MCI 2.0 76.8 F 1.0 25.0 0.0 -sub-CLNC0384 ses-M03 76.8 F 1.0 25.0 0.0 -sub-CLNC0384 ses-M06 MCI 2.0 76.8 F 1.0 25.0 0.0 -sub-CLNC0384 ses-M12 MCI 2.0 76.8 F 1.0 25.0 0.0 -sub-CLNC0384 ses-M18 76.8 F 1.0 25.0 0.0 -sub-CLNC0385 ses-M00 AD 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M06 AD 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M12 AD 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M18 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M24 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M30 60.63 M 1.0 26.0 0.0 -sub-CLNC0385 ses-M36 60.63 M 1.0 26.0 0.0 -sub-CLNC0386 ses-M00 MCI 2.0 74.83 M 6.0 26.0 0.5 -sub-CLNC0386 ses-M03 74.83 M 6.0 26.0 0.5 -sub-CLNC0386 ses-M06 MCI 2.0 74.83 M 6.0 26.0 0.5 -sub-CLNC0386 ses-M12 MCI 2.0 74.83 M 6.0 26.0 0.5 -sub-CLNC0386 ses-M18 74.83 M 6.0 26.0 0.5 -sub-CLNC0386 ses-M24 MCI 2.0 74.83 M 6.0 26.0 0.5 -sub-CLNC0387 ses-M00 CN 1.0 83.18 F 9.0 24.0 3.0 -sub-CLNC0387 ses-M03 83.18 F 9.0 24.0 3.0 -sub-CLNC0387 ses-M06 CN 1.0 83.18 F 9.0 24.0 3.0 -sub-CLNC0387 ses-M24 CN 1.0 83.18 F 9.0 24.0 3.0 -sub-CLNC0388 ses-M00 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M06 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M12 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M18 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M24 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M30 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M36 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M42 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M48 CN 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M54 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M60 CN 1.0 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M66 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M72 CN 1.0 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M78 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M90 82.91 F 5.0 24.0 2.0 -sub-CLNC0388 ses-M96 CN 1.0 82.91 F 5.0 24.0 2.0 -sub-CLNC0389 ses-M00 CN 1.0 75.38 F 8.0 27.0 0.5 -sub-CLNC0389 ses-M03 75.38 F 8.0 27.0 0.5 -sub-CLNC0389 ses-M06 4.0 75.38 F 8.0 27.0 0.5 -sub-CLNC0389 ses-M12 MCI 2.0 75.38 F 8.0 27.0 0.5 -sub-CLNC0389 ses-M18 75.38 F 8.0 27.0 0.5 -sub-CLNC0389 ses-M24 MCI 2.0 75.38 F 8.0 27.0 0.5 -sub-CLNC0390 ses-M00 AD 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M06 AD 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M12 AD 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M18 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M24 AD 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M30 73.0 M 7.0 29.0 0.5 -sub-CLNC0390 ses-M36 AD 73.0 M 7.0 29.0 0.5 -sub-CLNC0391 ses-M00 MCI 2.0 74.44 M 7.0 20.0 1.0 -sub-CLNC0391 ses-M03 74.44 M 7.0 20.0 1.0 -sub-CLNC0391 ses-M06 MCI 2.0 74.44 M 7.0 20.0 1.0 -sub-CLNC0391 ses-M12 MCI 2.0 74.44 M 7.0 20.0 1.0 -sub-CLNC0391 ses-M18 74.44 M 7.0 20.0 1.0 -sub-CLNC0391 ses-M24 MCI 2.0 74.44 M 7.0 20.0 1.0 -sub-CLNC0392 ses-M00 MCI 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M06 MCI 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M108 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M12 MCI 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M18 AD 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M24 AD 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M30 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M36 AD 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M48 AD 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M54 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M66 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M72 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M84 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M90 78.65 M 4.0 29.0 0.5 -sub-CLNC0392 ses-M96 78.65 M 4.0 29.0 0.5 -sub-CLNC0393 ses-M00 MCI 2.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M03 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M06 MCI 2.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M12 MCI 2.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M18 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M24 7.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M30 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M36 7.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M48 7.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0393 ses-M60 CN 1.0 79.24 F 7.0 29.0 0.5 -sub-CLNC0394 ses-M00 CN 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M06 CN 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M108 AD 3.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M12 CN 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M18 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M24 CN 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M30 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M36 CN 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M42 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M48 CN 1.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M54 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M60 CN 1.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M66 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M72 CN 1.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M78 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M84 MCI 2.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0394 ses-M96 5.0 73.02 F 9.0 21.0 0.0 -sub-CLNC0395 ses-M00 MCI 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M06 MCI 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M12 AD 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M18 AD 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M24 AD 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M30 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M36 AD 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M42 64.58 M 1.0 21.0 3.0 -sub-CLNC0395 ses-M48 AD 64.58 M 1.0 21.0 3.0 -sub-CLNC0396 ses-M00 CN 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M06 CN 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M102 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M108 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M12 CN 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M18 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M24 CN 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M30 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M36 CN 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M60 4.0 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M66 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M72 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M78 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M84 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M90 69.76 F 9.0 26.0 0.5 -sub-CLNC0396 ses-M96 69.76 F 9.0 26.0 0.5 -sub-CLNC0397 ses-M00 AD 3.0 83.48 F 2.0 22.0 3.0 -sub-CLNC0397 ses-M03 83.48 F 2.0 22.0 3.0 -sub-CLNC0397 ses-M06 AD 3.0 83.48 F 2.0 22.0 3.0 -sub-CLNC0397 ses-M12 AD 3.0 83.48 F 2.0 22.0 3.0 -sub-CLNC0398 ses-M00 MCI 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M06 MCI 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M12 MCI 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M18 AD 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M24 AD 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M30 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M36 AD 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M48 AD 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M60 AD 3.0 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M66 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M72 AD 3.0 73.88 M 3.0 23.0 0.5 -sub-CLNC0398 ses-M78 73.88 M 3.0 23.0 0.5 -sub-CLNC0399 ses-M00 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M06 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M12 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M18 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M24 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M30 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M36 MCI 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M60 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M72 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M78 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M84 64.83 M 6.0 22.0 2.0 -sub-CLNC0399 ses-M96 64.83 M 6.0 22.0 2.0 -sub-CLNC0400 ses-M00 MCI 2.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M03 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M06 MCI 2.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M12 MCI 2.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M18 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M24 7.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M30 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M36 CN 1.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M48 CN 1.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M60 CN 1.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0400 ses-M72 CN 1.0 61.22 F 7.0 27.0 2.0 -sub-CLNC0401 ses-M00 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M06 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M108 AD 3.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M12 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M18 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M24 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M30 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M36 MCI 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M42 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M48 MCI 2.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M54 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M60 MCI 2.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M66 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M72 MCI 2.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M78 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M84 MCI 2.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0401 ses-M96 5.0 65.38 F 7.0 20.0 2.0 -sub-CLNC0402 ses-M00 CN 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M06 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M12 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M18 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M24 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M30 68.79 F 3.0 21.0 0.0 -sub-CLNC0402 ses-M36 68.79 F 3.0 21.0 0.0 -sub-CLNC0403 ses-M00 MCI 2.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M03 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M06 MCI 2.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M12 MCI 2.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M18 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M24 MCI 2.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M30 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M36 MCI 2.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M42 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M48 7.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M60 CN 1.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0403 ses-M72 CN 1.0 57.91 F 5.0 21.0 1.0 -sub-CLNC0404 ses-M00 MCI 2.0 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M03 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M06 MCI 2.0 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M12 MCI 2.0 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M18 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M24 5.0 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M36 AD 3.0 67.26 F 4.0 24.0 2.0 -sub-CLNC0404 ses-M42 67.26 F 4.0 24.0 2.0 -sub-CLNC0405 ses-M00 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M06 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M108 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M12 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M18 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M24 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M30 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M36 MCI 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M42 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M48 AD 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M54 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M60 AD 3.0 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M66 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M72 AD 3.0 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M78 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M84 AD 3.0 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M90 76.67 F 7.0 29.0 3.0 -sub-CLNC0405 ses-M96 76.67 F 7.0 29.0 3.0 -sub-CLNC0406 ses-M00 CN 1.0 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M03 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M06 CN 1.0 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M12 CN 1.0 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M18 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M24 CN 1.0 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M30 88.75 M 5.0 24.0 0.0 -sub-CLNC0406 ses-M48 CN 1.0 88.75 M 5.0 24.0 0.0 -sub-CLNC0407 ses-M00 CN 1.0 63.57 F 6.0 23.0 2.0 -sub-CLNC0407 ses-M24 4.0 63.57 F 6.0 23.0 2.0 -sub-CLNC0408 ses-M00 MCI 2.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M03 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M06 5.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M12 AD 3.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M18 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M24 AD 3.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M36 AD 3.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0408 ses-M48 AD 3.0 71.05 M 2.0 29.0 2.0 -sub-CLNC0409 ses-M00 AD 3.0 69.7 F 4.0 21.0 0.0 -sub-CLNC0409 ses-M03 69.7 F 4.0 21.0 0.0 -sub-CLNC0409 ses-M06 AD 3.0 69.7 F 4.0 21.0 0.0 -sub-CLNC0409 ses-M12 AD 3.0 69.7 F 4.0 21.0 0.0 -sub-CLNC0409 ses-M18 69.7 F 4.0 21.0 0.0 -sub-CLNC0410 ses-M00 MCI 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M06 MCI 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M114 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M12 MCI 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M18 MCI 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M30 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M36 MCI 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M42 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M48 MCI 2.0 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M54 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M72 MCI 2.0 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M78 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M84 MCI 2.0 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M90 86.67 F 7.0 27.0 3.0 -sub-CLNC0410 ses-M96 MCI 2.0 86.67 F 7.0 27.0 3.0 -sub-CLNC0411 ses-M00 CN 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M06 CN 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M108 AD 3.0 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M12 CN 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M18 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M24 MCI 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M30 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M36 MCI 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M42 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M48 MCI 2.0 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M54 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M60 MCI 2.0 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M66 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M72 MCI 2.0 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M78 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M84 MCI 2.0 72.38 F 9.0 28.0 1.0 -sub-CLNC0411 ses-M90 72.38 F 9.0 28.0 1.0 -sub-CLNC0412 ses-M00 MCI 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M06 MCI 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M12 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M18 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M24 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M30 83.24 M 8.0 29.0 3.0 -sub-CLNC0412 ses-M36 83.24 M 8.0 29.0 3.0 -sub-CLNC0413 ses-M00 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M03 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M06 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M12 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M18 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M24 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M36 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0413 ses-M48 MCI 2.0 71.85 F 4.0 28.0 1.0 -sub-CLNC0414 ses-M00 AD 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M06 AD 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M12 AD 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M18 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M24 AD 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M30 70.56 F 9.0 28.0 0.0 -sub-CLNC0414 ses-M36 AD 70.56 F 9.0 28.0 0.0 -sub-CLNC0415 ses-M00 MCI 2.0 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M03 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M06 MCI 2.0 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M12 MCI 2.0 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M18 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M24 5.0 79.13 F 6.0 21.0 1.0 -sub-CLNC0415 ses-M30 79.13 F 6.0 21.0 1.0 -sub-CLNC0416 ses-M00 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M03 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M06 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M12 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M18 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M24 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M36 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0416 ses-M48 MCI 2.0 63.82 F 4.0 25.0 0.5 -sub-CLNC0417 ses-M00 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M03 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M06 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M12 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M18 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M24 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M30 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M36 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M42 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M48 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0417 ses-M60 MCI 2.0 74.67 M 9.0 26.0 2.0 -sub-CLNC0418 ses-M00 AD 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M06 AD 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M12 AD 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M18 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M24 AD 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M30 74.5 M 6.0 23.0 0.0 -sub-CLNC0418 ses-M36 74.5 M 6.0 23.0 0.0 -sub-CLNC0419 ses-M00 MCI 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M06 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M12 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M18 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M24 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M30 85.48 M 7.0 23.0 1.0 -sub-CLNC0419 ses-M36 85.48 M 7.0 23.0 1.0 -sub-CLNC0420 ses-M00 MCI 2.0 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M03 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M06 MCI 2.0 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M12 MCI 2.0 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M18 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M24 MCI 2.0 80.56 F 8.0 24.0 3.0 -sub-CLNC0420 ses-M36 MCI 2.0 80.56 F 8.0 24.0 3.0 -sub-CLNC0421 ses-M00 CN 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M06 CN 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M12 CN 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M18 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M24 CN 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M30 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M36 CN 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M42 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M48 CN 1.0 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M54 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M60 CN 1.0 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M66 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M72 CN 1.0 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M84 CN 1.0 75.24 F 3.0 24.0 3.0 -sub-CLNC0421 ses-M96 CN 1.0 75.24 F 3.0 24.0 3.0 -sub-CLNC0422 ses-M00 AD 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M06 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M12 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M18 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M24 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M30 72.12 F 1.0 29.0 0.5 -sub-CLNC0422 ses-M36 72.12 F 1.0 29.0 0.5 -sub-CLNC0423 ses-M00 CN 1.0 67.44 F 2.0 26.0 3.0 -sub-CLNC0423 ses-M06 CN 1.0 67.44 F 2.0 26.0 3.0 -sub-CLNC0423 ses-M12 CN 1.0 67.44 F 2.0 26.0 3.0 -sub-CLNC0423 ses-M18 67.44 F 2.0 26.0 3.0 -sub-CLNC0423 ses-M24 CN 1.0 67.44 F 2.0 26.0 3.0 -sub-CLNC0423 ses-M48 CN 1.0 67.44 F 2.0 26.0 3.0 -sub-CLNC0424 ses-M00 CN 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M06 CN 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M12 CN 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M120 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M18 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M24 CN 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M30 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M36 CN 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M42 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M48 CN 1.0 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M54 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M60 CN 1.0 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M66 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M72 CN 1.0 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M78 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M84 CN 1.0 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M90 75.03 F 5.0 21.0 0.0 -sub-CLNC0424 ses-M96 CN 1.0 75.03 F 5.0 21.0 0.0 -sub-CLNC0425 ses-M00 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M06 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M12 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M18 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M24 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M30 73.09 M 5.0 26.0 0.5 -sub-CLNC0425 ses-M36 MCI 73.09 M 5.0 26.0 0.5 -sub-CLNC0426 ses-M00 CN 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M06 CN 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M12 CN 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M18 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M24 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M30 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M36 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M60 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M66 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M72 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M78 54.98 M 9.0 22.0 3.0 -sub-CLNC0426 ses-M84 54.98 M 9.0 22.0 3.0 -sub-CLNC0427 ses-M00 AD 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M06 AD 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M12 AD 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M18 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M24 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M30 87.88 F 2.0 25.0 0.5 -sub-CLNC0427 ses-M36 87.88 F 2.0 25.0 0.5 -sub-CLNC0428 ses-M00 CN 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M06 CN 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M12 CN 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M18 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M24 CN 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M30 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M36 MCI 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M48 MCI 2.0 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M54 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M60 MCI 2.0 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M66 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M72 MCI 2.0 74.28 M 3.0 20.0 1.0 -sub-CLNC0428 ses-M78 74.28 M 3.0 20.0 1.0 -sub-CLNC0429 ses-M00 CN 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M06 CN 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M12 CN 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M18 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M24 CN 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M30 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M36 CN 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M60 CN 1.0 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M66 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M72 CN 1.0 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M78 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M84 CN 1.0 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M90 68.92 F 7.0 25.0 3.0 -sub-CLNC0429 ses-M96 CN 1.0 68.92 F 7.0 25.0 3.0 -sub-CLNC0430 ses-M00 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M03 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M06 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M12 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M18 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M24 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M36 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0430 ses-M48 MCI 2.0 73.5 M 6.0 24.0 2.0 -sub-CLNC0431 ses-M00 CN 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M06 CN 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M12 CN 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M18 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M24 CN 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M30 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M36 CN 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M42 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M48 CN 1.0 70.89 F 4.0 29.0 2.0 -sub-CLNC0431 ses-M54 70.89 F 4.0 29.0 2.0 -sub-CLNC0432 ses-M00 CN 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M06 CN 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M12 CN 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M18 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M24 CN 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M30 68.39 M 6.0 22.0 0.0 -sub-CLNC0432 ses-M36 CN 68.39 M 6.0 22.0 0.0 -sub-CLNC0433 ses-M00 MCI 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M06 MCI 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M12 MCI 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M18 MCI 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M24 AD 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M30 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M36 AD 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M42 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M48 AD 61.88 M 8.0 25.0 3.0 -sub-CLNC0433 ses-M54 61.88 M 8.0 25.0 3.0 -sub-CLNC0434 ses-M00 CN 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M06 CN 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M108 MCI 2.0 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M12 CN 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M18 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M24 CN 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M30 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M36 MCI 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M48 MCI 81.95 F 3.0 26.0 0.5 -sub-CLNC0434 ses-M72 MCI 2.0 81.95 F 3.0 26.0 0.5 -sub-CLNC0435 ses-M00 MCI 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M06 MCI 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M108 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M12 MCI 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M120 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M18 AD 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M24 AD 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M30 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M36 AD 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M42 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M48 AD 3.0 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M54 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M60 AD 3.0 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M66 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M72 AD 3.0 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M78 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M84 AD 3.0 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M90 78.49 F 2.0 29.0 0.0 -sub-CLNC0435 ses-M96 78.49 F 2.0 29.0 0.0 -sub-CLNC0436 ses-M00 CN 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M06 CN 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M108 AD 3.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M12 CN 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M18 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M24 CN 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M30 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M36 CN 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M42 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M48 4.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M54 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M60 MCI 2.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M66 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M72 5.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M78 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M84 AD 3.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M90 79.51 F 1.0 24.0 1.0 -sub-CLNC0436 ses-M96 AD 3.0 79.51 F 1.0 24.0 1.0 -sub-CLNC0437 ses-M00 CN 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M06 CN 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M12 CN 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M18 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M24 MCI 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M30 66.32 F 3.0 28.0 0.5 -sub-CLNC0437 ses-M36 66.32 F 3.0 28.0 0.5 -sub-CLNC0438 ses-M00 MCI 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M06 AD 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M12 AD 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M18 AD 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M24 AD 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M30 79.69 M 6.0 22.0 0.0 -sub-CLNC0438 ses-M36 AD 79.69 M 6.0 22.0 0.0 -sub-CLNC0439 ses-M00 CN 1.0 67.87 F 2.0 20.0 2.0 -sub-CLNC0439 ses-M03 67.87 F 2.0 20.0 2.0 -sub-CLNC0439 ses-M06 CN 1.0 67.87 F 2.0 20.0 2.0 -sub-CLNC0439 ses-M12 CN 1.0 67.87 F 2.0 20.0 2.0 -sub-CLNC0439 ses-M18 67.87 F 2.0 20.0 2.0 -sub-CLNC0439 ses-M24 CN 1.0 67.87 F 2.0 20.0 2.0 -sub-CLNC0440 ses-M00 AD 3.0 59.46 F 5.0 27.0 3.0 -sub-CLNC0440 ses-M03 59.46 F 5.0 27.0 3.0 -sub-CLNC0440 ses-M06 AD 3.0 59.46 F 5.0 27.0 3.0 -sub-CLNC0440 ses-M12 AD 3.0 59.46 F 5.0 27.0 3.0 -sub-CLNC0441 ses-M00 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M03 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M06 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M12 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M18 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M24 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M30 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M36 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0441 ses-M48 MCI 2.0 73.07 F 5.0 20.0 3.0 -sub-CLNC0442 ses-M00 MCI 2.0 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M03 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M06 MCI 2.0 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M12 MCI 2.0 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M18 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M24 MCI 2.0 64.24 F 8.0 20.0 0.0 -sub-CLNC0442 ses-M36 MCI 2.0 64.24 F 8.0 20.0 0.0 -sub-CLNC0443 ses-M00 MCI 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M06 MCI 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M12 MCI 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M18 AD 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M24 AD 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M30 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M36 AD 79.62 M 7.0 24.0 3.0 -sub-CLNC0443 ses-M42 79.62 M 7.0 24.0 3.0 -sub-CLNC0444 ses-M00 MCI 2.0 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M03 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M06 MCI 2.0 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M12 MCI 2.0 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M18 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M24 MCI 2.0 72.18 M 1.0 27.0 3.0 -sub-CLNC0444 ses-M36 MCI 2.0 72.18 M 1.0 27.0 3.0 -sub-CLNC0445 ses-M00 MCI 2.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M03 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M06 MCI 2.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M12 MCI 2.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M18 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M24 MCI 2.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M36 MCI 2.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0445 ses-M48 5.0 75.55 F 8.0 21.0 1.0 -sub-CLNC0446 ses-M00 CN 1.0 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M03 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M06 CN 1.0 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M12 CN 1.0 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M18 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M24 CN 1.0 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M30 78.65 F 1.0 29.0 1.0 -sub-CLNC0446 ses-M48 CN 1.0 78.65 F 1.0 29.0 1.0 -sub-CLNC0447 ses-M00 AD 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M06 AD 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M12 AD 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M18 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M24 AD 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M30 70.57 F 9.0 20.0 3.0 -sub-CLNC0447 ses-M36 70.57 F 9.0 20.0 3.0 -sub-CLNC0448 ses-M00 CN 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M06 CN 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M108 CN 1.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M12 CN 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M120 CN 1.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M18 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M24 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M30 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M36 CN 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M42 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M48 CN 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M60 CN 1.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M66 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M72 CN 1.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M78 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M84 4.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M90 87.31 F 6.0 22.0 0.0 -sub-CLNC0448 ses-M96 CN 1.0 87.31 F 6.0 22.0 0.0 -sub-CLNC0449 ses-M00 CN 1.0 79.32 F 3.0 26.0 2.0 -sub-CLNC0449 ses-M03 79.32 F 3.0 26.0 2.0 -sub-CLNC0449 ses-M06 CN 1.0 79.32 F 3.0 26.0 2.0 -sub-CLNC0449 ses-M24 CN 1.0 79.32 F 3.0 26.0 2.0 -sub-CLNC0450 ses-M00 CN 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M06 CN 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M12 CN 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M18 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M24 MCI 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M30 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M36 MCI 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M42 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M48 MCI 2.0 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M54 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M60 MCI 2.0 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M66 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M72 71.89 F 6.0 28.0 2.0 -sub-CLNC0450 ses-M84 71.89 F 6.0 28.0 2.0 -sub-CLNC0451 ses-M00 CN 1.0 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M03 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M06 CN 1.0 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M12 CN 1.0 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M18 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M24 CN 1.0 80.42 F 8.0 28.0 0.5 -sub-CLNC0451 ses-M48 80.42 F 8.0 28.0 0.5 -sub-CLNC0452 ses-M00 MCI 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M06 AD 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M12 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M18 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M24 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M30 68.2 F 6.0 22.0 0.5 -sub-CLNC0452 ses-M36 68.2 F 6.0 22.0 0.5 -sub-CLNC0453 ses-M00 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M06 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M12 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M18 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M24 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M30 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M36 MCI 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M42 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M48 AD 3.0 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M54 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M60 AD 3.0 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M66 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M72 AD 3.0 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M78 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M84 AD 3.0 72.89 F 3.0 25.0 3.0 -sub-CLNC0453 ses-M96 AD 3.0 72.89 F 3.0 25.0 3.0 -sub-CLNC0454 ses-M00 CN 1.0 81.68 M 2.0 20.0 3.0 -sub-CLNC0455 ses-M00 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M03 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M06 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M12 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M18 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M24 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M30 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M36 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0455 ses-M48 MCI 2.0 73.1 M 8.0 23.0 0.5 -sub-CLNC0456 ses-M00 MCI 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M06 MCI 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M12 MCI 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M18 MCI 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M24 MCI 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M30 75.15 F 1.0 25.0 1.0 -sub-CLNC0456 ses-M36 75.15 F 1.0 25.0 1.0 -sub-CLNC0457 ses-M00 CN 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M06 CN 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M12 CN 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M18 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M24 CN 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M30 76.3 M 4.0 22.0 3.0 -sub-CLNC0457 ses-M36 76.3 M 4.0 22.0 3.0 -sub-CLNC0458 ses-M00 CN 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M06 CN 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M12 CN 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M18 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M24 CN 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M30 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M36 MCI 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M42 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M48 MCI 2.0 72.76 F 3.0 29.0 3.0 -sub-CLNC0458 ses-M54 72.76 F 3.0 29.0 3.0 -sub-CLNC0459 ses-M00 CN 1.0 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M03 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M06 CN 1.0 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M12 CN 1.0 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M18 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M24 CN 1.0 80.16 F 4.0 29.0 0.5 -sub-CLNC0459 ses-M30 80.16 F 4.0 29.0 0.5 -sub-CLNC0460 ses-M00 CN 1.0 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M03 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M06 CN 1.0 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M12 CN 1.0 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M18 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M24 CN 1.0 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M30 81.2 M 2.0 28.0 0.5 -sub-CLNC0460 ses-M48 CN 1.0 81.2 M 2.0 28.0 0.5 -sub-CLNC0461 ses-M00 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M06 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M108 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M12 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M18 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M24 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M30 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M36 MCI 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M42 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M48 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M54 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M60 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M66 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M72 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M78 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M84 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0461 ses-M96 MCI 2.0 72.39 M 7.0 25.0 0.0 -sub-CLNC0462 ses-M00 CN 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M06 CN 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M12 CN 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M18 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M24 CN 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M30 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M36 CN 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M42 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M48 CN 1.0 75.24 F 8.0 27.0 2.0 -sub-CLNC0462 ses-M54 75.24 F 8.0 27.0 2.0 -sub-CLNC0463 ses-M00 MCI 2.0 83.94 F 6.0 24.0 0.5 -sub-CLNC0463 ses-M03 83.94 F 6.0 24.0 0.5 -sub-CLNC0463 ses-M06 MCI 2.0 83.94 F 6.0 24.0 0.5 -sub-CLNC0463 ses-M12 MCI 2.0 83.94 F 6.0 24.0 0.5 -sub-CLNC0463 ses-M24 MCI 2.0 83.94 F 6.0 24.0 0.5 -sub-CLNC0463 ses-M48 5.0 83.94 F 6.0 24.0 0.5 -sub-CLNC0464 ses-M00 CN 78.33 F 8.0 28.0 2.0 -sub-CLNC0464 ses-M06 CN 78.33 F 8.0 28.0 2.0 -sub-CLNC0465 ses-M00 MCI 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M06 MCI 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M12 AD 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M18 AD 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M24 AD 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M30 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M36 AD 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M42 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M48 AD 3.0 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M54 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M60 AD 3.0 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M66 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M72 AD 3.0 74.21 F 4.0 25.0 0.5 -sub-CLNC0465 ses-M78 74.21 F 4.0 25.0 0.5 -sub-CLNC0466 ses-M00 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M03 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M06 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M12 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M18 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M24 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M36 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0466 ses-M48 MCI 2.0 67.02 F 7.0 20.0 1.0 -sub-CLNC0467 ses-M00 AD 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M06 AD 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M12 AD 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M18 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M24 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M30 76.17 F 2.0 24.0 2.0 -sub-CLNC0467 ses-M36 76.17 F 2.0 24.0 2.0 -sub-CLNC0468 ses-M00 MCI 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M06 MCI 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M12 MCI 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M18 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M24 MCI 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M30 75.51 F 2.0 26.0 0.5 -sub-CLNC0468 ses-M36 MCI 75.51 F 2.0 26.0 0.5 -sub-CLNC0469 ses-M00 CN 1.0 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M03 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M06 CN 1.0 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M12 CN 1.0 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M18 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M24 CN 1.0 77.01 F 8.0 23.0 2.0 -sub-CLNC0469 ses-M48 CN 1.0 77.01 F 8.0 23.0 2.0 -sub-CLNC0470 ses-M00 MCI 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M06 MCI 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M12 MCI 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M18 MCI 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M24 AD 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M30 71.32 M 8.0 20.0 3.0 -sub-CLNC0470 ses-M36 71.32 M 8.0 20.0 3.0 -sub-CLNC0471 ses-M00 CN 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M06 CN 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M12 CN 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M18 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M24 CN 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M30 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M36 CN 82.28 M 3.0 21.0 0.5 -sub-CLNC0471 ses-M42 82.28 M 3.0 21.0 0.5 -sub-CLNC0472 ses-M00 MCI 2.0 69.7 F 4.0 27.0 3.0 -sub-CLNC0472 ses-M03 69.7 F 4.0 27.0 3.0 -sub-CLNC0472 ses-M06 MCI 2.0 69.7 F 4.0 27.0 3.0 -sub-CLNC0472 ses-M12 MCI 2.0 69.7 F 4.0 27.0 3.0 -sub-CLNC0472 ses-M18 69.7 F 4.0 27.0 3.0 -sub-CLNC0473 ses-M00 CN 1.0 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M03 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M06 CN 1.0 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M12 CN 1.0 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M18 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M24 CN 1.0 77.71 F 9.0 20.0 3.0 -sub-CLNC0473 ses-M48 CN 1.0 77.71 F 9.0 20.0 3.0 -sub-CLNC0474 ses-M00 MCI 2.0 73.69 F 4.0 20.0 3.0 -sub-CLNC0474 ses-M03 73.69 F 4.0 20.0 3.0 -sub-CLNC0475 ses-M00 AD 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M06 AD 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M12 AD 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M18 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M24 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M30 81.24 M 4.0 23.0 0.0 -sub-CLNC0475 ses-M36 81.24 M 4.0 23.0 0.0 -sub-CLNC0476 ses-M00 CN 1.0 65.11 F 9.0 21.0 3.0 -sub-CLNC0476 ses-M03 65.11 F 9.0 21.0 3.0 -sub-CLNC0476 ses-M06 CN 1.0 65.11 F 9.0 21.0 3.0 -sub-CLNC0476 ses-M24 CN 1.0 65.11 F 9.0 21.0 3.0 -sub-CLNC0477 ses-M00 CN 1.0 68.33 M 8.0 26.0 1.0 -sub-CLNC0477 ses-M06 CN 1.0 68.33 M 8.0 26.0 1.0 -sub-CLNC0477 ses-M12 CN 1.0 68.33 M 8.0 26.0 1.0 -sub-CLNC0477 ses-M48 68.33 M 8.0 26.0 1.0 -sub-CLNC0478 ses-M00 AD 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M06 AD 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M12 AD 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M18 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M24 AD 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M30 68.38 M 4.0 25.0 0.0 -sub-CLNC0478 ses-M36 68.38 M 4.0 25.0 0.0 -sub-CLNC0479 ses-M00 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M03 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M06 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M12 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M18 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M24 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M30 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M36 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0479 ses-M48 MCI 2.0 72.61 M 1.0 22.0 3.0 -sub-CLNC0480 ses-M00 CN 1.0 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M03 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M06 CN 1.0 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M12 CN 1.0 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M18 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M24 CN 1.0 66.8 M 9.0 24.0 0.0 -sub-CLNC0480 ses-M48 CN 1.0 66.8 M 9.0 24.0 0.0 -sub-CLNC0481 ses-M00 MCI 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M06 MCI 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M12 MCI 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M18 MCI 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M24 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M30 70.15 F 1.0 25.0 3.0 -sub-CLNC0481 ses-M36 70.15 F 1.0 25.0 3.0 -sub-CLNC0482 ses-M00 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M06 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M12 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M120 CN 1.0 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M18 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M24 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M30 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M36 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M48 CN 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M60 CN 1.0 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M66 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M72 CN 1.0 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M78 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M84 CN 1.0 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M90 79.99 M 6.0 22.0 1.0 -sub-CLNC0482 ses-M96 CN 1.0 79.99 M 6.0 22.0 1.0 -sub-CLNC0483 ses-M00 CN 1.0 81.69 M 9.0 29.0 2.0 -sub-CLNC0483 ses-M06 CN 1.0 81.69 M 9.0 29.0 2.0 -sub-CLNC0483 ses-M12 CN 1.0 81.69 M 9.0 29.0 2.0 -sub-CLNC0483 ses-M24 CN 1.0 81.69 M 9.0 29.0 2.0 -sub-CLNC0484 ses-M00 CN 1.0 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M03 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M06 CN 1.0 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M12 CN 1.0 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M18 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M24 CN 1.0 67.05 M 8.0 28.0 2.0 -sub-CLNC0484 ses-M48 67.05 M 8.0 28.0 2.0 -sub-CLNC0485 ses-M00 CN 1.0 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M03 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M06 CN 1.0 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M12 CN 1.0 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M18 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M24 CN 1.0 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M30 76.92 M 4.0 27.0 2.0 -sub-CLNC0485 ses-M48 CN 1.0 76.92 M 4.0 27.0 2.0 -sub-CLNC0486 ses-M00 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M03 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M06 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M12 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M18 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M24 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M30 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M36 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M42 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M48 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0486 ses-M60 MCI 2.0 78.62 F 6.0 22.0 3.0 -sub-CLNC0487 ses-M00 CN 1.0 80.11 F 2.0 21.0 3.0 -sub-CLNC0487 ses-M24 CN 1.0 80.11 F 2.0 21.0 3.0 -sub-CLNC0488 ses-M00 CN 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M06 CN 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M12 CN 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M18 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M24 CN 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M30 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M36 CN 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M42 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M48 CN 1.0 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M54 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M60 4.0 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M66 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M72 5.0 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M78 76.3 F 3.0 29.0 2.0 -sub-CLNC0488 ses-M84 AD 3.0 76.3 F 3.0 29.0 2.0 -sub-CLNC0489 ses-M00 CN 1.0 60.92 M 7.0 22.0 1.0 -sub-CLNC0489 ses-M03 60.92 M 7.0 22.0 1.0 -sub-CLNC0489 ses-M06 CN 1.0 60.92 M 7.0 22.0 1.0 -sub-CLNC0489 ses-M24 4.0 60.92 M 7.0 22.0 1.0 -sub-CLNC0490 ses-M00 MCI 2.0 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M03 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M06 MCI 2.0 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M12 MCI 2.0 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M18 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M24 5.0 68.04 F 1.0 28.0 1.0 -sub-CLNC0490 ses-M36 AD 3.0 68.04 F 1.0 28.0 1.0 -sub-CLNC0491 ses-M00 MCI 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M06 MCI 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M12 MCI 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M18 MCI 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M24 MCI 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M30 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M36 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M42 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M48 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M54 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M60 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M66 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M72 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M78 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M84 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0491 ses-M96 MCI 2.0 71.41 F 6.0 29.0 1.0 -sub-CLNC0492 ses-M00 AD 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M06 AD 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M12 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M18 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M24 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M30 68.67 M 2.0 20.0 3.0 -sub-CLNC0492 ses-M36 68.67 M 2.0 20.0 3.0 -sub-CLNC0493 ses-M00 CN 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M06 CN 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M108 AD 3.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M12 CN 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M18 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M24 CN 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M30 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M36 CN 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M42 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M48 CN 1.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M54 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M60 CN 1.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M66 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M72 CN 1.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M78 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M84 6.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0493 ses-M96 AD 3.0 75.97 M 7.0 25.0 1.0 -sub-CLNC0494 ses-M00 MCI 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M06 MCI 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M12 MCI 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M18 MCI 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M24 AD 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M30 78.8 M 1.0 28.0 1.0 -sub-CLNC0494 ses-M36 AD 78.8 M 1.0 28.0 1.0 -sub-CLNC0495 ses-M00 CN 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M06 CN 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M12 CN 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M18 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M24 CN 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M30 77.45 M 3.0 24.0 3.0 -sub-CLNC0495 ses-M36 77.45 M 3.0 24.0 3.0 -sub-CLNC0496 ses-M00 MCI 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M06 MCI 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M12 MCI 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M18 MCI 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M24 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M30 80.49 M 1.0 21.0 3.0 -sub-CLNC0496 ses-M36 80.49 M 1.0 21.0 3.0 -sub-CLNC0497 ses-M00 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M03 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M06 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M12 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M18 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M24 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M30 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M36 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M42 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M48 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0497 ses-M60 MCI 2.0 79.84 M 2.0 21.0 3.0 -sub-CLNC0498 ses-M00 MCI 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M06 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M12 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M18 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M24 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M30 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M36 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M42 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M48 AD 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M54 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M60 AD 3.0 65.38 F 7.0 27.0 3.0 -sub-CLNC0498 ses-M66 65.38 F 7.0 27.0 3.0 -sub-CLNC0499 ses-M00 CN 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M06 CN 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M12 CN 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M18 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M24 CN 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M30 89.63 M 9.0 24.0 3.0 -sub-CLNC0499 ses-M36 89.63 M 9.0 24.0 3.0 -sub-CLNC0500 ses-M00 AD 3.0 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M03 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M06 AD 3.0 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M12 AD 3.0 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M18 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M24 AD 3.0 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M30 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M36 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M42 68.48 F 6.0 23.0 0.0 -sub-CLNC0500 ses-M48 68.48 F 6.0 23.0 0.0 -sub-CLNC0501 ses-M00 MCI 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M06 MCI 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M12 MCI 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M18 MCI 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M24 MCI 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M30 70.45 M 3.0 20.0 1.0 -sub-CLNC0501 ses-M36 AD 70.45 M 3.0 20.0 1.0 -sub-CLNC0502 ses-M00 AD 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M06 AD 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M12 AD 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M18 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M24 AD 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M30 65.96 M 2.0 21.0 0.0 -sub-CLNC0502 ses-M36 AD 65.96 M 2.0 21.0 0.0 -sub-CLNC0503 ses-M00 MCI 2.0 71.5 M 8.0 20.0 3.0 -sub-CLNC0503 ses-M03 71.5 M 8.0 20.0 3.0 -sub-CLNC0503 ses-M06 MCI 2.0 71.5 M 8.0 20.0 3.0 -sub-CLNC0504 ses-M00 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M03 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M06 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M12 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M18 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M24 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M30 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M36 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M42 68.23 F 7.0 26.0 1.0 -sub-CLNC0504 ses-M48 MCI 2.0 68.23 F 7.0 26.0 1.0 -sub-CLNC0505 ses-M00 AD 3.0 79.54 F 7.0 29.0 1.0 -sub-CLNC0505 ses-M03 79.54 F 7.0 29.0 1.0 -sub-CLNC0505 ses-M06 AD 3.0 79.54 F 7.0 29.0 1.0 -sub-CLNC0505 ses-M12 79.54 F 7.0 29.0 1.0 -sub-CLNC0506 ses-M00 CN 1.0 74.13 M 4.0 22.0 0.0 -sub-CLNC0506 ses-M03 74.13 M 4.0 22.0 0.0 -sub-CLNC0506 ses-M06 CN 1.0 74.13 M 4.0 22.0 0.0 -sub-CLNC0507 ses-M00 MCI 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M06 MCI 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M12 MCI 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M18 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M24 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M30 64.57 M 1.0 20.0 3.0 -sub-CLNC0507 ses-M36 64.57 M 1.0 20.0 3.0 -sub-CLNC0508 ses-M00 AD 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M06 AD 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M12 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M18 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M24 AD 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M30 61.9 F 1.0 22.0 0.0 -sub-CLNC0508 ses-M36 61.9 F 1.0 22.0 0.0 -sub-CLNC0509 ses-M00 CN 1.0 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M03 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M06 CN 1.0 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M12 CN 1.0 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M18 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M24 CN 1.0 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M30 69.04 M 1.0 21.0 0.0 -sub-CLNC0509 ses-M48 CN 1.0 69.04 M 1.0 21.0 0.0 -sub-CLNC0510 ses-M00 MCI 2.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M03 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M12 MCI 2.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M18 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M24 MCI 2.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M30 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M36 MCI 2.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M48 5.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0510 ses-M60 AD 3.0 77.45 M 6.0 26.0 0.5 -sub-CLNC0511 ses-M00 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M03 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M06 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M12 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M18 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M24 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M36 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0511 ses-M48 MCI 2.0 75.35 F 1.0 29.0 2.0 -sub-CLNC0512 ses-M00 MCI 2.0 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M03 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M06 MCI 2.0 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M12 MCI 2.0 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M18 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M24 5.0 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M36 AD 3.0 63.02 M 2.0 28.0 0.0 -sub-CLNC0512 ses-M48 63.02 M 2.0 28.0 0.0 -sub-CLNC0513 ses-M00 MCI 2.0 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M03 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M06 MCI 2.0 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M12 5.0 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M18 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M24 AD 3.0 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M30 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M36 8.0 82.11 F 5.0 21.0 0.5 -sub-CLNC0513 ses-M48 82.11 F 5.0 21.0 0.5 -sub-CLNC0514 ses-M00 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M06 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M12 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M18 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M24 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M30 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M36 MCI 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M42 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M48 5.0 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M54 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M60 AD 3.0 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M66 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M72 AD 3.0 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M78 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M84 AD 3.0 78.24 M 2.0 24.0 0.5 -sub-CLNC0514 ses-M96 AD 3.0 78.24 M 2.0 24.0 0.5 -sub-CLNC0515 ses-M00 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M06 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M108 5.0 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M12 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M120 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M18 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M24 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M30 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M36 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M48 CN 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M60 4.0 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M66 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M72 MCI 2.0 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M78 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M84 MCI 2.0 80.22 F 6.0 23.0 2.0 -sub-CLNC0515 ses-M96 MCI 2.0 80.22 F 6.0 23.0 2.0 -sub-CLNC0516 ses-M00 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M06 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M12 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M18 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M24 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M30 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M36 MCI 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M42 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M48 MCI 2.0 66.69 M 6.0 22.0 0.0 -sub-CLNC0516 ses-M54 66.69 M 6.0 22.0 0.0 -sub-CLNC0517 ses-M00 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M03 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M06 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M12 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M18 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M24 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M30 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M36 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M42 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M48 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0517 ses-M60 MCI 2.0 76.96 M 6.0 29.0 1.0 -sub-CLNC0518 ses-M00 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M06 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M12 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M18 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M24 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M30 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M36 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M42 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M48 CN 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M60 CN 1.0 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M72 CN 1.0 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M78 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M84 CN 1.0 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M90 77.36 M 1.0 22.0 3.0 -sub-CLNC0518 ses-M96 CN 1.0 77.36 M 1.0 22.0 3.0 -sub-CLNC0519 ses-M00 AD 3.0 71.95 F 5.0 29.0 0.5 -sub-CLNC0519 ses-M03 71.95 F 5.0 29.0 0.5 -sub-CLNC0519 ses-M06 AD 3.0 71.95 F 5.0 29.0 0.5 -sub-CLNC0519 ses-M12 AD 3.0 71.95 F 5.0 29.0 0.5 -sub-CLNC0520 ses-M00 MCI 2.0 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M03 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M06 MCI 2.0 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M12 5.0 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M18 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M24 AD 3.0 75.26 M 5.0 22.0 0.0 -sub-CLNC0520 ses-M36 AD 3.0 75.26 M 5.0 22.0 0.0 -sub-CLNC0521 ses-M00 CN 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M06 CN 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M108 5.0 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M12 CN 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M18 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M24 CN 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M30 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M36 CN 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M42 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M48 CN 1.0 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M54 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M66 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M72 CN 1.0 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M78 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M84 4.0 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M90 84.97 M 9.0 21.0 0.5 -sub-CLNC0521 ses-M96 MCI 2.0 84.97 M 9.0 21.0 0.5 -sub-CLNC0522 ses-M00 CN 1.0 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M03 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M06 CN 1.0 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M12 CN 1.0 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M18 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M24 84.82 M 4.0 23.0 0.0 -sub-CLNC0522 ses-M48 84.82 M 4.0 23.0 0.0 -sub-CLNC0523 ses-M00 MCI 2.0 65.68 M 4.0 25.0 2.0 -sub-CLNC0523 ses-M03 65.68 M 4.0 25.0 2.0 -sub-CLNC0523 ses-M06 MCI 2.0 65.68 M 4.0 25.0 2.0 -sub-CLNC0523 ses-M18 65.68 M 4.0 25.0 2.0 -sub-CLNC0524 ses-M00 CN 1.0 75.87 M 5.0 26.0 3.0 -sub-CLNC0524 ses-M06 75.87 M 5.0 26.0 3.0 -sub-CLNC0524 ses-M12 CN 1.0 75.87 M 5.0 26.0 3.0 -sub-CLNC0524 ses-M24 CN 1.0 75.87 M 5.0 26.0 3.0 -sub-CLNC0525 ses-M00 CN 1.0 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M03 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M06 CN 1.0 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M12 CN 1.0 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M18 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M24 CN 1.0 87.72 M 4.0 23.0 1.0 -sub-CLNC0525 ses-M30 87.72 M 4.0 23.0 1.0 -sub-CLNC0526 ses-M00 MCI 2.0 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M03 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M06 MCI 2.0 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M12 MCI 2.0 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M18 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M24 5.0 75.0 M 4.0 27.0 0.5 -sub-CLNC0526 ses-M36 MCI 2.0 75.0 M 4.0 27.0 0.5 -sub-CLNC0527 ses-M00 MCI 2.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M03 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M06 5.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M12 AD 3.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M18 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M24 AD 3.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M30 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M36 AD 3.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M48 AD 3.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0527 ses-M60 AD 3.0 74.73 M 9.0 29.0 2.0 -sub-CLNC0528 ses-M00 CN 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M06 CN 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M12 CN 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M18 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M24 CN 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M30 74.49 F 1.0 21.0 0.0 -sub-CLNC0528 ses-M36 CN 74.49 F 1.0 21.0 0.0 -sub-CLNC0529 ses-M00 MCI 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M06 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M12 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M18 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M24 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M30 75.11 M 2.0 22.0 3.0 -sub-CLNC0529 ses-M36 75.11 M 2.0 22.0 3.0 -sub-CLNC0530 ses-M00 CN 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M06 CN 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M108 CN 1.0 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M12 CN 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M18 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M24 CN 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M30 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M48 CN 1.0 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M54 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M60 CN 1.0 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M66 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M72 CN 1.0 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M78 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M84 CN 1.0 75.84 M 2.0 27.0 1.0 -sub-CLNC0530 ses-M90 75.84 M 2.0 27.0 1.0 -sub-CLNC0531 ses-M00 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M03 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M06 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M12 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M18 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M24 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M30 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M36 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0531 ses-M60 CN 1.0 65.92 F 9.0 20.0 3.0 -sub-CLNC0532 ses-M00 MCI 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M06 MCI 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M12 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M18 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M24 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M30 87.31 M 8.0 28.0 2.0 -sub-CLNC0532 ses-M36 87.31 M 8.0 28.0 2.0 -sub-CLNC0533 ses-M00 AD 79.38 F 4.0 26.0 0.0 -sub-CLNC0534 ses-M00 AD 3.0 61.66 F 9.0 29.0 0.5 -sub-CLNC0534 ses-M03 61.66 F 9.0 29.0 0.5 -sub-CLNC0534 ses-M06 AD 3.0 61.66 F 9.0 29.0 0.5 -sub-CLNC0534 ses-M12 AD 3.0 61.66 F 9.0 29.0 0.5 -sub-CLNC0534 ses-M24 AD 3.0 61.66 F 9.0 29.0 0.5 -sub-CLNC0535 ses-M00 MCI 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M06 MCI 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M12 AD 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M18 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M24 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M30 66.36 M 3.0 29.0 0.5 -sub-CLNC0535 ses-M36 66.36 M 3.0 29.0 0.5 -sub-CLNC0536 ses-M00 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M03 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M06 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M12 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M18 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M24 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M30 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M36 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M42 83.98 M 3.0 24.0 2.0 -sub-CLNC0536 ses-M48 MCI 2.0 83.98 M 3.0 24.0 2.0 -sub-CLNC0537 ses-M00 CN 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M06 CN 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M12 CN 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M120 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M18 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M24 CN 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M30 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M36 CN 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M48 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M54 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M60 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M66 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M72 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M78 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M84 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0537 ses-M96 CN 1.0 82.94 M 2.0 27.0 0.0 -sub-CLNC0538 ses-M00 AD 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M06 AD 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M12 AD 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M18 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M24 AD 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M30 73.18 F 3.0 28.0 3.0 -sub-CLNC0538 ses-M36 73.18 F 3.0 28.0 3.0 -sub-CLNC0539 ses-M00 MCI 2.0 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M03 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M06 MCI 2.0 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M12 5.0 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M18 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M24 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M36 71.74 M 5.0 27.0 3.0 -sub-CLNC0539 ses-M48 71.74 M 5.0 27.0 3.0 -sub-CLNC0540 ses-M00 CN 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M06 CN 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M12 CN 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M18 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M24 CN 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M30 77.01 M 5.0 29.0 0.5 -sub-CLNC0540 ses-M36 CN 77.01 M 5.0 29.0 0.5 -sub-CLNC0541 ses-M00 MCI 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M06 MCI 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M12 MCI 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M18 MCI 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M24 AD 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M30 89.18 F 2.0 24.0 1.0 -sub-CLNC0541 ses-M36 AD 89.18 F 2.0 24.0 1.0 -sub-CLNC0542 ses-M00 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M06 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M12 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M18 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M24 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M30 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M36 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M42 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M48 MCI 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M54 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M60 MCI 2.0 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M66 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M72 MCI 2.0 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M78 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M84 MCI 2.0 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M90 79.89 M 8.0 21.0 1.0 -sub-CLNC0542 ses-M96 MCI 2.0 79.89 M 8.0 21.0 1.0 -sub-CLNC0543 ses-M00 AD 3.0 65.8 M 2.0 21.0 0.0 -sub-CLNC0543 ses-M03 65.8 M 2.0 21.0 0.0 -sub-CLNC0543 ses-M06 AD 3.0 65.8 M 2.0 21.0 0.0 -sub-CLNC0543 ses-M12 AD 3.0 65.8 M 2.0 21.0 0.0 -sub-CLNC0543 ses-M18 65.8 M 2.0 21.0 0.0 -sub-CLNC0544 ses-M00 CN 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M06 CN 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M108 MCI 2.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M12 CN 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M120 MCI 2.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M18 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M24 CN 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M30 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M36 CN 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M42 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M48 CN 1.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M54 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M66 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M72 CN 1.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M78 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M84 CN 1.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M90 76.26 F 3.0 29.0 1.0 -sub-CLNC0544 ses-M96 4.0 76.26 F 3.0 29.0 1.0 -sub-CLNC0545 ses-M00 CN 1.0 62.5 M 2.0 29.0 0.5 -sub-CLNC0545 ses-M03 62.5 M 2.0 29.0 0.5 -sub-CLNC0545 ses-M06 CN 1.0 62.5 M 2.0 29.0 0.5 -sub-CLNC0546 ses-M00 MCI 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M06 MCI 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M12 MCI 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M120 MCI 2.0 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M18 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M24 CN 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M30 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M36 CN 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M72 CN 1.0 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M78 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M84 CN 1.0 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M90 68.85 F 3.0 28.0 0.5 -sub-CLNC0546 ses-M96 CN 1.0 68.85 F 3.0 28.0 0.5 -sub-CLNC0547 ses-M00 AD 3.0 88.19 M 6.0 25.0 3.0 -sub-CLNC0547 ses-M03 88.19 M 6.0 25.0 3.0 -sub-CLNC0547 ses-M06 AD 3.0 88.19 M 6.0 25.0 3.0 -sub-CLNC0547 ses-M12 AD 3.0 88.19 M 6.0 25.0 3.0 -sub-CLNC0548 ses-M00 CN 1.0 68.23 M 2.0 29.0 1.0 -sub-CLNC0548 ses-M03 68.23 M 2.0 29.0 1.0 -sub-CLNC0548 ses-M06 CN 1.0 68.23 M 2.0 29.0 1.0 -sub-CLNC0548 ses-M24 CN 1.0 68.23 M 2.0 29.0 1.0 -sub-CLNC0548 ses-M36 CN 1.0 68.23 M 2.0 29.0 1.0 -sub-CLNC0549 ses-M00 AD 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M06 AD 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M12 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M18 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M24 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M30 64.72 M 7.0 28.0 0.0 -sub-CLNC0549 ses-M36 64.72 M 7.0 28.0 0.0 -sub-CLNC0550 ses-M00 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M06 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M12 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M18 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M24 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M30 75.64 F 5.0 28.0 0.5 -sub-CLNC0550 ses-M36 MCI 75.64 F 5.0 28.0 0.5 -sub-CLNC0551 ses-M00 CN 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M06 CN 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M12 CN 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M18 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M24 CN 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M30 65.73 M 1.0 28.0 3.0 -sub-CLNC0551 ses-M36 65.73 M 1.0 28.0 3.0 -sub-CLNC0552 ses-M00 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M03 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M06 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M12 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M18 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M24 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M30 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M36 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M48 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0552 ses-M60 MCI 2.0 83.26 M 8.0 22.0 2.0 -sub-CLNC0553 ses-M00 MCI 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M06 MCI 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M12 MCI 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M18 MCI 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M24 MCI 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M30 68.74 F 7.0 28.0 0.5 -sub-CLNC0553 ses-M36 68.74 F 7.0 28.0 0.5 -sub-CLNC0554 ses-M00 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M06 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M12 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M18 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M24 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M30 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M36 AD 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M48 8.0 74.13 F 5.0 28.0 2.0 -sub-CLNC0554 ses-M54 74.13 F 5.0 28.0 2.0 -sub-CLNC0555 ses-M00 AD 69.45 F 2.0 29.0 2.0 -sub-CLNC0555 ses-M06 AD 69.45 F 2.0 29.0 2.0 -sub-CLNC0556 ses-M00 AD 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M06 AD 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M12 AD 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M18 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M24 AD 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M30 67.64 F 3.0 26.0 2.0 -sub-CLNC0556 ses-M36 67.64 F 3.0 26.0 2.0 -sub-CLNC0557 ses-M00 MCI 2.0 72.68 M 5.0 29.0 0.0 -sub-CLNC0557 ses-M03 72.68 M 5.0 29.0 0.0 -sub-CLNC0557 ses-M06 5.0 72.68 M 5.0 29.0 0.0 -sub-CLNC0557 ses-M12 AD 3.0 72.68 M 5.0 29.0 0.0 -sub-CLNC0557 ses-M24 AD 3.0 72.68 M 5.0 29.0 0.0 -sub-CLNC0557 ses-M36 72.68 M 5.0 29.0 0.0 -sub-CLNC0558 ses-M00 MCI 2.0 75.44 F 2.0 25.0 3.0 -sub-CLNC0558 ses-M03 75.44 F 2.0 25.0 3.0 -sub-CLNC0558 ses-M06 MCI 2.0 75.44 F 2.0 25.0 3.0 -sub-CLNC0558 ses-M12 MCI 2.0 75.44 F 2.0 25.0 3.0 -sub-CLNC0558 ses-M24 MCI 2.0 75.44 F 2.0 25.0 3.0 -sub-CLNC0558 ses-M36 MCI 2.0 75.44 F 2.0 25.0 3.0 -sub-CLNC0559 ses-M00 CN 1.0 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M06 CN 1.0 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M12 CN 1.0 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M18 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M24 CN 1.0 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M30 69.17 F 3.0 24.0 1.0 -sub-CLNC0559 ses-M48 CN 1.0 69.17 F 3.0 24.0 1.0 -sub-CLNC0560 ses-M00 MCI 2.0 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M03 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M06 MCI 2.0 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M12 MCI 2.0 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M18 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M24 MCI 2.0 77.33 F 7.0 29.0 0.0 -sub-CLNC0560 ses-M36 MCI 2.0 77.33 F 7.0 29.0 0.0 -sub-CLNC0561 ses-M00 AD 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M06 AD 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M12 AD 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M18 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M24 AD 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M30 77.82 M 8.0 27.0 1.0 -sub-CLNC0561 ses-M36 77.82 M 8.0 27.0 1.0 -sub-CLNC0562 ses-M00 MCI 2.0 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M03 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M06 MCI 2.0 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M12 MCI 2.0 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M18 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M24 MCI 2.0 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M36 5.0 75.65 F 4.0 21.0 2.0 -sub-CLNC0562 ses-M48 75.65 F 4.0 21.0 2.0 -sub-CLNC0563 ses-M00 MCI 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M06 MCI 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M12 MCI 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M18 MCI 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M24 MCI 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M30 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M36 AD 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M42 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M48 AD 77.52 F 1.0 24.0 2.0 -sub-CLNC0563 ses-M54 77.52 F 1.0 24.0 2.0 -sub-CLNC0564 ses-M00 MCI 2.0 78.34 F 5.0 28.0 1.0 -sub-CLNC0564 ses-M03 78.34 F 5.0 28.0 1.0 -sub-CLNC0564 ses-M06 MCI 2.0 78.34 F 5.0 28.0 1.0 -sub-CLNC0564 ses-M12 MCI 2.0 78.34 F 5.0 28.0 1.0 -sub-CLNC0564 ses-M18 78.34 F 5.0 28.0 1.0 -sub-CLNC0564 ses-M24 78.34 F 5.0 28.0 1.0 -sub-CLNC0565 ses-M00 AD 3.0 73.77 M 7.0 23.0 0.0 -sub-CLNC0565 ses-M03 73.77 M 7.0 23.0 0.0 -sub-CLNC0565 ses-M06 AD 3.0 73.77 M 7.0 23.0 0.0 -sub-CLNC0565 ses-M12 AD 3.0 73.77 M 7.0 23.0 0.0 -sub-CLNC0566 ses-M00 CN 1.0 63.48 M 9.0 25.0 1.0 -sub-CLNC0566 ses-M06 CN 1.0 63.48 M 9.0 25.0 1.0 -sub-CLNC0566 ses-M24 63.48 M 9.0 25.0 1.0 -sub-CLNC0567 ses-M00 CN 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M06 CN 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M102 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M108 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M12 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M120 CN 1.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M132 4.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M18 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M24 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M30 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M36 CN 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M60 CN 1.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M66 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M72 CN 1.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M78 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M84 4.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M90 72.72 F 7.0 29.0 1.0 -sub-CLNC0567 ses-M96 CN 1.0 72.72 F 7.0 29.0 1.0 -sub-CLNC0568 ses-M00 CN 1.0 66.81 M 4.0 27.0 1.0 -sub-CLNC0568 ses-M03 66.81 M 4.0 27.0 1.0 -sub-CLNC0568 ses-M06 CN 1.0 66.81 M 4.0 27.0 1.0 -sub-CLNC0568 ses-M12 CN 1.0 66.81 M 4.0 27.0 1.0 -sub-CLNC0568 ses-M36 CN 1.0 66.81 M 4.0 27.0 1.0 -sub-CLNC0569 ses-M00 CN 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M06 CN 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M12 CN 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M18 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M24 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M30 77.73 F 1.0 25.0 1.0 -sub-CLNC0569 ses-M36 77.73 F 1.0 25.0 1.0 -sub-CLNC0570 ses-M00 CN 1.0 73.62 F 8.0 26.0 2.0 -sub-CLNC0570 ses-M06 CN 1.0 73.62 F 8.0 26.0 2.0 -sub-CLNC0571 ses-M00 CN 1.0 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M03 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M06 CN 1.0 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M12 CN 1.0 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M18 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M24 CN 1.0 83.84 F 2.0 27.0 0.5 -sub-CLNC0571 ses-M48 CN 1.0 83.84 F 2.0 27.0 0.5 -sub-CLNC0572 ses-M00 CN 1.0 84.26 M 2.0 26.0 2.0 -sub-CLNC0572 ses-M03 84.26 M 2.0 26.0 2.0 -sub-CLNC0572 ses-M06 CN 1.0 84.26 M 2.0 26.0 2.0 -sub-CLNC0572 ses-M24 CN 1.0 84.26 M 2.0 26.0 2.0 -sub-CLNC0573 ses-M00 MCI 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M06 MCI 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M12 MCI 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M24 MCI 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M30 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M36 MCI 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M48 MCI 2.0 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M54 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M60 5.0 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M66 74.01 F 9.0 21.0 3.0 -sub-CLNC0573 ses-M72 5.0 74.01 F 9.0 21.0 3.0 -sub-CLNC0574 ses-M00 AD 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M06 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M12 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M18 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M24 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M30 77.85 F 5.0 21.0 0.0 -sub-CLNC0574 ses-M36 77.85 F 5.0 21.0 0.0 -sub-CLNC0575 ses-M00 MCI 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M06 MCI 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M12 MCI 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M18 MCI 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M24 AD 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M30 73.26 F 2.0 24.0 0.5 -sub-CLNC0575 ses-M36 AD 73.26 F 2.0 24.0 0.5 -sub-CLNC0576 ses-M00 MCI 2.0 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M03 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M06 5.0 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M12 AD 3.0 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M18 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M24 AD 3.0 74.85 M 6.0 29.0 0.0 -sub-CLNC0576 ses-M36 AD 3.0 74.85 M 6.0 29.0 0.0 -sub-CLNC0577 ses-M00 CN 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M06 CN 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M12 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M18 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M24 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M30 66.98 F 5.0 26.0 3.0 -sub-CLNC0577 ses-M36 66.98 F 5.0 26.0 3.0 -sub-CLNC0578 ses-M00 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M03 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M06 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M12 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M18 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M24 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M30 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M36 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M48 MCI 2.0 66.93 F 7.0 20.0 2.0 -sub-CLNC0578 ses-M60 66.93 F 7.0 20.0 2.0 -sub-CLNC0579 ses-M00 MCI 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M06 MCI 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M12 MCI 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M18 MCI 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M24 AD 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M30 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M36 AD 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M42 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M48 AD 3.0 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M60 AD 3.0 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M66 81.79 M 4.0 22.0 1.0 -sub-CLNC0579 ses-M72 AD 3.0 81.79 M 4.0 22.0 1.0 -sub-CLNC0580 ses-M00 AD 3.0 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M03 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M06 AD 3.0 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M12 AD 3.0 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M18 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M24 AD 3.0 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M30 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M36 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M42 70.66 F 4.0 26.0 2.0 -sub-CLNC0580 ses-M48 70.66 F 4.0 26.0 2.0 -sub-CLNC0581 ses-M00 MCI 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M06 MCI 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M12 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M18 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M24 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M30 64.97 M 1.0 20.0 0.5 -sub-CLNC0581 ses-M36 64.97 M 1.0 20.0 0.5 -sub-CLNC0582 ses-M00 MCI 2.0 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M03 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M06 5.0 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M12 AD 3.0 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M18 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M24 5.0 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M36 AD 3.0 62.53 M 3.0 29.0 1.0 -sub-CLNC0582 ses-M48 62.53 M 3.0 29.0 1.0 -sub-CLNC0583 ses-M00 AD 3.0 67.59 M 2.0 28.0 3.0 -sub-CLNC0583 ses-M03 67.59 M 2.0 28.0 3.0 -sub-CLNC0583 ses-M06 AD 3.0 67.59 M 2.0 28.0 3.0 -sub-CLNC0583 ses-M12 AD 3.0 67.59 M 2.0 28.0 3.0 -sub-CLNC0584 ses-M00 MCI 2.0 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M03 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M06 MCI 2.0 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M12 MCI 2.0 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M18 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M24 5.0 59.44 M 1.0 24.0 3.0 -sub-CLNC0584 ses-M30 59.44 M 1.0 24.0 3.0 -sub-CLNC0585 ses-M00 MCI 2.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M03 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M06 MCI 2.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M12 7.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M18 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M24 CN 1.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M36 CN 1.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0585 ses-M48 CN 1.0 76.13 F 6.0 28.0 0.0 -sub-CLNC0586 ses-M00 MCI 2.0 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M03 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M06 MCI 2.0 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M12 MCI 2.0 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M18 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M24 MCI 2.0 63.33 F 3.0 25.0 2.0 -sub-CLNC0586 ses-M36 MCI 2.0 63.33 F 3.0 25.0 2.0 -sub-CLNC0587 ses-M00 AD 3.0 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M03 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M06 AD 3.0 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M12 AD 3.0 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M18 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M24 AD 3.0 66.83 F 6.0 21.0 1.0 -sub-CLNC0587 ses-M30 66.83 F 6.0 21.0 1.0 -sub-CLNC0588 ses-M00 CN 1.0 77.13 F 2.0 27.0 2.0 -sub-CLNC0588 ses-M06 CN 1.0 77.13 F 2.0 27.0 2.0 -sub-CLNC0588 ses-M24 CN 1.0 77.13 F 2.0 27.0 2.0 -sub-CLNC0589 ses-M00 AD 3.0 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M03 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M06 AD 3.0 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M12 AD 3.0 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M18 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M24 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M30 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M36 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M42 78.03 M 2.0 27.0 0.0 -sub-CLNC0589 ses-M48 78.03 M 2.0 27.0 0.0 -sub-CLNC0590 ses-M00 CN 1.0 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M03 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M06 CN 1.0 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M12 CN 1.0 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M18 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M24 CN 1.0 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M30 74.41 M 5.0 25.0 0.0 -sub-CLNC0590 ses-M48 74.41 M 5.0 25.0 0.0 -sub-CLNC0591 ses-M00 CN 1.0 76.02 M 2.0 29.0 3.0 -sub-CLNC0591 ses-M06 CN 1.0 76.02 M 2.0 29.0 3.0 -sub-CLNC0591 ses-M12 CN 1.0 76.02 M 2.0 29.0 3.0 -sub-CLNC0591 ses-M24 CN 1.0 76.02 M 2.0 29.0 3.0 -sub-CLNC0592 ses-M00 MCI 2.0 65.74 F 6.0 22.0 0.5 -sub-CLNC0592 ses-M03 65.74 F 6.0 22.0 0.5 -sub-CLNC0592 ses-M06 MCI 2.0 65.74 F 6.0 22.0 0.5 -sub-CLNC0593 ses-M00 MCI 2.0 61.71 M 5.0 28.0 0.5 -sub-CLNC0593 ses-M24 MCI 2.0 61.71 M 5.0 28.0 0.5 -sub-CLNC0593 ses-M36 MCI 2.0 61.71 M 5.0 28.0 0.5 -sub-CLNC0593 ses-M48 MCI 2.0 61.71 M 5.0 28.0 0.5 -sub-CLNC0594 ses-M00 MCI 2.0 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M03 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M06 5.0 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M12 AD 3.0 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M18 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M24 AD 3.0 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M36 74.92 M 6.0 25.0 1.0 -sub-CLNC0594 ses-M48 74.92 M 6.0 25.0 1.0 -sub-CLNC0595 ses-M00 AD 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M06 AD 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M12 AD 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M18 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M24 AD 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M30 75.7 M 8.0 22.0 3.0 -sub-CLNC0595 ses-M36 75.7 M 8.0 22.0 3.0 -sub-CLNC0596 ses-M00 CN 1.0 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M03 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M06 CN 1.0 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M12 CN 1.0 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M18 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M24 CN 1.0 68.12 F 8.0 29.0 1.0 -sub-CLNC0596 ses-M48 CN 1.0 68.12 F 8.0 29.0 1.0 -sub-CLNC0597 ses-M00 CN 1.0 71.05 M 8.0 22.0 0.5 -sub-CLNC0597 ses-M03 71.05 M 8.0 22.0 0.5 -sub-CLNC0597 ses-M06 4.0 71.05 M 8.0 22.0 0.5 -sub-CLNC0597 ses-M12 MCI 2.0 71.05 M 8.0 22.0 0.5 -sub-CLNC0598 ses-M00 MCI 2.0 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M03 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M06 MCI 2.0 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M12 MCI 2.0 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M18 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M24 MCI 2.0 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M36 MCI 2.0 68.02 M 6.0 22.0 3.0 -sub-CLNC0598 ses-M48 68.02 M 6.0 22.0 3.0 -sub-CLNC0599 ses-M00 CN 1.0 65.67 M 4.0 26.0 0.0 -sub-CLNC0599 ses-M06 CN 1.0 65.67 M 4.0 26.0 0.0 -sub-CLNC0600 ses-M00 MCI 2.0 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M03 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M06 MCI 2.0 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M12 MCI 2.0 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M18 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M24 MCI 2.0 62.43 F 8.0 28.0 0.5 -sub-CLNC0600 ses-M36 MCI 2.0 62.43 F 8.0 28.0 0.5 -sub-CLNC0601 ses-M00 MCI 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M06 MCI 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M108 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M12 CN 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M18 CN 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M24 CN 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M30 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M36 CN 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M42 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M48 CN 1.0 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M54 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M60 CN 1.0 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M66 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M72 CN 1.0 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M78 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M84 CN 1.0 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M90 67.25 F 5.0 28.0 2.0 -sub-CLNC0601 ses-M96 4.0 67.25 F 5.0 28.0 2.0 -sub-CLNC0602 ses-M00 CN 1.0 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M03 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M06 CN 1.0 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M12 CN 1.0 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M18 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M24 CN 1.0 73.9 M 5.0 26.0 0.0 -sub-CLNC0602 ses-M36 CN 1.0 73.9 M 5.0 26.0 0.0 -sub-CLNC0603 ses-M00 MCI 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M06 MCI 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M12 AD 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M18 AD 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M24 AD 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M30 77.91 F 2.0 26.0 2.0 -sub-CLNC0603 ses-M36 AD 77.91 F 2.0 26.0 2.0 -sub-CLNC0604 ses-M00 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M06 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M12 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M18 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M24 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M30 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M36 MCI 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M48 MCI 2.0 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M54 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M60 MCI 2.0 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M66 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M72 MCI 2.0 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M78 67.42 F 1.0 21.0 0.0 -sub-CLNC0604 ses-M84 MCI 2.0 67.42 F 1.0 21.0 0.0 -sub-CLNC0605 ses-M00 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M06 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M108 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M12 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M120 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M18 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M24 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M30 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M36 MCI 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M42 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M48 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M54 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M72 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M78 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M84 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M90 78.85 F 9.0 27.0 0.0 -sub-CLNC0605 ses-M96 MCI 2.0 78.85 F 9.0 27.0 0.0 -sub-CLNC0606 ses-M00 AD 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M06 MCI 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M12 MCI 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M18 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M24 MCI 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M30 76.58 F 7.0 26.0 2.0 -sub-CLNC0606 ses-M36 76.58 F 7.0 26.0 2.0 -sub-CLNC0607 ses-M00 CN 1.0 67.81 F 5.0 26.0 0.0 -sub-CLNC0607 ses-M03 67.81 F 5.0 26.0 0.0 -sub-CLNC0607 ses-M06 CN 1.0 67.81 F 5.0 26.0 0.0 -sub-CLNC0607 ses-M12 CN 1.0 67.81 F 5.0 26.0 0.0 -sub-CLNC0607 ses-M30 67.81 F 5.0 26.0 0.0 -sub-CLNC0608 ses-M00 MCI 2.0 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M03 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M06 MCI 2.0 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M12 MCI 2.0 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M18 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M24 MCI 2.0 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M30 80.74 M 4.0 20.0 2.0 -sub-CLNC0608 ses-M48 MCI 2.0 80.74 M 4.0 20.0 2.0 -sub-CLNC0609 ses-M00 MCI 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M06 MCI 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M12 MCI 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M18 MCI 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M24 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M30 72.59 M 7.0 27.0 1.0 -sub-CLNC0609 ses-M36 MCI 72.59 M 7.0 27.0 1.0 -sub-CLNC0610 ses-M00 CN 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M06 CN 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M12 CN 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M120 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M18 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M24 CN 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M30 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M36 CN 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M42 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M48 CN 1.0 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M54 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M60 CN 1.0 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M66 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M72 CN 1.0 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M78 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M84 CN 1.0 68.23 F 6.0 28.0 0.0 -sub-CLNC0610 ses-M96 CN 1.0 68.23 F 6.0 28.0 0.0 -sub-CLNC0611 ses-M00 CN 1.0 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M03 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M06 CN 1.0 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M12 4.0 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M18 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M24 MCI 2.0 59.16 M 1.0 29.0 0.0 -sub-CLNC0611 ses-M36 MCI 2.0 59.16 M 1.0 29.0 0.0 -sub-CLNC0612 ses-M00 CN 1.0 71.61 M 7.0 21.0 1.0 -sub-CLNC0612 ses-M12 CN 1.0 71.61 M 7.0 21.0 1.0 -sub-CLNC0612 ses-M18 71.61 M 7.0 21.0 1.0 -sub-CLNC0612 ses-M24 CN 1.0 71.61 M 7.0 21.0 1.0 -sub-CLNC0612 ses-M30 71.61 M 7.0 21.0 1.0 -sub-CLNC0612 ses-M36 CN 1.0 71.61 M 7.0 21.0 1.0 -sub-CLNC0613 ses-M00 MCI 2.0 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M03 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M06 MCI 2.0 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M12 MCI 2.0 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M24 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M36 66.51 F 7.0 25.0 2.0 -sub-CLNC0613 ses-M48 66.51 F 7.0 25.0 2.0 -sub-CLNC0614 ses-M00 CN 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M06 CN 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M12 CN 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M18 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M24 CN 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M30 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M36 CN 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M48 CN 1.0 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M54 79.86 M 3.0 27.0 0.5 -sub-CLNC0614 ses-M60 CN 1.0 79.86 M 3.0 27.0 0.5 -sub-CLNC0615 ses-M00 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M03 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M06 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M12 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M18 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M24 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M30 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M36 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0615 ses-M48 MCI 2.0 76.78 M 6.0 20.0 3.0 -sub-CLNC0616 ses-M00 MCI 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M06 MCI 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M12 MCI 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M18 AD 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M24 AD 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M30 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M36 AD 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M42 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M48 AD 3.0 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M54 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M60 AD 3.0 69.29 F 3.0 23.0 3.0 -sub-CLNC0616 ses-M72 AD 3.0 69.29 F 3.0 23.0 3.0 -sub-CLNC0617 ses-M00 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M03 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M06 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M12 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M18 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M24 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M30 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M36 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M48 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0617 ses-M60 MCI 2.0 74.22 M 1.0 28.0 2.0 -sub-CLNC0618 ses-M00 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M06 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M12 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M18 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M24 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M30 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M36 MCI 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M42 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M48 MCI 2.0 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M54 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M60 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M66 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M72 77.48 M 4.0 27.0 0.0 -sub-CLNC0618 ses-M78 77.48 M 4.0 27.0 0.0 -sub-CLNC0619 ses-M00 CN 1.0 93.05 M 4.0 20.0 0.5 -sub-CLNC0619 ses-M03 93.05 M 4.0 20.0 0.5 -sub-CLNC0619 ses-M12 CN 1.0 93.05 M 4.0 20.0 0.5 -sub-CLNC0619 ses-M24 CN 1.0 93.05 M 4.0 20.0 0.5 -sub-CLNC0620 ses-M00 AD 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M06 AD 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M12 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M18 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M24 AD 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M30 71.5 F 4.0 23.0 0.5 -sub-CLNC0620 ses-M36 71.5 F 4.0 23.0 0.5 -sub-CLNC0621 ses-M00 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M06 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M12 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M18 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M24 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M30 71.13 F 6.0 21.0 0.5 -sub-CLNC0621 ses-M36 MCI 71.13 F 6.0 21.0 0.5 -sub-CLNC0622 ses-M00 CN 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M06 CN 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M12 CN 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M18 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M24 CN 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M30 74.6 F 9.0 20.0 0.5 -sub-CLNC0622 ses-M36 CN 74.6 F 9.0 20.0 0.5 -sub-CLNC0623 ses-M00 AD 3.0 85.86 F 9.0 28.0 1.0 -sub-CLNC0623 ses-M03 85.86 F 9.0 28.0 1.0 -sub-CLNC0623 ses-M06 AD 3.0 85.86 F 9.0 28.0 1.0 -sub-CLNC0623 ses-M12 AD 3.0 85.86 F 9.0 28.0 1.0 -sub-CLNC0623 ses-M18 85.86 F 9.0 28.0 1.0 -sub-CLNC0623 ses-M24 AD 3.0 85.86 F 9.0 28.0 1.0 -sub-CLNC0624 ses-M00 AD 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M06 AD 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M12 AD 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M18 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M24 AD 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M30 73.25 M 5.0 23.0 0.5 -sub-CLNC0624 ses-M36 73.25 M 5.0 23.0 0.5 -sub-CLNC0625 ses-M00 CN 1.0 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M03 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M06 CN 1.0 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M12 4.0 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M18 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M24 5.0 81.26 F 7.0 23.0 1.0 -sub-CLNC0625 ses-M36 AD 3.0 81.26 F 7.0 23.0 1.0 -sub-CLNC0626 ses-M00 CN 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M06 CN 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M108 AD 3.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M12 CN 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M120 AD 3.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M18 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M24 CN 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M30 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M48 MCI 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M60 MCI 2.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M66 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M72 5.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M78 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M84 AD 3.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0626 ses-M96 AD 3.0 67.46 F 9.0 20.0 1.0 -sub-CLNC0627 ses-M00 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M03 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M06 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M12 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M24 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M36 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0627 ses-M48 MCI 2.0 61.59 M 3.0 22.0 3.0 -sub-CLNC0628 ses-M00 AD 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M06 AD 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M12 AD 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M18 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M24 AD 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M30 62.67 F 3.0 27.0 0.5 -sub-CLNC0628 ses-M36 AD 62.67 F 3.0 27.0 0.5 -sub-CLNC0629 ses-M00 AD 3.0 80.46 M 4.0 28.0 2.0 -sub-CLNC0629 ses-M03 80.46 M 4.0 28.0 2.0 -sub-CLNC0629 ses-M06 AD 3.0 80.46 M 4.0 28.0 2.0 -sub-CLNC0629 ses-M12 AD 3.0 80.46 M 4.0 28.0 2.0 -sub-CLNC0629 ses-M30 80.46 M 4.0 28.0 2.0 -sub-CLNC0629 ses-M42 80.46 M 4.0 28.0 2.0 -sub-CLNC0630 ses-M00 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M06 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M12 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M18 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M24 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M30 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M36 MCI 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M42 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M48 MCI 2.0 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M54 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M60 MCI 2.0 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M66 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M72 MCI 2.0 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M78 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M84 MCI 2.0 76.3 M 2.0 27.0 0.0 -sub-CLNC0630 ses-M96 MCI 2.0 76.3 M 2.0 27.0 0.0 -sub-CLNC0631 ses-M00 CN 1.0 74.13 F 3.0 24.0 2.0 -sub-CLNC0631 ses-M03 74.13 F 3.0 24.0 2.0 -sub-CLNC0631 ses-M06 CN 1.0 74.13 F 3.0 24.0 2.0 -sub-CLNC0631 ses-M12 CN 1.0 74.13 F 3.0 24.0 2.0 -sub-CLNC0631 ses-M18 74.13 F 3.0 24.0 2.0 -sub-CLNC0631 ses-M24 CN 1.0 74.13 F 3.0 24.0 2.0 -sub-CLNC0632 ses-M00 MCI 2.0 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M03 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M06 MCI 2.0 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M12 MCI 2.0 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M18 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M24 MCI 2.0 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M36 MCI 2.0 76.72 F 3.0 23.0 2.0 -sub-CLNC0632 ses-M48 76.72 F 3.0 23.0 2.0 -sub-CLNC0633 ses-M00 AD 3.0 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M03 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M06 AD 3.0 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M12 AD 3.0 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M18 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M24 AD 3.0 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M30 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M36 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M42 78.91 F 3.0 24.0 0.0 -sub-CLNC0633 ses-M48 78.91 F 3.0 24.0 0.0 -sub-CLNC0634 ses-M00 MCI 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M06 MCI 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M12 MCI 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M18 MCI 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M30 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M36 MCI 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M42 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M48 5.0 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M54 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M60 AD 3.0 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M66 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M72 AD 3.0 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M78 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M84 AD 3.0 74.08 M 9.0 22.0 0.5 -sub-CLNC0634 ses-M96 AD 3.0 74.08 M 9.0 22.0 0.5 -sub-CLNC0635 ses-M00 MCI 2.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M03 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M06 MCI 2.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M12 MCI 2.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M18 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M24 5.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M36 5.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0635 ses-M48 AD 3.0 77.07 F 4.0 27.0 0.5 -sub-CLNC0636 ses-M00 AD 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M06 AD 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M12 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M18 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M24 AD 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M30 69.62 F 5.0 29.0 1.0 -sub-CLNC0636 ses-M36 69.62 F 5.0 29.0 1.0 -sub-CLNC0637 ses-M00 MCI 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M06 MCI 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M108 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M12 MCI 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M120 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M18 MCI 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M24 MCI 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M30 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M36 AD 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M42 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M48 AD 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M54 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M60 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M66 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M72 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M78 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M84 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M90 85.3 F 6.0 27.0 0.5 -sub-CLNC0637 ses-M96 AD 3.0 85.3 F 6.0 27.0 0.5 -sub-CLNC0638 ses-M00 MCI 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M06 MCI 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M12 AD 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M18 AD 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M24 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M30 68.35 F 8.0 26.0 1.0 -sub-CLNC0638 ses-M36 68.35 F 8.0 26.0 1.0 -sub-CLNC0639 ses-M00 AD 79.91 F 9.0 29.0 2.0 -sub-CLNC0639 ses-M06 AD 79.91 F 9.0 29.0 2.0 -sub-CLNC0639 ses-M12 AD 79.91 F 9.0 29.0 2.0 -sub-CLNC0639 ses-M18 79.91 F 9.0 29.0 2.0 -sub-CLNC0639 ses-M24 AD 79.91 F 9.0 29.0 2.0 -sub-CLNC0640 ses-M00 CN 1.0 74.59 F 7.0 24.0 3.0 -sub-CLNC0640 ses-M03 74.59 F 7.0 24.0 3.0 -sub-CLNC0640 ses-M06 CN 1.0 74.59 F 7.0 24.0 3.0 -sub-CLNC0640 ses-M12 CN 1.0 74.59 F 7.0 24.0 3.0 -sub-CLNC0640 ses-M18 74.59 F 7.0 24.0 3.0 -sub-CLNC0640 ses-M24 CN 1.0 74.59 F 7.0 24.0 3.0 -sub-CLNC0641 ses-M00 MCI 2.0 66.47 M 5.0 29.0 0.0 -sub-CLNC0641 ses-M03 66.47 M 5.0 29.0 0.0 -sub-CLNC0641 ses-M06 MCI 2.0 66.47 M 5.0 29.0 0.0 -sub-CLNC0641 ses-M12 5.0 66.47 M 5.0 29.0 0.0 -sub-CLNC0642 ses-M00 CN 1.0 79.93 F 4.0 28.0 0.5 -sub-CLNC0642 ses-M03 79.93 F 4.0 28.0 0.5 -sub-CLNC0642 ses-M06 CN 1.0 79.93 F 4.0 28.0 0.5 -sub-CLNC0642 ses-M12 CN 1.0 79.93 F 4.0 28.0 0.5 -sub-CLNC0642 ses-M18 79.93 F 4.0 28.0 0.5 -sub-CLNC0642 ses-M24 CN 1.0 79.93 F 4.0 28.0 0.5 -sub-CLNC0643 ses-M00 MCI 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M06 MCI 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M12 MCI 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M18 MCI 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M24 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M30 72.26 M 2.0 29.0 2.0 -sub-CLNC0643 ses-M36 72.26 M 2.0 29.0 2.0 -sub-CLNC0644 ses-M00 MCI 2.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M03 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M06 MCI 2.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M12 MCI 2.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M18 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M24 CN 1.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M36 MCI 2.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0644 ses-M48 MCI 2.0 69.97 F 4.0 23.0 1.0 -sub-CLNC0645 ses-M00 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M06 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M108 AD 3.0 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M12 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M120 AD 3.0 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M18 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M24 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M30 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M36 MCI 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M72 AD 3.0 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M78 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M84 AD 3.0 76.0 F 3.0 24.0 0.0 -sub-CLNC0645 ses-M96 AD 3.0 76.0 F 3.0 24.0 0.0 -sub-CLNC0646 ses-M00 AD 3.0 74.85 M 7.0 28.0 1.0 -sub-CLNC0646 ses-M03 74.85 M 7.0 28.0 1.0 -sub-CLNC0646 ses-M06 AD 3.0 74.85 M 7.0 28.0 1.0 -sub-CLNC0646 ses-M12 AD 3.0 74.85 M 7.0 28.0 1.0 -sub-CLNC0647 ses-M00 MCI 2.0 85.41 F 1.0 20.0 3.0 -sub-CLNC0647 ses-M03 85.41 F 1.0 20.0 3.0 -sub-CLNC0647 ses-M06 MCI 2.0 85.41 F 1.0 20.0 3.0 -sub-CLNC0647 ses-M12 MCI 2.0 85.41 F 1.0 20.0 3.0 -sub-CLNC0648 ses-M00 CN 1.0 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M03 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M06 CN 1.0 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M12 CN 1.0 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M18 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M24 CN 1.0 76.49 M 5.0 20.0 0.5 -sub-CLNC0648 ses-M48 CN 1.0 76.49 M 5.0 20.0 0.5 -sub-CLNC0649 ses-M00 CN 1.0 83.72 F 7.0 24.0 0.0 -sub-CLNC0649 ses-M03 83.72 F 7.0 24.0 0.0 -sub-CLNC0649 ses-M06 CN 1.0 83.72 F 7.0 24.0 0.0 -sub-CLNC0649 ses-M12 CN 1.0 83.72 F 7.0 24.0 0.0 -sub-CLNC0649 ses-M24 CN 1.0 83.72 F 7.0 24.0 0.0 -sub-CLNC0650 ses-M00 MCI 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M06 MCI 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M12 MCI 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M18 MCI 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M24 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M30 72.81 F 2.0 27.0 0.5 -sub-CLNC0650 ses-M36 MCI 72.81 F 2.0 27.0 0.5 -sub-CLNC0651 ses-M00 MCI 2.0 82.17 M 8.0 21.0 0.0 -sub-CLNC0651 ses-M06 MCI 2.0 82.17 M 8.0 21.0 0.0 -sub-CLNC0651 ses-M12 MCI 2.0 82.17 M 8.0 21.0 0.0 -sub-CLNC0652 ses-M00 MCI 2.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M03 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M06 MCI 2.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M12 MCI 2.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M18 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M24 MCI 2.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M30 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M36 5.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0652 ses-M48 AD 3.0 78.96 F 6.0 20.0 0.5 -sub-CLNC0653 ses-M00 AD 3.0 67.95 F 5.0 23.0 2.0 -sub-CLNC0653 ses-M03 67.95 F 5.0 23.0 2.0 -sub-CLNC0653 ses-M06 AD 3.0 67.95 F 5.0 23.0 2.0 -sub-CLNC0653 ses-M12 AD 3.0 67.95 F 5.0 23.0 2.0 -sub-CLNC0654 ses-M00 CN 1.0 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M03 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M06 CN 1.0 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M12 CN 1.0 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M18 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M24 CN 1.0 67.85 F 4.0 24.0 3.0 -sub-CLNC0654 ses-M48 CN 1.0 67.85 F 4.0 24.0 3.0 -sub-CLNC0655 ses-M00 AD 3.0 73.3 M 6.0 26.0 3.0 -sub-CLNC0655 ses-M03 73.3 M 6.0 26.0 3.0 -sub-CLNC0655 ses-M06 AD 3.0 73.3 M 6.0 26.0 3.0 -sub-CLNC0655 ses-M12 AD 3.0 73.3 M 6.0 26.0 3.0 -sub-CLNC0656 ses-M00 MCI 2.0 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M03 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M06 MCI 2.0 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M12 MCI 2.0 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M18 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M24 MCI 2.0 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M36 MCI 2.0 71.86 M 7.0 29.0 0.0 -sub-CLNC0656 ses-M48 71.86 M 7.0 29.0 0.0 -sub-CLNC0657 ses-M00 MCI 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M06 CN 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M12 CN 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M18 CN 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M24 CN 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M30 69.86 F 7.0 25.0 1.0 -sub-CLNC0657 ses-M36 CN 69.86 F 7.0 25.0 1.0 -sub-CLNC0658 ses-M00 CN 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M06 CN 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M102 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M12 CN 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M120 CN 1.0 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M18 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M24 CN 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M30 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M36 CN 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M60 CN 1.0 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M66 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M72 CN 1.0 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M78 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M84 CN 1.0 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M90 79.7 M 2.0 25.0 2.0 -sub-CLNC0658 ses-M96 CN 1.0 79.7 M 2.0 25.0 2.0 -sub-CLNC0659 ses-M00 AD 3.0 72.8 M 6.0 22.0 3.0 -sub-CLNC0659 ses-M03 72.8 M 6.0 22.0 3.0 -sub-CLNC0659 ses-M06 AD 3.0 72.8 M 6.0 22.0 3.0 -sub-CLNC0659 ses-M12 AD 3.0 72.8 M 6.0 22.0 3.0 -sub-CLNC0660 ses-M00 MCI 2.0 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M03 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M06 MCI 2.0 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M12 MCI 2.0 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M18 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M24 66.57 M 6.0 22.0 1.0 -sub-CLNC0660 ses-M36 66.57 M 6.0 22.0 1.0 -sub-CLNC0661 ses-M00 MCI 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M06 AD 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M12 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M18 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M24 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M30 61.97 M 8.0 23.0 1.0 -sub-CLNC0661 ses-M36 61.97 M 8.0 23.0 1.0 -sub-CLNC0662 ses-M00 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M06 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M108 CN 1.0 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M12 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M18 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M24 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M30 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M36 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M42 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M48 CN 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M54 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M60 CN 1.0 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M66 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M72 CN 1.0 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M78 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M84 CN 1.0 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M90 73.23 M 2.0 23.0 0.0 -sub-CLNC0662 ses-M96 CN 1.0 73.23 M 2.0 23.0 0.0 -sub-CLNC0663 ses-M00 AD 3.0 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M03 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M06 AD 3.0 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M12 AD 3.0 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M18 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M24 AD 3.0 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M30 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M36 81.04 F 6.0 20.0 2.0 -sub-CLNC0663 ses-M42 81.04 F 6.0 20.0 2.0 -sub-CLNC0664 ses-M00 CN 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M06 CN 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M108 CN 1.0 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M12 CN 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M18 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M24 CN 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M30 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M36 CN 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M48 CN 1.0 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M54 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M60 CN 1.0 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M66 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M72 CN 1.0 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M78 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M84 CN 1.0 74.13 M 9.0 21.0 1.0 -sub-CLNC0664 ses-M90 74.13 M 9.0 21.0 1.0 -sub-CLNC0665 ses-M00 CN 1.0 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M03 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M06 CN 1.0 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M12 CN 1.0 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M18 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M24 CN 1.0 76.07 F 1.0 22.0 2.0 -sub-CLNC0665 ses-M48 CN 1.0 76.07 F 1.0 22.0 2.0 -sub-CLNC0666 ses-M00 CN 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M06 CN 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M12 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M18 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M24 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M30 72.76 F 4.0 25.0 0.5 -sub-CLNC0666 ses-M36 72.76 F 4.0 25.0 0.5 -sub-CLNC0667 ses-M00 AD 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M06 AD 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M12 AD 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M18 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M24 AD 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M30 69.83 F 3.0 22.0 1.0 -sub-CLNC0667 ses-M36 69.83 F 3.0 22.0 1.0 -sub-CLNC0668 ses-M00 CN 1.0 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M03 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M06 CN 1.0 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M12 CN 1.0 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M18 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M24 CN 1.0 79.32 M 5.0 23.0 1.0 -sub-CLNC0668 ses-M48 CN 1.0 79.32 M 5.0 23.0 1.0 -sub-CLNC0669 ses-M00 CN 1.0 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M03 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M06 CN 1.0 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M12 CN 1.0 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M18 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M24 CN 1.0 78.85 F 5.0 20.0 0.5 -sub-CLNC0669 ses-M48 CN 1.0 78.85 F 5.0 20.0 0.5 -sub-CLNC0670 ses-M00 MCI 2.0 93.01 M 4.0 20.0 0.5 -sub-CLNC0670 ses-M06 MCI 2.0 93.01 M 4.0 20.0 0.5 -sub-CLNC0670 ses-M12 MCI 2.0 93.01 M 4.0 20.0 0.5 -sub-CLNC0670 ses-M18 93.01 M 4.0 20.0 0.5 -sub-CLNC0670 ses-M24 MCI 2.0 93.01 M 4.0 20.0 0.5 -sub-CLNC0671 ses-M00 CN 1.0 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M03 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M06 CN 1.0 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M12 CN 1.0 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M18 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M24 CN 1.0 77.05 M 8.0 24.0 0.5 -sub-CLNC0671 ses-M48 CN 1.0 77.05 M 8.0 24.0 0.5 -sub-CLNC0672 ses-M00 CN 1.0 75.73 F 3.0 21.0 1.0 -sub-CLNC0672 ses-M03 75.73 F 3.0 21.0 1.0 -sub-CLNC0672 ses-M06 CN 1.0 75.73 F 3.0 21.0 1.0 -sub-CLNC0672 ses-M12 CN 1.0 75.73 F 3.0 21.0 1.0 -sub-CLNC0672 ses-M18 75.73 F 3.0 21.0 1.0 -sub-CLNC0672 ses-M24 CN 1.0 75.73 F 3.0 21.0 1.0 -sub-CLNC0673 ses-M00 AD 3.0 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M03 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M06 AD 3.0 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M12 AD 3.0 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M18 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M24 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M36 67.54 F 8.0 22.0 0.5 -sub-CLNC0673 ses-M42 67.54 F 8.0 22.0 0.5 -sub-CLNC0674 ses-M00 CN 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M06 CN 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M12 CN 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M18 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M24 CN 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M30 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M36 CN 83.73 F 5.0 21.0 2.0 -sub-CLNC0674 ses-M42 83.73 F 5.0 21.0 2.0 -sub-CLNC0675 ses-M00 MCI 2.0 68.58 F 5.0 27.0 1.0 -sub-CLNC0676 ses-M00 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M03 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M06 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M12 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M18 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M24 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M36 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0676 ses-M48 MCI 2.0 65.25 M 7.0 25.0 2.0 -sub-CLNC0677 ses-M00 AD 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M06 AD 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M12 AD 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M18 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M24 AD 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M30 73.03 M 2.0 29.0 3.0 -sub-CLNC0677 ses-M36 73.03 M 2.0 29.0 3.0 -sub-CLNC0678 ses-M00 CN 1.0 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M03 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M06 CN 1.0 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M12 CN 1.0 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M18 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M24 CN 1.0 60.74 M 8.0 20.0 2.0 -sub-CLNC0678 ses-M48 CN 1.0 60.74 M 8.0 20.0 2.0 -sub-CLNC0679 ses-M00 CN 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M06 CN 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M12 CN 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M18 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M24 MCI 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M30 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M36 MCI 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M42 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M48 MCI 2.0 63.49 M 3.0 24.0 3.0 -sub-CLNC0679 ses-M54 63.49 M 3.0 24.0 3.0 -sub-CLNC0680 ses-M00 CN 1.0 77.95 F 2.0 27.0 2.0 -sub-CLNC0680 ses-M03 77.95 F 2.0 27.0 2.0 -sub-CLNC0680 ses-M06 CN 1.0 77.95 F 2.0 27.0 2.0 -sub-CLNC0680 ses-M24 CN 1.0 77.95 F 2.0 27.0 2.0 -sub-CLNC0681 ses-M00 MCI 2.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M03 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M06 MCI 2.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M12 7.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M18 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M24 CN 1.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M30 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M36 CN 1.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M48 CN 1.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0681 ses-M60 CN 1.0 64.43 F 7.0 28.0 0.5 -sub-CLNC0682 ses-M00 MCI 2.0 87.52 M 8.0 26.0 0.0 -sub-CLNC0682 ses-M03 87.52 M 8.0 26.0 0.0 -sub-CLNC0682 ses-M12 MCI 2.0 87.52 M 8.0 26.0 0.0 -sub-CLNC0682 ses-M24 MCI 2.0 87.52 M 8.0 26.0 0.0 -sub-CLNC0683 ses-M00 CN 1.0 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M03 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M06 CN 1.0 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M12 CN 1.0 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M18 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M24 CN 1.0 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M30 71.25 F 2.0 23.0 1.0 -sub-CLNC0683 ses-M48 CN 1.0 71.25 F 2.0 23.0 1.0 -sub-CLNC0684 ses-M00 AD 3.0 70.14 M 9.0 24.0 2.0 -sub-CLNC0684 ses-M03 70.14 M 9.0 24.0 2.0 -sub-CLNC0684 ses-M06 AD 3.0 70.14 M 9.0 24.0 2.0 -sub-CLNC0684 ses-M12 AD 3.0 70.14 M 9.0 24.0 2.0 -sub-CLNC0685 ses-M00 MCI 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M06 MCI 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M12 MCI 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M18 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M24 AD 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M30 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M36 AD 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M42 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M48 AD 3.0 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M60 AD 3.0 70.74 M 6.0 23.0 2.0 -sub-CLNC0685 ses-M72 AD 3.0 70.74 M 6.0 23.0 2.0 -sub-CLNC0686 ses-M00 MCI 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M06 MCI 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M12 MCI 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M18 MCI 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M24 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M30 72.5 F 4.0 23.0 1.0 -sub-CLNC0686 ses-M36 72.5 F 4.0 23.0 1.0 -sub-CLNC0687 ses-M00 MCI 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M06 MCI 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M12 MCI 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M18 MCI 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M24 MCI 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M30 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M36 AD 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M42 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M48 AD 3.0 72.56 F 3.0 21.0 1.0 -sub-CLNC0687 ses-M54 72.56 F 3.0 21.0 1.0 -sub-CLNC0688 ses-M00 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M06 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M12 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M18 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M24 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M30 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M36 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M42 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M48 MCI 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M54 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M60 MCI 2.0 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M66 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M72 MCI 2.0 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M90 81.34 M 1.0 26.0 0.5 -sub-CLNC0688 ses-M96 81.34 M 1.0 26.0 0.5 -sub-CLNC0689 ses-M00 CN 1.0 66.93 M 8.0 25.0 3.0 -sub-CLNC0689 ses-M06 CN 1.0 66.93 M 8.0 25.0 3.0 -sub-CLNC0689 ses-M24 CN 1.0 66.93 M 8.0 25.0 3.0 -sub-CLNC0690 ses-M00 CN 1.0 81.55 M 7.0 20.0 1.0 -sub-CLNC0690 ses-M03 81.55 M 7.0 20.0 1.0 -sub-CLNC0690 ses-M06 CN 1.0 81.55 M 7.0 20.0 1.0 -sub-CLNC0690 ses-M12 CN 1.0 81.55 M 7.0 20.0 1.0 -sub-CLNC0691 ses-M00 AD 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M06 AD 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M12 AD 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M18 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M24 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M30 76.45 M 8.0 27.0 1.0 -sub-CLNC0691 ses-M36 76.45 M 8.0 27.0 1.0 -sub-CLNC0692 ses-M00 MCI 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M06 MCI 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M12 AD 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M18 AD 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M24 AD 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M30 83.16 F 6.0 29.0 0.5 -sub-CLNC0692 ses-M36 83.16 F 6.0 29.0 0.5 -sub-CLNC0693 ses-M00 MCI 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M06 MCI 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M12 MCI 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M18 MCI 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M24 MCI 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M30 70.62 F 6.0 22.0 1.0 -sub-CLNC0693 ses-M36 70.62 F 6.0 22.0 1.0 -sub-CLNC0694 ses-M00 CN 1.0 64.47 F 9.0 28.0 3.0 -sub-CLNC0694 ses-M03 64.47 F 9.0 28.0 3.0 -sub-CLNC0694 ses-M06 CN 1.0 64.47 F 9.0 28.0 3.0 -sub-CLNC0694 ses-M24 CN 1.0 64.47 F 9.0 28.0 3.0 -sub-CLNC0695 ses-M00 MCI 71.68 F 1.0 20.0 0.5 -sub-CLNC0696 ses-M00 MCI 78.17 F 5.0 27.0 3.0 -sub-CLNC0697 ses-M00 CN 1.0 71.47 M 8.0 20.0 1.0 -sub-CLNC0697 ses-M03 71.47 M 8.0 20.0 1.0 -sub-CLNC0697 ses-M06 CN 1.0 71.47 M 8.0 20.0 1.0 -sub-CLNC0697 ses-M12 CN 1.0 71.47 M 8.0 20.0 1.0 -sub-CLNC0698 ses-M00 CN 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M06 CN 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M12 MCI 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M18 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M24 CN 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M30 72.07 F 1.0 21.0 1.0 -sub-CLNC0698 ses-M36 CN 72.07 F 1.0 21.0 1.0 -sub-CLNC0699 ses-M00 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M06 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M108 MCI 2.0 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M12 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M18 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M24 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M30 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M36 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M48 MCI 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M60 MCI 2.0 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M66 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M72 MCI 2.0 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M78 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M84 MCI 2.0 80.65 F 7.0 23.0 3.0 -sub-CLNC0699 ses-M96 MCI 2.0 80.65 F 7.0 23.0 3.0 -sub-CLNC0700 ses-M00 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M06 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M108 5.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M12 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M120 AD 3.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M18 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M24 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M30 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M36 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M42 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M48 CN 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M54 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M60 CN 1.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M66 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M72 CN 1.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M78 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M84 CN 1.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M90 80.42 F 1.0 23.0 0.0 -sub-CLNC0700 ses-M96 4.0 80.42 F 1.0 23.0 0.0 -sub-CLNC0701 ses-M00 MCI 2.0 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M03 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M06 MCI 2.0 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M12 7.0 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M18 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M24 CN 1.0 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M30 74.32 M 2.0 27.0 0.5 -sub-CLNC0701 ses-M36 CN 1.0 74.32 M 2.0 27.0 0.5 -sub-CLNC0702 ses-M00 AD 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M06 AD 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M12 AD 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M18 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M24 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M30 75.32 F 5.0 22.0 0.0 -sub-CLNC0702 ses-M36 75.32 F 5.0 22.0 0.0 -sub-CLNC0703 ses-M00 CN 1.0 68.43 M 7.0 28.0 0.0 -sub-CLNC0703 ses-M03 68.43 M 7.0 28.0 0.0 -sub-CLNC0703 ses-M06 CN 1.0 68.43 M 7.0 28.0 0.0 -sub-CLNC0703 ses-M12 CN 1.0 68.43 M 7.0 28.0 0.0 -sub-CLNC0704 ses-M00 AD 3.0 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M03 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M06 AD 3.0 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M12 AD 3.0 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M18 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M24 AD 3.0 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M30 69.8 M 4.0 24.0 2.0 -sub-CLNC0704 ses-M42 69.8 M 4.0 24.0 2.0 -sub-CLNC0705 ses-M00 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M03 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M06 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M12 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M24 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M30 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M36 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M48 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0705 ses-M60 MCI 2.0 81.93 M 4.0 28.0 0.5 -sub-CLNC0706 ses-M00 MCI 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M06 MCI 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M12 AD 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M18 AD 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M24 AD 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M30 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M36 AD 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M42 74.74 M 6.0 22.0 1.0 -sub-CLNC0706 ses-M48 AD 74.74 M 6.0 22.0 1.0 -sub-CLNC0707 ses-M00 CN 1.0 79.75 F 5.0 24.0 1.0 -sub-CLNC0707 ses-M03 79.75 F 5.0 24.0 1.0 -sub-CLNC0707 ses-M18 79.75 F 5.0 24.0 1.0 -sub-CLNC0707 ses-M24 79.75 F 5.0 24.0 1.0 -sub-CLNC0708 ses-M00 MCI 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M06 MCI 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M108 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M12 MCI 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M18 MCI 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M24 AD 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M30 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M36 AD 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M48 AD 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M72 AD 3.0 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M84 AD 3.0 81.45 M 5.0 26.0 1.0 -sub-CLNC0708 ses-M90 81.45 M 5.0 26.0 1.0 -sub-CLNC0709 ses-M00 CN 1.0 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M03 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M06 CN 1.0 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M12 CN 1.0 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M18 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M24 CN 1.0 83.6 M 2.0 21.0 2.0 -sub-CLNC0709 ses-M30 83.6 M 2.0 21.0 2.0 -sub-CLNC0710 ses-M00 MCI 2.0 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M03 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M06 MCI 2.0 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M12 MCI 2.0 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M18 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M24 5.0 74.61 M 9.0 21.0 0.0 -sub-CLNC0710 ses-M36 AD 3.0 74.61 M 9.0 21.0 0.0 -sub-CLNC0711 ses-M00 MCI 2.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M06 MCI 2.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M12 MCI 2.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M18 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M24 MCI 2.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M36 MCI 2.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0711 ses-M48 5.0 69.69 M 9.0 23.0 3.0 -sub-CLNC0712 ses-M00 MCI 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M06 MCI 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M108 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M12 AD 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M18 AD 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M24 AD 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M30 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M36 AD 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M42 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M48 AD 3.0 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M54 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M60 AD 3.0 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M66 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M72 AD 3.0 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M78 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M84 AD 3.0 72.42 M 3.0 28.0 3.0 -sub-CLNC0712 ses-M90 72.42 M 3.0 28.0 3.0 -sub-CLNC0713 ses-M00 AD 3.0 81.88 F 3.0 20.0 0.0 -sub-CLNC0713 ses-M03 81.88 F 3.0 20.0 0.0 -sub-CLNC0713 ses-M06 AD 3.0 81.88 F 3.0 20.0 0.0 -sub-CLNC0714 ses-M00 CN 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M06 CN 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M108 MCI 2.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M12 CN 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M120 MCI 2.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M18 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M24 CN 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M30 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M36 CN 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M48 CN 1.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M54 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M60 CN 1.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M66 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M72 CN 1.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M78 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M84 4.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0714 ses-M96 MCI 2.0 82.94 M 4.0 23.0 2.0 -sub-CLNC0715 ses-M00 CN 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M06 CN 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M12 CN 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M18 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M24 CN 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M30 81.16 M 1.0 29.0 2.0 -sub-CLNC0715 ses-M36 81.16 M 1.0 29.0 2.0 -sub-CLNC0716 ses-M00 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M06 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M12 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M18 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M24 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M30 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M36 MCI 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M42 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M48 5.0 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M54 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M60 AD 3.0 86.33 M 2.0 28.0 2.0 -sub-CLNC0716 ses-M66 86.33 M 2.0 28.0 2.0 -sub-CLNC0717 ses-M00 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M06 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M108 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M12 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M18 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M24 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M30 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M36 MCI 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M42 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M48 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M60 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M66 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M72 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M78 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M84 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0717 ses-M96 MCI 2.0 85.08 M 2.0 25.0 0.0 -sub-CLNC0718 ses-M00 AD 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M06 AD 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M12 AD 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M18 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M24 AD 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M30 71.13 F 7.0 29.0 1.0 -sub-CLNC0718 ses-M36 71.13 F 7.0 29.0 1.0 -sub-CLNC0719 ses-M00 MCI 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M06 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M12 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M18 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M24 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M30 88.68 M 5.0 23.0 2.0 -sub-CLNC0719 ses-M36 88.68 M 5.0 23.0 2.0 -sub-CLNC0720 ses-M00 MCI 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M06 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M12 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M18 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M24 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M30 75.15 F 5.0 20.0 1.0 -sub-CLNC0720 ses-M36 75.15 F 5.0 20.0 1.0 -sub-CLNC0721 ses-M00 MCI 2.0 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M03 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M06 MCI 2.0 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M12 MCI 2.0 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M18 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M24 MCI 2.0 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M30 71.43 M 6.0 26.0 1.0 -sub-CLNC0721 ses-M36 MCI 2.0 71.43 M 6.0 26.0 1.0 -sub-CLNC0722 ses-M00 MCI 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M06 AD 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M12 AD 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M18 AD 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M24 AD 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M30 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M36 AD 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M60 AD 3.0 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M66 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M72 AD 3.0 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M78 67.6 M 5.0 24.0 0.5 -sub-CLNC0722 ses-M84 AD 3.0 67.6 M 5.0 24.0 0.5 -sub-CLNC0723 ses-M00 MCI 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M06 MCI 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M108 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M12 MCI 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M120 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M18 MCI 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M24 MCI 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M30 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M36 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M60 5.0 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M66 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M72 AD 3.0 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M78 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M84 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M90 78.53 F 5.0 27.0 3.0 -sub-CLNC0723 ses-M96 78.53 F 5.0 27.0 3.0 -sub-CLNC0724 ses-M00 MCI 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M06 MCI 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M12 AD 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M18 AD 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M24 AD 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M30 75.25 M 5.0 23.0 0.0 -sub-CLNC0724 ses-M36 75.25 M 5.0 23.0 0.0 -sub-CLNC0725 ses-M00 CN 1.0 77.98 F 8.0 22.0 0.5 -sub-CLNC0725 ses-M06 CN 1.0 77.98 F 8.0 22.0 0.5 -sub-CLNC0725 ses-M24 CN 1.0 77.98 F 8.0 22.0 0.5 -sub-CLNC0726 ses-M00 CN 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M06 CN 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M12 CN 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M18 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M24 CN 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M30 75.86 F 7.0 22.0 3.0 -sub-CLNC0726 ses-M36 CN 75.86 F 7.0 22.0 3.0 -sub-CLNC0727 ses-M00 MCI 2.0 65.49 F 6.0 27.0 0.0 -sub-CLNC0727 ses-M03 65.49 F 6.0 27.0 0.0 -sub-CLNC0727 ses-M06 MCI 2.0 65.49 F 6.0 27.0 0.0 -sub-CLNC0727 ses-M12 MCI 2.0 65.49 F 6.0 27.0 0.0 -sub-CLNC0727 ses-M24 MCI 2.0 65.49 F 6.0 27.0 0.0 -sub-CLNC0727 ses-M36 MCI 2.0 65.49 F 6.0 27.0 0.0 -sub-CLNC0728 ses-M00 AD 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M06 AD 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M12 AD 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M18 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M24 AD 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M30 74.84 M 3.0 24.0 3.0 -sub-CLNC0728 ses-M36 74.84 M 3.0 24.0 3.0 -sub-CLNC0729 ses-M00 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M06 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M12 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M18 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M24 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M30 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M36 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M48 MCI 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M54 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M60 MCI 2.0 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M66 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M72 MCI 2.0 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M78 65.59 F 2.0 29.0 3.0 -sub-CLNC0729 ses-M84 65.59 F 2.0 29.0 3.0 -sub-CLNC0730 ses-M00 MCI 2.0 63.01 F 6.0 24.0 1.0 -sub-CLNC0730 ses-M03 63.01 F 6.0 24.0 1.0 -sub-CLNC0730 ses-M06 MCI 2.0 63.01 F 6.0 24.0 1.0 -sub-CLNC0730 ses-M12 MCI 2.0 63.01 F 6.0 24.0 1.0 -sub-CLNC0731 ses-M00 MCI 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M06 MCI 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M12 MCI 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M18 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M24 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M30 74.48 F 9.0 21.0 3.0 -sub-CLNC0731 ses-M36 74.48 F 9.0 21.0 3.0 -sub-CLNC0732 ses-M00 MCI 2.0 85.02 M 8.0 27.0 0.0 -sub-CLNC0732 ses-M03 85.02 M 8.0 27.0 0.0 -sub-CLNC0732 ses-M06 MCI 2.0 85.02 M 8.0 27.0 0.0 -sub-CLNC0732 ses-M12 MCI 2.0 85.02 M 8.0 27.0 0.0 -sub-CLNC0732 ses-M24 MCI 2.0 85.02 M 8.0 27.0 0.0 -sub-CLNC0733 ses-M00 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M03 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M06 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M12 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M18 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M24 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M30 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M36 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M48 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0733 ses-M60 MCI 2.0 81.93 F 6.0 23.0 2.0 -sub-CLNC0734 ses-M00 CN 1.0 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M03 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M06 CN 1.0 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M12 CN 1.0 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M18 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M24 CN 1.0 71.45 M 1.0 24.0 0.5 -sub-CLNC0734 ses-M30 71.45 M 1.0 24.0 0.5 -sub-CLNC0735 ses-M00 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M06 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M108 MCI 2.0 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M12 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M120 MCI 2.0 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M18 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M24 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M30 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M36 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M42 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M48 CN 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M54 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M60 CN 1.0 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M66 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M72 CN 1.0 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M78 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M84 4.0 68.26 M 8.0 28.0 0.5 -sub-CLNC0735 ses-M90 68.26 M 8.0 28.0 0.5 -sub-CLNC0736 ses-M00 AD 3.0 75.43 M 2.0 28.0 0.0 -sub-CLNC0736 ses-M03 75.43 M 2.0 28.0 0.0 -sub-CLNC0736 ses-M06 AD 3.0 75.43 M 2.0 28.0 0.0 -sub-CLNC0737 ses-M00 MCI 2.0 84.11 F 3.0 23.0 1.0 -sub-CLNC0737 ses-M03 84.11 F 3.0 23.0 1.0 -sub-CLNC0737 ses-M06 MCI 2.0 84.11 F 3.0 23.0 1.0 -sub-CLNC0737 ses-M12 MCI 2.0 84.11 F 3.0 23.0 1.0 -sub-CLNC0737 ses-M18 84.11 F 3.0 23.0 1.0 -sub-CLNC0737 ses-M24 MCI 2.0 84.11 F 3.0 23.0 1.0 -sub-CLNC0738 ses-M00 CN 1.0 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M03 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M06 CN 1.0 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M12 CN 1.0 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M18 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M24 CN 1.0 77.06 F 4.0 29.0 1.0 -sub-CLNC0738 ses-M48 CN 1.0 77.06 F 4.0 29.0 1.0 -sub-CLNC0739 ses-M00 AD 3.0 99.69 M 8.0 27.0 0.0 -sub-CLNC0740 ses-M00 CN 1.0 63.91 M 7.0 26.0 2.0 -sub-CLNC0740 ses-M03 63.91 M 7.0 26.0 2.0 -sub-CLNC0740 ses-M06 CN 1.0 63.91 M 7.0 26.0 2.0 -sub-CLNC0740 ses-M24 CN 1.0 63.91 M 7.0 26.0 2.0 -sub-CLNC0741 ses-M00 CN 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M06 CN 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M12 CN 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M18 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M24 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M30 66.14 M 6.0 25.0 2.0 -sub-CLNC0741 ses-M36 66.14 M 6.0 25.0 2.0 -sub-CLNC0742 ses-M00 CN 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M06 CN 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M12 CN 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M18 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M24 CN 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M30 73.63 F 9.0 24.0 1.0 -sub-CLNC0742 ses-M36 CN 73.63 F 9.0 24.0 1.0 -sub-CLNC0743 ses-M00 MCI 2.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M03 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M06 MCI 2.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M12 CN 1.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M18 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M24 MCI 2.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M30 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M36 MCI 2.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0743 ses-M48 MCI 2.0 72.55 F 6.0 25.0 3.0 -sub-CLNC0744 ses-M00 CN 1.0 66.09 M 4.0 29.0 1.0 -sub-CLNC0744 ses-M03 66.09 M 4.0 29.0 1.0 -sub-CLNC0744 ses-M06 CN 1.0 66.09 M 4.0 29.0 1.0 -sub-CLNC0744 ses-M24 CN 1.0 66.09 M 4.0 29.0 1.0 -sub-CLNC0744 ses-M36 66.09 M 4.0 29.0 1.0 -sub-CLNC0745 ses-M00 MCI 2.0 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M03 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M06 MCI 2.0 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M12 MCI 2.0 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M18 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M24 MCI 2.0 75.27 M 3.0 25.0 1.0 -sub-CLNC0745 ses-M36 MCI 2.0 75.27 M 3.0 25.0 1.0 -sub-CLNC0746 ses-M00 AD 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M06 AD 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M12 AD 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M18 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M24 AD 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M30 69.0 M 3.0 29.0 0.0 -sub-CLNC0746 ses-M36 69.0 M 3.0 29.0 0.0 -sub-CLNC0747 ses-M00 AD 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M06 AD 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M12 AD 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M18 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M24 AD 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M30 62.1 M 5.0 25.0 2.0 -sub-CLNC0747 ses-M36 62.1 M 5.0 25.0 2.0 -sub-CLNC0748 ses-M00 MCI 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M06 MCI 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M12 MCI 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M18 MCI 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M24 AD 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M30 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M36 AD 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M42 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M48 AD 3.0 65.03 F 7.0 22.0 1.0 -sub-CLNC0748 ses-M54 65.03 F 7.0 22.0 1.0 -sub-CLNC0749 ses-M00 MCI 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M06 MCI 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M12 AD 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M18 AD 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M24 AD 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M30 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M36 AD 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M42 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M48 AD 3.0 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M54 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M60 AD 3.0 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M66 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M72 AD 3.0 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M90 73.03 F 9.0 20.0 1.0 -sub-CLNC0749 ses-M96 AD 3.0 73.03 F 9.0 20.0 1.0 -sub-CLNC0750 ses-M00 MCI 2.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M03 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M06 5.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M12 AD 3.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M18 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M24 AD 3.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M36 AD 3.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0750 ses-M48 AD 3.0 84.98 F 4.0 29.0 1.0 -sub-CLNC0751 ses-M00 AD 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M06 AD 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M12 AD 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M18 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M24 AD 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M30 67.25 M 7.0 25.0 3.0 -sub-CLNC0751 ses-M36 67.25 M 7.0 25.0 3.0 -sub-CLNC0752 ses-M00 MCI 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M06 MCI 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M12 MCI 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M18 MCI 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M24 MCI 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M30 69.22 M 6.0 28.0 0.0 -sub-CLNC0752 ses-M36 69.22 M 6.0 28.0 0.0 -sub-CLNC0753 ses-M00 CN 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M06 CN 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M12 CN 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M18 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M24 CN 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M30 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M36 CN 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M42 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M48 CN 1.0 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M54 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M72 CN 1.0 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M78 72.77 M 4.0 24.0 3.0 -sub-CLNC0753 ses-M84 CN 1.0 72.77 M 4.0 24.0 3.0 -sub-CLNC0754 ses-M00 CN 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M06 CN 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M12 CN 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M18 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M24 CN 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M30 73.74 M 4.0 22.0 3.0 -sub-CLNC0754 ses-M36 CN 73.74 M 4.0 22.0 3.0 -sub-CLNC0755 ses-M00 CN 1.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M03 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M06 CN 1.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M12 CN 1.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M18 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M24 4.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M30 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M36 MCI 2.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M48 MCI 2.0 80.45 M 9.0 29.0 0.5 -sub-CLNC0755 ses-M60 80.45 M 9.0 29.0 0.5 -sub-CLNC0756 ses-M00 AD 3.0 87.53 M 8.0 25.0 3.0 -sub-CLNC0756 ses-M03 87.53 M 8.0 25.0 3.0 -sub-CLNC0756 ses-M06 AD 3.0 87.53 M 8.0 25.0 3.0 -sub-CLNC0756 ses-M12 AD 3.0 87.53 M 8.0 25.0 3.0 -sub-CLNC0756 ses-M18 87.53 M 8.0 25.0 3.0 -sub-CLNC0757 ses-M00 AD 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M06 AD 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M12 AD 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M18 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M24 AD 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M30 79.95 F 4.0 22.0 2.0 -sub-CLNC0757 ses-M36 79.95 F 4.0 22.0 2.0 -sub-CLNC0758 ses-M00 MCI 2.0 70.57 F 4.0 23.0 3.0 -sub-CLNC0758 ses-M03 70.57 F 4.0 23.0 3.0 -sub-CLNC0758 ses-M06 MCI 2.0 70.57 F 4.0 23.0 3.0 -sub-CLNC0758 ses-M12 MCI 2.0 70.57 F 4.0 23.0 3.0 -sub-CLNC0758 ses-M18 70.57 F 4.0 23.0 3.0 -sub-CLNC0759 ses-M00 MCI 2.0 67.69 F 2.0 29.0 0.0 -sub-CLNC0759 ses-M03 67.69 F 2.0 29.0 0.0 -sub-CLNC0759 ses-M06 5.0 67.69 F 2.0 29.0 0.0 -sub-CLNC0759 ses-M12 5.0 67.69 F 2.0 29.0 0.0 -sub-CLNC0759 ses-M18 67.69 F 2.0 29.0 0.0 -sub-CLNC0760 ses-M00 AD 3.0 71.7 M 1.0 25.0 3.0 -sub-CLNC0760 ses-M03 71.7 M 1.0 25.0 3.0 -sub-CLNC0760 ses-M06 AD 3.0 71.7 M 1.0 25.0 3.0 -sub-CLNC0761 ses-M00 AD 3.0 84.56 M 7.0 26.0 0.5 -sub-CLNC0761 ses-M03 84.56 M 7.0 26.0 0.5 -sub-CLNC0761 ses-M06 AD 3.0 84.56 M 7.0 26.0 0.5 -sub-CLNC0761 ses-M12 AD 3.0 84.56 M 7.0 26.0 0.5 -sub-CLNC0762 ses-M00 CN 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M06 CN 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M12 CN 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M18 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M24 CN 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M30 77.44 F 2.0 25.0 1.0 -sub-CLNC0762 ses-M36 CN 77.44 F 2.0 25.0 1.0 -sub-CLNC0763 ses-M00 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M06 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M12 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M120 CN 1.0 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M18 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M24 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M30 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M36 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M48 CN 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M60 CN 1.0 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M66 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M72 CN 1.0 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M78 78.4 M 2.0 22.0 2.0 -sub-CLNC0763 ses-M84 CN 1.0 78.4 M 2.0 22.0 2.0 -sub-CLNC0764 ses-M00 CN 1.0 68.42 F 5.0 26.0 3.0 -sub-CLNC0764 ses-M03 68.42 F 5.0 26.0 3.0 -sub-CLNC0764 ses-M06 CN 1.0 68.42 F 5.0 26.0 3.0 -sub-CLNC0764 ses-M12 CN 1.0 68.42 F 5.0 26.0 3.0 -sub-CLNC0764 ses-M36 68.42 F 5.0 26.0 3.0 -sub-CLNC0765 ses-M00 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M06 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M12 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M18 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M24 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M30 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M36 MCI 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M72 AD 3.0 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M78 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M84 AD 3.0 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M90 85.31 M 3.0 22.0 2.0 -sub-CLNC0765 ses-M96 AD 3.0 85.31 M 3.0 22.0 2.0 -sub-CLNC0766 ses-M00 CN 1.0 70.0 F 3.0 21.0 3.0 -sub-CLNC0766 ses-M03 70.0 F 3.0 21.0 3.0 -sub-CLNC0766 ses-M06 CN 1.0 70.0 F 3.0 21.0 3.0 -sub-CLNC0766 ses-M12 CN 1.0 70.0 F 3.0 21.0 3.0 -sub-CLNC0766 ses-M24 CN 1.0 70.0 F 3.0 21.0 3.0 -sub-CLNC0767 ses-M00 MCI 2.0 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M03 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M06 MCI 2.0 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M12 MCI 2.0 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M18 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M24 MCI 2.0 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M30 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M36 MCI 2.0 64.35 F 5.0 22.0 3.0 -sub-CLNC0767 ses-M42 64.35 F 5.0 22.0 3.0 -sub-CLNC0768 ses-M00 CN 1.0 68.01 M 9.0 23.0 2.0 -sub-CLNC0768 ses-M03 68.01 M 9.0 23.0 2.0 -sub-CLNC0768 ses-M06 CN 1.0 68.01 M 9.0 23.0 2.0 -sub-CLNC0768 ses-M12 CN 1.0 68.01 M 9.0 23.0 2.0 -sub-CLNC0768 ses-M18 68.01 M 9.0 23.0 2.0 -sub-CLNC0768 ses-M24 CN 1.0 68.01 M 9.0 23.0 2.0 -sub-CLNC0769 ses-M00 CN 1.0 68.86 M 9.0 23.0 1.0 -sub-CLNC0769 ses-M03 68.86 M 9.0 23.0 1.0 -sub-CLNC0769 ses-M06 CN 1.0 68.86 M 9.0 23.0 1.0 -sub-CLNC0769 ses-M12 CN 1.0 68.86 M 9.0 23.0 1.0 -sub-CLNC0769 ses-M18 68.86 M 9.0 23.0 1.0 -sub-CLNC0769 ses-M24 CN 1.0 68.86 M 9.0 23.0 1.0 -sub-CLNC0770 ses-M00 MCI 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M06 MCI 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M12 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M18 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M24 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M30 71.73 F 5.0 20.0 3.0 -sub-CLNC0770 ses-M36 71.73 F 5.0 20.0 3.0 -sub-CLNC0771 ses-M00 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M06 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M108 MCI 2.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M12 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M120 MCI 2.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M18 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M24 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M36 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M42 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M48 CN 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M54 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M60 CN 1.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M66 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M72 CN 1.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M84 4.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0771 ses-M96 4.0 74.73 M 3.0 25.0 2.0 -sub-CLNC0772 ses-M00 CN 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M06 CN 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M12 CN 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M18 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M24 CN 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M30 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M36 CN 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M48 CN 1.0 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M54 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M60 CN 1.0 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M66 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M72 CN 1.0 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M78 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M84 CN 1.0 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M90 70.03 F 3.0 21.0 0.0 -sub-CLNC0772 ses-M96 CN 1.0 70.03 F 3.0 21.0 0.0 -sub-CLNC0773 ses-M00 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M06 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M108 AD 3.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M12 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M120 AD 3.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M18 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M24 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M30 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M36 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M42 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M48 MCI 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M60 MCI 2.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M66 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M72 MCI 2.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M78 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M84 MCI 2.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M90 74.18 F 7.0 27.0 1.0 -sub-CLNC0773 ses-M96 5.0 74.18 F 7.0 27.0 1.0 -sub-CLNC0774 ses-M00 CN 1.0 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M03 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M06 CN 1.0 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M12 CN 1.0 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M18 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M24 CN 1.0 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M30 74.4 M 8.0 24.0 0.0 -sub-CLNC0774 ses-M48 74.4 M 8.0 24.0 0.0 -sub-CLNC0775 ses-M00 MCI 2.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M03 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M06 MCI 2.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M12 7.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M18 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M24 7.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M36 CN 1.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0775 ses-M48 CN 1.0 78.74 M 2.0 20.0 0.5 -sub-CLNC0776 ses-M00 MCI 73.26 F 6.0 21.0 0.0 -sub-CLNC0777 ses-M00 CN 1.0 83.05 F 9.0 26.0 0.5 -sub-CLNC0777 ses-M03 83.05 F 9.0 26.0 0.5 -sub-CLNC0777 ses-M06 CN 1.0 83.05 F 9.0 26.0 0.5 -sub-CLNC0777 ses-M12 CN 1.0 83.05 F 9.0 26.0 0.5 -sub-CLNC0777 ses-M24 CN 1.0 83.05 F 9.0 26.0 0.5 -sub-CLNC0778 ses-M00 MCI 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M06 MCI 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M108 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M12 MCI 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M18 AD 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M24 AD 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M30 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M36 AD 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M48 AD 3.0 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M54 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M60 AD 3.0 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M66 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M72 AD 3.0 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M78 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M84 AD 3.0 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M90 89.94 M 9.0 21.0 0.5 -sub-CLNC0778 ses-M96 89.94 M 9.0 21.0 0.5 -sub-CLNC0779 ses-M00 AD 3.0 65.78 M 8.0 29.0 3.0 -sub-CLNC0780 ses-M00 MCI 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M06 MCI 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M12 MCI 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M18 AD 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M24 AD 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M30 63.37 F 1.0 23.0 0.5 -sub-CLNC0780 ses-M36 AD 63.37 F 1.0 23.0 0.5 -sub-CLNC0781 ses-M00 CN 1.0 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M03 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M06 CN 1.0 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M12 CN 1.0 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M18 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M24 CN 1.0 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M30 59.32 F 6.0 27.0 2.0 -sub-CLNC0781 ses-M48 CN 1.0 59.32 F 6.0 27.0 2.0 -sub-CLNC0782 ses-M00 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M06 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M12 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M18 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M24 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M30 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M36 MCI 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M42 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M48 5.0 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M54 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M60 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M66 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M72 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M78 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M84 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M90 78.55 F 3.0 27.0 2.0 -sub-CLNC0782 ses-M96 78.55 F 3.0 27.0 2.0 -sub-CLNC0783 ses-M00 AD 3.0 66.95 F 8.0 25.0 0.0 -sub-CLNC0783 ses-M06 AD 3.0 66.95 F 8.0 25.0 0.0 -sub-CLNC0783 ses-M12 66.95 F 8.0 25.0 0.0 -sub-CLNC0784 ses-M00 AD 3.0 67.64 M 1.0 28.0 0.0 -sub-CLNC0784 ses-M06 AD 3.0 67.64 M 1.0 28.0 0.0 -sub-CLNC0784 ses-M12 AD 3.0 67.64 M 1.0 28.0 0.0 -sub-CLNC0784 ses-M18 67.64 M 1.0 28.0 0.0 -sub-CLNC0784 ses-M24 67.64 M 1.0 28.0 0.0 -sub-CLNC0784 ses-M30 67.64 M 1.0 28.0 0.0 -sub-CLNC0785 ses-M00 AD 3.0 70.94 F 1.0 25.0 0.5 -sub-CLNC0786 ses-M00 MCI 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M06 MCI 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M12 MCI 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M18 MCI 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M24 MCI 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M30 70.43 M 3.0 21.0 2.0 -sub-CLNC0786 ses-M36 70.43 M 3.0 21.0 2.0 -sub-CLNC0787 ses-M00 MCI 2.0 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M03 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M06 MCI 2.0 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M12 MCI 2.0 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M18 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M24 MCI 2.0 76.58 M 1.0 24.0 3.0 -sub-CLNC0787 ses-M36 5.0 76.58 M 1.0 24.0 3.0 -sub-CLNC0788 ses-M00 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M03 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M06 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M12 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M18 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M24 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M30 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M36 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0788 ses-M48 MCI 2.0 71.09 M 4.0 29.0 1.0 -sub-CLNC0789 ses-M00 CN 1.0 67.72 F 8.0 24.0 0.0 -sub-CLNC0789 ses-M03 67.72 F 8.0 24.0 0.0 -sub-CLNC0789 ses-M06 CN 1.0 67.72 F 8.0 24.0 0.0 -sub-CLNC0789 ses-M12 CN 1.0 67.72 F 8.0 24.0 0.0 -sub-CLNC0789 ses-M36 CN 1.0 67.72 F 8.0 24.0 0.0 -sub-CLNC0790 ses-M00 CN 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M06 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M12 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M18 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M24 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M30 78.83 F 5.0 22.0 3.0 -sub-CLNC0790 ses-M36 78.83 F 5.0 22.0 3.0 -sub-CLNC0791 ses-M00 CN 1.0 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M03 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M06 CN 1.0 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M12 CN 1.0 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M18 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M24 CN 1.0 74.24 F 9.0 29.0 0.0 -sub-CLNC0791 ses-M48 CN 1.0 74.24 F 9.0 29.0 0.0 -sub-CLNC0792 ses-M00 AD 3.0 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M03 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M06 MCI 2.0 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M12 AD 3.0 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M18 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M24 79.54 M 1.0 21.0 2.0 -sub-CLNC0792 ses-M30 79.54 M 1.0 21.0 2.0 -sub-CLNC0793 ses-M00 MCI 2.0 71.16 M 9.0 20.0 2.0 -sub-CLNC0793 ses-M03 71.16 M 9.0 20.0 2.0 -sub-CLNC0793 ses-M36 MCI 2.0 71.16 M 9.0 20.0 2.0 -sub-CLNC0794 ses-M00 CN 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M06 CN 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M12 CN 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M18 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M24 CN 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M30 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M36 CN 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M42 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M48 4.0 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M54 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M60 MCI 2.0 76.55 M 2.0 27.0 3.0 -sub-CLNC0794 ses-M66 76.55 M 2.0 27.0 3.0 -sub-CLNC0795 ses-M00 CN 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M06 CN 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M12 CN 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M18 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M24 CN 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M30 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M36 CN 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M72 4.0 68.61 F 7.0 22.0 2.0 -sub-CLNC0795 ses-M78 68.61 F 7.0 22.0 2.0 -sub-CLNC0796 ses-M00 MCI 2.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M03 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M06 MCI 2.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M12 5.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M18 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M24 AD 3.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M30 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M36 AD 3.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0796 ses-M48 AD 3.0 83.91 M 1.0 28.0 0.0 -sub-CLNC0797 ses-M00 MCI 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M06 MCI 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M102 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M12 MCI 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M18 MCI 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M24 AD 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M30 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M36 AD 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M42 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M48 AD 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M54 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M60 AD 3.0 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M66 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M72 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M78 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M84 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M90 66.04 F 1.0 22.0 0.0 -sub-CLNC0797 ses-M96 66.04 F 1.0 22.0 0.0 -sub-CLNC0798 ses-M00 CN 1.0 80.41 F 5.0 27.0 2.0 -sub-CLNC0798 ses-M03 80.41 F 5.0 27.0 2.0 -sub-CLNC0798 ses-M06 CN 1.0 80.41 F 5.0 27.0 2.0 -sub-CLNC0798 ses-M12 CN 1.0 80.41 F 5.0 27.0 2.0 -sub-CLNC0798 ses-M24 CN 1.0 80.41 F 5.0 27.0 2.0 -sub-CLNC0798 ses-M48 CN 1.0 80.41 F 5.0 27.0 2.0 -sub-CLNC0799 ses-M00 MCI 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M06 MCI 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M12 MCI 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M18 MCI 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M24 MCI 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M30 82.09 F 3.0 27.0 3.0 -sub-CLNC0799 ses-M36 82.09 F 3.0 27.0 3.0 -sub-CLNC0800 ses-M00 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M03 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M06 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M12 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M18 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M24 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M30 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M36 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0800 ses-M48 MCI 2.0 71.38 F 6.0 26.0 1.0 -sub-CLNC0801 ses-M00 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M03 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M06 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M12 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M18 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M24 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M30 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M36 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M48 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0801 ses-M60 MCI 2.0 94.04 F 1.0 20.0 0.0 -sub-CLNC0802 ses-M00 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0802 ses-M06 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0802 ses-M12 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0802 ses-M18 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0802 ses-M24 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0802 ses-M36 MCI 76.39 F 9.0 26.0 0.0 -sub-CLNC0803 ses-M00 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M03 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M06 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M12 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M18 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M24 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M30 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M36 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M48 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0803 ses-M60 MCI 2.0 72.23 M 4.0 25.0 1.0 -sub-CLNC0804 ses-M00 CN 1.0 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M03 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M06 CN 1.0 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M12 CN 1.0 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M18 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M24 CN 1.0 67.52 F 1.0 24.0 1.0 -sub-CLNC0804 ses-M48 CN 1.0 67.52 F 1.0 24.0 1.0 -sub-CLNC0805 ses-M00 CN 1.0 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M03 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M06 CN 1.0 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M12 CN 1.0 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M18 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M24 CN 1.0 73.94 F 5.0 21.0 1.0 -sub-CLNC0805 ses-M48 CN 1.0 73.94 F 5.0 21.0 1.0 -sub-CLNC0806 ses-M00 AD 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M06 AD 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M12 AD 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M18 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M24 AD 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M30 78.14 M 2.0 21.0 1.0 -sub-CLNC0806 ses-M36 78.14 M 2.0 21.0 1.0 -sub-CLNC0807 ses-M00 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M06 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M12 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M18 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M24 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M30 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M36 MCI 72.7 F 2.0 23.0 3.0 -sub-CLNC0807 ses-M42 72.7 F 2.0 23.0 3.0 -sub-CLNC0808 ses-M00 MCI 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M06 MCI 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M12 MCI 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M18 MCI 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M24 MCI 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M30 73.69 F 8.0 20.0 2.0 -sub-CLNC0808 ses-M36 AD 73.69 F 8.0 20.0 2.0 -sub-CLNC0809 ses-M00 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M03 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M06 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M12 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M18 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M24 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M30 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M36 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M42 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M48 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M60 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0809 ses-M72 MCI 2.0 83.49 F 4.0 20.0 0.5 -sub-CLNC0810 ses-M00 MCI 2.0 75.34 F 9.0 26.0 3.0 -sub-CLNC0810 ses-M03 75.34 F 9.0 26.0 3.0 -sub-CLNC0810 ses-M06 MCI 2.0 75.34 F 9.0 26.0 3.0 -sub-CLNC0810 ses-M18 75.34 F 9.0 26.0 3.0 -sub-CLNC0811 ses-M00 CN 1.0 80.5 F 1.0 22.0 0.0 -sub-CLNC0811 ses-M06 CN 1.0 80.5 F 1.0 22.0 0.0 -sub-CLNC0811 ses-M24 CN 1.0 80.5 F 1.0 22.0 0.0 -sub-CLNC0812 ses-M00 AD 3.0 66.76 F 2.0 23.0 3.0 -sub-CLNC0812 ses-M03 66.76 F 2.0 23.0 3.0 -sub-CLNC0812 ses-M06 AD 3.0 66.76 F 2.0 23.0 3.0 -sub-CLNC0812 ses-M12 AD 3.0 66.76 F 2.0 23.0 3.0 -sub-CLNC0812 ses-M18 66.76 F 2.0 23.0 3.0 -sub-CLNC0813 ses-M00 CN 1.0 75.93 M 2.0 28.0 2.0 -sub-CLNC0813 ses-M06 CN 1.0 75.93 M 2.0 28.0 2.0 -sub-CLNC0813 ses-M24 CN 1.0 75.93 M 2.0 28.0 2.0 -sub-CLNC0814 ses-M00 AD 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M06 AD 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M12 AD 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M18 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M24 AD 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M30 67.03 M 3.0 25.0 1.0 -sub-CLNC0814 ses-M36 67.03 M 3.0 25.0 1.0 -sub-CLNC0815 ses-M00 AD 3.0 75.27 F 7.0 22.0 2.0 -sub-CLNC0815 ses-M03 75.27 F 7.0 22.0 2.0 -sub-CLNC0815 ses-M06 AD 3.0 75.27 F 7.0 22.0 2.0 -sub-CLNC0815 ses-M12 AD 3.0 75.27 F 7.0 22.0 2.0 -sub-CLNC0815 ses-M18 75.27 F 7.0 22.0 2.0 -sub-CLNC0816 ses-M00 AD 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M06 AD 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M12 AD 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M18 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M24 AD 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M30 75.22 M 4.0 29.0 1.0 -sub-CLNC0816 ses-M36 75.22 M 4.0 29.0 1.0 -sub-CLNC0817 ses-M00 CN 1.0 76.64 M 8.0 22.0 2.0 -sub-CLNC0817 ses-M03 76.64 M 8.0 22.0 2.0 -sub-CLNC0817 ses-M06 CN 1.0 76.64 M 8.0 22.0 2.0 -sub-CLNC0817 ses-M12 CN 1.0 76.64 M 8.0 22.0 2.0 -sub-CLNC0817 ses-M24 CN 1.0 76.64 M 8.0 22.0 2.0 -sub-CLNC0818 ses-M00 CN 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M06 CN 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M108 7.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M12 CN 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M120 CN 1.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M18 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M24 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M30 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M36 CN 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M42 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M48 CN 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M60 CN 1.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M66 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M72 CN 1.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M78 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M84 4.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M90 92.62 M 4.0 25.0 2.0 -sub-CLNC0818 ses-M96 7.0 92.62 M 4.0 25.0 2.0 -sub-CLNC0819 ses-M00 CN 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M06 CN 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M108 CN 1.0 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M12 CN 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M18 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M24 CN 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M30 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M36 CN 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M48 CN 1.0 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M54 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M60 CN 1.0 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M66 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M72 CN 1.0 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M78 72.51 F 7.0 23.0 2.0 -sub-CLNC0819 ses-M84 CN 1.0 72.51 F 7.0 23.0 2.0 -sub-CLNC0820 ses-M00 CN 1.0 80.19 F 4.0 28.0 2.0 -sub-CLNC0820 ses-M03 80.19 F 4.0 28.0 2.0 -sub-CLNC0820 ses-M06 CN 1.0 80.19 F 4.0 28.0 2.0 -sub-CLNC0820 ses-M12 CN 1.0 80.19 F 4.0 28.0 2.0 -sub-CLNC0820 ses-M18 80.19 F 4.0 28.0 2.0 -sub-CLNC0820 ses-M24 CN 1.0 80.19 F 4.0 28.0 2.0 -sub-CLNC0821 ses-M00 CN 1.0 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M03 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M06 CN 1.0 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M12 CN 1.0 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M18 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M24 CN 1.0 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M30 63.88 F 9.0 24.0 0.0 -sub-CLNC0821 ses-M48 CN 1.0 63.88 F 9.0 24.0 0.0 -sub-CLNC0822 ses-M00 CN 1.0 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M03 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M06 CN 1.0 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M12 CN 1.0 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M18 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M24 CN 1.0 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M30 76.69 M 9.0 27.0 0.0 -sub-CLNC0822 ses-M48 76.69 M 9.0 27.0 0.0 -sub-CLNC0823 ses-M00 MCI 2.0 71.95 F 6.0 22.0 1.0 -sub-CLNC0823 ses-M03 71.95 F 6.0 22.0 1.0 -sub-CLNC0823 ses-M06 MCI 2.0 71.95 F 6.0 22.0 1.0 -sub-CLNC0823 ses-M12 71.95 F 6.0 22.0 1.0 -sub-CLNC0824 ses-M00 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M03 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M06 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M12 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M18 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M24 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M30 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M36 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0824 ses-M48 MCI 2.0 72.56 F 9.0 24.0 0.5 -sub-CLNC0825 ses-M00 AD 3.0 72.19 M 8.0 25.0 2.0 -sub-CLNC0825 ses-M06 AD 3.0 72.19 M 8.0 25.0 2.0 -sub-CLNC0825 ses-M12 AD 3.0 72.19 M 8.0 25.0 2.0 -sub-CLNC0826 ses-M00 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M03 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M06 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M12 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M18 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M24 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M30 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M36 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M48 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0826 ses-M60 MCI 2.0 76.33 M 1.0 21.0 3.0 -sub-CLNC0827 ses-M00 MCI 69.44 F 9.0 20.0 0.0 -sub-CLNC0827 ses-M06 MCI 69.44 F 9.0 20.0 0.0 -sub-CLNC0828 ses-M00 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M06 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M108 AD 3.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M12 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M120 AD 3.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M18 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M24 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M30 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M36 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M42 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M48 CN 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M60 CN 1.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M66 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M72 MCI 2.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M78 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M84 MCI 2.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M90 60.64 F 3.0 28.0 0.5 -sub-CLNC0828 ses-M96 5.0 60.64 F 3.0 28.0 0.5 -sub-CLNC0829 ses-M00 CN 1.0 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M03 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M06 CN 1.0 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M12 CN 1.0 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M18 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M24 CN 1.0 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M30 70.59 M 2.0 25.0 2.0 -sub-CLNC0829 ses-M48 CN 1.0 70.59 M 2.0 25.0 2.0 -sub-CLNC0830 ses-M00 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M06 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M12 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M18 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M24 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M30 59.4 F 1.0 21.0 0.5 -sub-CLNC0830 ses-M36 MCI 59.4 F 1.0 21.0 0.5 -sub-CLNC0831 ses-M00 CN 1.0 80.38 M 8.0 29.0 0.0 -sub-CLNC0831 ses-M03 80.38 M 8.0 29.0 0.0 -sub-CLNC0831 ses-M06 CN 1.0 80.38 M 8.0 29.0 0.0 -sub-CLNC0832 ses-M00 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M03 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M06 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M12 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M18 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M24 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M36 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0832 ses-M48 MCI 2.0 74.62 M 8.0 25.0 0.0 -sub-CLNC0833 ses-M00 MCI 2.0 72.12 F 3.0 26.0 2.0 -sub-CLNC0833 ses-M03 72.12 F 3.0 26.0 2.0 -sub-CLNC0833 ses-M12 MCI 2.0 72.12 F 3.0 26.0 2.0 -sub-CLNC0833 ses-M18 72.12 F 3.0 26.0 2.0 -sub-CLNC0834 ses-M00 MCI 2.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M03 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M06 MCI 2.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M12 MCI 2.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M18 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M24 MCI 2.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M36 5.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0834 ses-M48 8.0 77.3 M 4.0 26.0 2.0 -sub-CLNC0835 ses-M00 CN 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M06 CN 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M12 CN 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M18 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M24 CN 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M30 84.46 M 1.0 27.0 1.0 -sub-CLNC0835 ses-M36 CN 84.46 M 1.0 27.0 1.0 -sub-CLNC0836 ses-M00 MCI 2.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M03 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M06 7.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M12 CN 1.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M18 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M24 CN 1.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M36 4.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0836 ses-M48 MCI 2.0 74.97 F 1.0 23.0 1.0 -sub-CLNC0837 ses-M00 CN 1.0 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M03 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M06 CN 1.0 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M12 CN 1.0 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M18 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M24 CN 1.0 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M30 73.75 F 7.0 24.0 0.0 -sub-CLNC0837 ses-M48 CN 1.0 73.75 F 7.0 24.0 0.0 -sub-CLNC0838 ses-M00 AD 3.0 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M03 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M06 AD 3.0 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M18 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M24 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M30 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M36 63.77 M 4.0 22.0 1.0 -sub-CLNC0838 ses-M48 63.77 M 4.0 22.0 1.0 -sub-CLNC0839 ses-M00 AD 3.0 85.3 F 1.0 26.0 2.0 -sub-CLNC0839 ses-M03 85.3 F 1.0 26.0 2.0 -sub-CLNC0839 ses-M06 AD 3.0 85.3 F 1.0 26.0 2.0 -sub-CLNC0839 ses-M12 AD 3.0 85.3 F 1.0 26.0 2.0 -sub-CLNC0840 ses-M00 MCI 2.0 74.69 F 3.0 29.0 0.5 -sub-CLNC0841 ses-M00 CN 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M06 CN 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M12 CN 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M18 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M24 MCI 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M30 74.47 M 7.0 25.0 3.0 -sub-CLNC0841 ses-M36 MCI 74.47 M 7.0 25.0 3.0 -sub-CLNC0842 ses-M00 CN 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M06 CN 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M12 CN 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M18 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M24 CN 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M30 80.77 F 3.0 21.0 2.0 -sub-CLNC0842 ses-M36 CN 80.77 F 3.0 21.0 2.0 -sub-CLNC0843 ses-M00 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M06 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M108 5.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M12 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M18 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M24 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M30 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M36 MCI 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M42 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M48 MCI 2.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M54 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M60 MCI 2.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M66 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M72 MCI 2.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M78 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M84 MCI 2.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0843 ses-M96 MCI 2.0 75.84 M 2.0 21.0 1.0 -sub-CLNC0844 ses-M00 AD 3.0 80.65 M 3.0 28.0 0.0 -sub-CLNC0844 ses-M03 80.65 M 3.0 28.0 0.0 -sub-CLNC0844 ses-M06 AD 3.0 80.65 M 3.0 28.0 0.0 -sub-CLNC0844 ses-M12 AD 3.0 80.65 M 3.0 28.0 0.0 -sub-CLNC0844 ses-M18 80.65 M 3.0 28.0 0.0 -sub-CLNC0845 ses-M00 MCI 2.0 76.89 M 5.0 25.0 0.0 -sub-CLNC0845 ses-M03 76.89 M 5.0 25.0 0.0 -sub-CLNC0845 ses-M06 5.0 76.89 M 5.0 25.0 0.0 -sub-CLNC0845 ses-M12 MCI 2.0 76.89 M 5.0 25.0 0.0 -sub-CLNC0845 ses-M24 5.0 76.89 M 5.0 25.0 0.0 -sub-CLNC0846 ses-M00 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M03 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M06 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M12 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M18 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M24 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M30 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M36 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M48 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0846 ses-M60 MCI 2.0 71.79 F 4.0 23.0 1.0 -sub-CLNC0847 ses-M00 CN 1.0 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M03 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M06 CN 1.0 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M12 CN 1.0 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M18 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M24 CN 1.0 67.68 F 8.0 26.0 0.5 -sub-CLNC0847 ses-M30 67.68 F 8.0 26.0 0.5 -sub-CLNC0848 ses-M00 MCI 2.0 72.21 F 3.0 22.0 0.5 -sub-CLNC0848 ses-M03 72.21 F 3.0 22.0 0.5 -sub-CLNC0848 ses-M06 MCI 2.0 72.21 F 3.0 22.0 0.5 -sub-CLNC0848 ses-M12 MCI 2.0 72.21 F 3.0 22.0 0.5 -sub-CLNC0848 ses-M18 72.21 F 3.0 22.0 0.5 -sub-CLNC0848 ses-M24 5.0 72.21 F 3.0 22.0 0.5 -sub-CLNC0849 ses-M00 CN 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M06 CN 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M12 CN 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M18 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M24 CN 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M30 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M36 CN 67.3 M 3.0 28.0 0.5 -sub-CLNC0849 ses-M42 67.3 M 3.0 28.0 0.5 -sub-CLNC0850 ses-M00 MCI 2.0 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M03 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M06 MCI 2.0 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M12 MCI 2.0 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M18 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M24 MCI 2.0 71.75 F 4.0 24.0 3.0 -sub-CLNC0850 ses-M60 MCI 2.0 71.75 F 4.0 24.0 3.0 -sub-CLNC0851 ses-M00 MCI 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M06 MCI 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M12 AD 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M18 AD 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M24 AD 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M30 64.46 F 6.0 26.0 0.0 -sub-CLNC0851 ses-M36 64.46 F 6.0 26.0 0.0 -sub-CLNC0852 ses-M00 MCI 2.0 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M03 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M06 MCI 2.0 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M12 MCI 2.0 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M18 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M24 MCI 2.0 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M30 62.43 F 4.0 26.0 3.0 -sub-CLNC0852 ses-M36 MCI 2.0 62.43 F 4.0 26.0 3.0 -sub-CLNC0853 ses-M00 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M06 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M12 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M18 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M24 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M30 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M36 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M42 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M48 CN 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M60 CN 1.0 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M72 CN 1.0 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M78 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M84 CN 1.0 65.08 F 4.0 23.0 0.5 -sub-CLNC0853 ses-M96 CN 1.0 65.08 F 4.0 23.0 0.5 -sub-CLNC0854 ses-M00 MCI 2.0 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M03 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M06 MCI 2.0 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M12 MCI 2.0 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M18 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M24 MCI 2.0 76.28 F 8.0 20.0 0.5 -sub-CLNC0854 ses-M30 76.28 F 8.0 20.0 0.5 -sub-CLNC0855 ses-M00 MCI 2.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M03 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M06 MCI 2.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M12 MCI 2.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M18 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M24 5.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M30 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M36 AD 3.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M48 AD 3.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0855 ses-M60 AD 3.0 70.98 F 6.0 24.0 0.0 -sub-CLNC0856 ses-M00 MCI 77.83 M 6.0 24.0 2.0 -sub-CLNC0856 ses-M06 MCI 77.83 M 6.0 24.0 2.0 -sub-CLNC0856 ses-M12 MCI 77.83 M 6.0 24.0 2.0 -sub-CLNC0856 ses-M24 AD 77.83 M 6.0 24.0 2.0 -sub-CLNC0856 ses-M30 77.83 M 6.0 24.0 2.0 -sub-CLNC0856 ses-M36 AD 77.83 M 6.0 24.0 2.0 -sub-CLNC0857 ses-M00 CN 1.0 76.55 M 9.0 22.0 0.0 -sub-CLNC0857 ses-M03 76.55 M 9.0 22.0 0.0 -sub-CLNC0857 ses-M06 CN 1.0 76.55 M 9.0 22.0 0.0 -sub-CLNC0857 ses-M12 CN 1.0 76.55 M 9.0 22.0 0.0 -sub-CLNC0857 ses-M18 76.55 M 9.0 22.0 0.0 -sub-CLNC0857 ses-M36 4.0 76.55 M 9.0 22.0 0.0 -sub-CLNC0858 ses-M00 MCI 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M06 CN 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M12 CN 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M18 CN 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M24 CN 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M30 69.25 F 7.0 23.0 2.0 -sub-CLNC0858 ses-M36 CN 69.25 F 7.0 23.0 2.0 -sub-CLNC0859 ses-M00 MCI 2.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M03 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M06 MCI 2.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M12 MCI 2.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M18 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M24 MCI 2.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M36 5.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0859 ses-M48 AD 3.0 75.51 F 2.0 29.0 1.0 -sub-CLNC0860 ses-M00 MCI 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M06 MCI 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M12 MCI 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M18 MCI 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M24 AD 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M30 74.63 F 2.0 29.0 2.0 -sub-CLNC0860 ses-M36 AD 74.63 F 2.0 29.0 2.0 -sub-CLNC0861 ses-M00 MCI 2.0 81.22 M 8.0 23.0 3.0 -sub-CLNC0862 ses-M00 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M03 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M06 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M12 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M18 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M24 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M30 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M36 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M42 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M48 MCI 2.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0862 ses-M60 7.0 64.63 M 3.0 28.0 0.5 -sub-CLNC0863 ses-M00 MCI 2.0 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M03 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M06 MCI 2.0 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M12 MCI 2.0 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M18 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M24 MCI 2.0 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M42 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M48 5.0 75.89 M 2.0 28.0 0.5 -sub-CLNC0863 ses-M60 75.89 M 2.0 28.0 0.5 -sub-CLNC0864 ses-M00 CN 1.0 83.99 M 1.0 26.0 2.0 -sub-CLNC0864 ses-M03 83.99 M 1.0 26.0 2.0 -sub-CLNC0864 ses-M06 CN 1.0 83.99 M 1.0 26.0 2.0 -sub-CLNC0864 ses-M12 CN 1.0 83.99 M 1.0 26.0 2.0 -sub-CLNC0864 ses-M18 83.99 M 1.0 26.0 2.0 -sub-CLNC0864 ses-M36 CN 1.0 83.99 M 1.0 26.0 2.0 -sub-CLNC0865 ses-M00 MCI 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M06 MCI 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M12 MCI 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M18 MCI 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M24 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M30 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M36 MCI 2.0 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M48 MCI 2.0 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M54 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M60 MCI 2.0 74.22 F 4.0 28.0 3.0 -sub-CLNC0865 ses-M84 MCI 2.0 74.22 F 4.0 28.0 3.0 -sub-CLNC0866 ses-M00 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M03 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M06 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M12 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M18 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M24 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M30 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M36 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0866 ses-M48 MCI 2.0 73.61 M 5.0 21.0 0.5 -sub-CLNC0867 ses-M00 CN 1.0 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M03 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M06 CN 1.0 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M12 CN 1.0 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M18 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M24 CN 1.0 65.47 F 8.0 20.0 1.0 -sub-CLNC0867 ses-M48 CN 1.0 65.47 F 8.0 20.0 1.0 -sub-CLNC0868 ses-M00 AD 3.0 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M03 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M06 AD 3.0 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M12 AD 3.0 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M18 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M24 AD 3.0 59.8 F 1.0 26.0 0.5 -sub-CLNC0868 ses-M30 59.8 F 1.0 26.0 0.5 -sub-CLNC0869 ses-M00 MCI 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M06 AD 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M12 AD 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M18 AD 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M24 AD 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M30 72.62 M 1.0 23.0 1.0 -sub-CLNC0869 ses-M36 72.62 M 1.0 23.0 1.0 -sub-CLNC0870 ses-M00 MCI 2.0 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M03 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M06 5.0 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M12 AD 3.0 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M18 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M24 AD 3.0 76.32 M 4.0 21.0 1.0 -sub-CLNC0870 ses-M36 AD 3.0 76.32 M 4.0 21.0 1.0 -sub-CLNC0871 ses-M00 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M06 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M108 CN 1.0 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M12 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M18 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M24 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M30 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M36 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M42 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M48 CN 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M60 CN 1.0 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M66 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M72 CN 1.0 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M78 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M84 CN 1.0 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M90 68.57 F 7.0 24.0 0.0 -sub-CLNC0871 ses-M96 CN 1.0 68.57 F 7.0 24.0 0.0 -sub-CLNC0872 ses-M00 AD 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M06 MCI 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M12 MCI 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M18 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M24 AD 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M30 63.23 F 5.0 29.0 0.0 -sub-CLNC0872 ses-M36 63.23 F 5.0 29.0 0.0 -sub-CLNC0873 ses-M00 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M06 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M108 MCI 2.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M12 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M120 MCI 2.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M18 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M24 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M30 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M36 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M48 CN 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M60 4.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M66 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M72 MCI 2.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M78 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M84 MCI 2.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M90 75.85 F 9.0 21.0 2.0 -sub-CLNC0873 ses-M96 MCI 2.0 75.85 F 9.0 21.0 2.0 -sub-CLNC0874 ses-M00 MCI 2.0 65.0 F 4.0 29.0 1.0 -sub-CLNC0874 ses-M03 65.0 F 4.0 29.0 1.0 -sub-CLNC0874 ses-M12 MCI 2.0 65.0 F 4.0 29.0 1.0 -sub-CLNC0874 ses-M18 65.0 F 4.0 29.0 1.0 -sub-CLNC0874 ses-M24 MCI 2.0 65.0 F 4.0 29.0 1.0 -sub-CLNC0874 ses-M36 5.0 65.0 F 4.0 29.0 1.0 -sub-CLNC0875 ses-M00 MCI 2.0 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M03 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M06 MCI 2.0 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M12 MCI 2.0 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M18 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M24 MCI 2.0 77.55 M 5.0 29.0 0.0 -sub-CLNC0875 ses-M48 77.55 M 5.0 29.0 0.0 -sub-CLNC0876 ses-M00 MCI 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M06 MCI 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M12 MCI 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M18 AD 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M24 AD 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M30 70.48 F 4.0 22.0 0.0 -sub-CLNC0876 ses-M36 70.48 F 4.0 22.0 0.0 -sub-CLNC0877 ses-M00 MCI 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M06 MCI 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M12 AD 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M18 AD 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M24 AD 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M30 76.45 M 7.0 21.0 0.5 -sub-CLNC0877 ses-M36 AD 76.45 M 7.0 21.0 0.5 -sub-CLNC0878 ses-M00 AD 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M06 AD 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M12 AD 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M18 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M24 AD 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M30 74.44 M 8.0 29.0 3.0 -sub-CLNC0878 ses-M36 74.44 M 8.0 29.0 3.0 -sub-CLNC0879 ses-M00 MCI 2.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M03 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M06 MCI 2.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M12 MCI 2.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M18 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M24 5.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M36 MCI 2.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0879 ses-M48 5.0 71.06 F 6.0 26.0 3.0 -sub-CLNC0880 ses-M00 CN 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M06 CN 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M12 CN 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M120 CN 1.0 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M18 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M24 CN 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M30 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M36 CN 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M42 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M48 CN 1.0 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M54 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M66 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M72 CN 1.0 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M78 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M84 CN 1.0 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M90 83.76 M 1.0 22.0 3.0 -sub-CLNC0880 ses-M96 CN 1.0 83.76 M 1.0 22.0 3.0 -sub-CLNC0881 ses-M00 MCI 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M06 MCI 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M108 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M12 MCI 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M120 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M18 MCI 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M24 MCI 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M30 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M36 CN 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M42 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M48 CN 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M60 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M66 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M72 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M78 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M84 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M90 61.28 M 7.0 28.0 0.5 -sub-CLNC0881 ses-M96 CN 1.0 61.28 M 7.0 28.0 0.5 -sub-CLNC0882 ses-M00 CN 1.0 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M03 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M06 CN 1.0 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M12 CN 1.0 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M18 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M24 CN 1.0 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M30 65.25 F 3.0 25.0 3.0 -sub-CLNC0882 ses-M48 CN 1.0 65.25 F 3.0 25.0 3.0 -sub-CLNC0883 ses-M00 CN 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M06 CN 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M12 CN 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M18 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M24 CN 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M30 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M36 CN 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M42 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M60 CN 1.0 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M72 CN 1.0 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M78 73.07 F 5.0 26.0 0.0 -sub-CLNC0883 ses-M84 CN 1.0 73.07 F 5.0 26.0 0.0 -sub-CLNC0884 ses-M00 MCI 2.0 77.0 F 8.0 27.0 3.0 -sub-CLNC0884 ses-M03 77.0 F 8.0 27.0 3.0 -sub-CLNC0884 ses-M06 MCI 2.0 77.0 F 8.0 27.0 3.0 -sub-CLNC0884 ses-M12 MCI 2.0 77.0 F 8.0 27.0 3.0 -sub-CLNC0884 ses-M18 77.0 F 8.0 27.0 3.0 -sub-CLNC0884 ses-M24 MCI 2.0 77.0 F 8.0 27.0 3.0 -sub-CLNC0885 ses-M00 AD 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M06 AD 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M12 AD 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M18 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M24 AD 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M30 65.52 F 6.0 21.0 0.0 -sub-CLNC0885 ses-M36 65.52 F 6.0 21.0 0.0 -sub-CLNC0886 ses-M00 CN 1.0 65.17 M 8.0 22.0 3.0 -sub-CLNC0886 ses-M03 65.17 M 8.0 22.0 3.0 -sub-CLNC0886 ses-M06 CN 1.0 65.17 M 8.0 22.0 3.0 -sub-CLNC0886 ses-M12 CN 1.0 65.17 M 8.0 22.0 3.0 -sub-CLNC0886 ses-M36 CN 1.0 65.17 M 8.0 22.0 3.0 -sub-CLNC0887 ses-M00 CN 1.0 76.71 M 1.0 26.0 2.0 -sub-CLNC0887 ses-M03 76.71 M 1.0 26.0 2.0 -sub-CLNC0887 ses-M06 CN 1.0 76.71 M 1.0 26.0 2.0 -sub-CLNC0887 ses-M12 CN 1.0 76.71 M 1.0 26.0 2.0 -sub-CLNC0887 ses-M24 CN 1.0 76.71 M 1.0 26.0 2.0 -sub-CLNC0888 ses-M00 CN 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M06 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M12 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M18 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M24 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M30 84.63 M 3.0 25.0 3.0 -sub-CLNC0888 ses-M36 84.63 M 3.0 25.0 3.0 -sub-CLNC0889 ses-M00 CN 1.0 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M03 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M06 CN 1.0 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M12 CN 1.0 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M18 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M24 CN 1.0 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M30 80.62 F 7.0 21.0 3.0 -sub-CLNC0889 ses-M48 80.62 F 7.0 21.0 3.0 -sub-CLNC0890 ses-M00 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M03 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M06 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M12 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M18 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M24 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M30 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M36 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0890 ses-M48 MCI 2.0 72.78 F 1.0 23.0 2.0 -sub-CLNC0891 ses-M00 CN 1.0 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M03 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M06 CN 1.0 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M12 CN 1.0 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M18 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M24 CN 1.0 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M30 75.0 F 2.0 26.0 0.5 -sub-CLNC0891 ses-M48 CN 1.0 75.0 F 2.0 26.0 0.5 -sub-CLNC0892 ses-M00 AD 3.0 71.19 M 4.0 25.0 2.0 -sub-CLNC0892 ses-M03 71.19 M 4.0 25.0 2.0 -sub-CLNC0892 ses-M06 AD 3.0 71.19 M 4.0 25.0 2.0 -sub-CLNC0892 ses-M12 AD 3.0 71.19 M 4.0 25.0 2.0 -sub-CLNC0892 ses-M18 71.19 M 4.0 25.0 2.0 -sub-CLNC0892 ses-M42 71.19 M 4.0 25.0 2.0 -sub-CLNC0893 ses-M00 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M06 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M12 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M18 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M24 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M30 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M36 MCI 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M42 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M60 5.0 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M66 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M72 AD 3.0 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M78 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M84 70.26 F 1.0 20.0 0.5 -sub-CLNC0893 ses-M90 70.26 F 1.0 20.0 0.5 -sub-CLNC0894 ses-M00 CN 1.0 62.84 M 3.0 22.0 0.0 -sub-CLNC0894 ses-M03 62.84 M 3.0 22.0 0.0 -sub-CLNC0894 ses-M06 CN 1.0 62.84 M 3.0 22.0 0.0 -sub-CLNC0894 ses-M12 CN 1.0 62.84 M 3.0 22.0 0.0 -sub-CLNC0894 ses-M18 62.84 M 3.0 22.0 0.0 -sub-CLNC0894 ses-M36 CN 1.0 62.84 M 3.0 22.0 0.0 -sub-CLNC0895 ses-M00 CN 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M06 CN 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M12 CN 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M18 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M24 CN 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M30 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M36 CN 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M42 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M48 CN 1.0 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M54 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M60 CN 1.0 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M72 CN 1.0 77.25 F 3.0 23.0 2.0 -sub-CLNC0895 ses-M78 77.25 F 3.0 23.0 2.0 -sub-CLNC0896 ses-M00 AD 3.0 82.69 M 3.0 29.0 2.0 -sub-CLNC0897 ses-M00 MCI 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M06 MCI 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M12 MCI 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M18 MCI 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M24 MCI 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M30 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M36 5.0 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M42 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M48 5.0 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M54 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M60 AD 3.0 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M66 76.0 M 3.0 25.0 1.0 -sub-CLNC0897 ses-M84 AD 3.0 76.0 M 3.0 25.0 1.0 -sub-CLNC0898 ses-M00 MCI 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M06 MCI 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M12 MCI 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M18 MCI 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M24 MCI 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M30 85.28 M 3.0 22.0 3.0 -sub-CLNC0898 ses-M36 85.28 M 3.0 22.0 3.0 -sub-CLNC0899 ses-M00 MCI 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M06 MCI 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M12 AD 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M18 AD 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M24 AD 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M30 53.37 M 9.0 23.0 2.0 -sub-CLNC0899 ses-M36 AD 53.37 M 9.0 23.0 2.0 -sub-CLNC0900 ses-M00 MCI 2.0 56.42 M 5.0 27.0 3.0 -sub-CLNC0900 ses-M03 56.42 M 5.0 27.0 3.0 -sub-CLNC0900 ses-M06 5.0 56.42 M 5.0 27.0 3.0 -sub-CLNC0901 ses-M00 MCI 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M06 MCI 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M12 MCI 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M18 MCI 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M24 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M30 70.43 F 4.0 24.0 0.5 -sub-CLNC0901 ses-M36 70.43 F 4.0 24.0 0.5 -sub-CLNC0902 ses-M00 AD 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M06 AD 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M12 AD 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M18 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M24 AD 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M30 87.07 F 5.0 27.0 0.5 -sub-CLNC0902 ses-M36 87.07 F 5.0 27.0 0.5 -sub-CLNC0903 ses-M00 MCI 2.0 69.57 F 3.0 24.0 0.0 -sub-CLNC0903 ses-M03 69.57 F 3.0 24.0 0.0 -sub-CLNC0903 ses-M06 MCI 2.0 69.57 F 3.0 24.0 0.0 -sub-CLNC0903 ses-M12 MCI 2.0 69.57 F 3.0 24.0 0.0 -sub-CLNC0903 ses-M48 MCI 2.0 69.57 F 3.0 24.0 0.0 -sub-CLNC0904 ses-M00 CN 1.0 74.82 F 5.0 28.0 0.5 -sub-CLNC0904 ses-M03 74.82 F 5.0 28.0 0.5 -sub-CLNC0904 ses-M06 CN 1.0 74.82 F 5.0 28.0 0.5 -sub-CLNC0904 ses-M24 CN 1.0 74.82 F 5.0 28.0 0.5 -sub-CLNC0905 ses-M00 MCI 2.0 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M03 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M06 MCI 2.0 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M12 MCI 2.0 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M18 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M24 5.0 76.28 F 3.0 26.0 1.0 -sub-CLNC0905 ses-M36 76.28 F 3.0 26.0 1.0 -sub-CLNC0906 ses-M00 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M06 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M12 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M18 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M24 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M30 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M36 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M42 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M48 CN 66.73 F 2.0 20.0 1.0 -sub-CLNC0906 ses-M54 66.73 F 2.0 20.0 1.0 -sub-CLNC0907 ses-M00 AD 3.0 81.25 F 9.0 21.0 0.0 -sub-CLNC0907 ses-M06 AD 3.0 81.25 F 9.0 21.0 0.0 -sub-CLNC0908 ses-M00 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M06 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M108 CN 1.0 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M12 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M18 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M24 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M30 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M36 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M42 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M48 CN 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M60 CN 1.0 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M66 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M72 CN 1.0 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M78 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M84 CN 1.0 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M90 62.01 F 4.0 20.0 0.0 -sub-CLNC0908 ses-M96 CN 1.0 62.01 F 4.0 20.0 0.0 -sub-CLNC0909 ses-M00 AD 3.0 69.63 M 7.0 24.0 0.0 -sub-CLNC0909 ses-M03 69.63 M 7.0 24.0 0.0 -sub-CLNC0909 ses-M06 AD 3.0 69.63 M 7.0 24.0 0.0 -sub-CLNC0909 ses-M12 AD 3.0 69.63 M 7.0 24.0 0.0 -sub-CLNC0910 ses-M00 CN 1.0 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M03 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M06 CN 1.0 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M12 CN 1.0 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M18 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M24 CN 1.0 81.51 F 7.0 26.0 2.0 -sub-CLNC0910 ses-M48 CN 1.0 81.51 F 7.0 26.0 2.0 -sub-CLNC0911 ses-M00 MCI 2.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M03 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M06 5.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M12 AD 3.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M18 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M24 AD 3.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M36 AD 3.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0911 ses-M48 AD 3.0 88.08 F 9.0 24.0 0.0 -sub-CLNC0912 ses-M00 CN 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M06 CN 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M12 CN 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M18 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M24 CN 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M30 79.39 M 2.0 25.0 1.0 -sub-CLNC0912 ses-M36 CN 79.39 M 2.0 25.0 1.0 -sub-CLNC0913 ses-M00 AD 3.0 77.12 F 6.0 22.0 0.0 -sub-CLNC0913 ses-M03 77.12 F 6.0 22.0 0.0 -sub-CLNC0913 ses-M06 AD 3.0 77.12 F 6.0 22.0 0.0 -sub-CLNC0913 ses-M12 AD 3.0 77.12 F 6.0 22.0 0.0 -sub-CLNC0913 ses-M18 77.12 F 6.0 22.0 0.0 -sub-CLNC0913 ses-M24 AD 3.0 77.12 F 6.0 22.0 0.0 -sub-CLNC0914 ses-M00 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M06 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M12 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M120 CN 1.0 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M18 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M24 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M30 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M36 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M42 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M48 CN 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M60 CN 1.0 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M66 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M72 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M78 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M84 CN 1.0 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M90 76.3 M 3.0 27.0 0.0 -sub-CLNC0914 ses-M96 CN 1.0 76.3 M 3.0 27.0 0.0 -sub-CLNC0915 ses-M00 MCI 2.0 70.42 M 2.0 22.0 0.5 -sub-CLNC0915 ses-M03 70.42 M 2.0 22.0 0.5 -sub-CLNC0915 ses-M06 MCI 2.0 70.42 M 2.0 22.0 0.5 -sub-CLNC0915 ses-M12 MCI 2.0 70.42 M 2.0 22.0 0.5 -sub-CLNC0915 ses-M24 MCI 2.0 70.42 M 2.0 22.0 0.5 -sub-CLNC0916 ses-M00 CN 1.0 87.16 F 7.0 20.0 0.5 -sub-CLNC0916 ses-M06 CN 1.0 87.16 F 7.0 20.0 0.5 -sub-CLNC0916 ses-M24 CN 1.0 87.16 F 7.0 20.0 0.5 -sub-CLNC0917 ses-M00 AD 3.0 57.82 F 1.0 21.0 3.0 -sub-CLNC0917 ses-M03 57.82 F 1.0 21.0 3.0 -sub-CLNC0917 ses-M06 AD 3.0 57.82 F 1.0 21.0 3.0 -sub-CLNC0917 ses-M12 AD 3.0 57.82 F 1.0 21.0 3.0 -sub-CLNC0918 ses-M00 AD 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M06 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M12 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M18 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M24 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M30 66.18 F 5.0 23.0 1.0 -sub-CLNC0918 ses-M36 66.18 F 5.0 23.0 1.0 -sub-CLNC0919 ses-M00 CN 1.0 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M03 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M06 CN 1.0 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M12 CN 1.0 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M18 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M24 CN 1.0 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M48 CN 1.0 74.61 F 3.0 20.0 3.0 -sub-CLNC0919 ses-M54 74.61 F 3.0 20.0 3.0 -sub-CLNC0920 ses-M00 CN 1.0 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M03 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M06 CN 1.0 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M12 CN 1.0 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M18 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M24 CN 1.0 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M30 76.41 M 2.0 25.0 2.0 -sub-CLNC0920 ses-M36 CN 1.0 76.41 M 2.0 25.0 2.0 -sub-CLNC0921 ses-M00 CN 1.0 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M03 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M06 CN 1.0 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M12 CN 1.0 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M18 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M24 CN 1.0 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M30 70.86 F 9.0 27.0 1.0 -sub-CLNC0921 ses-M48 CN 1.0 70.86 F 9.0 27.0 1.0 -sub-CLNC0922 ses-M00 CN 1.0 69.42 F 7.0 22.0 0.5 -sub-CLNC0922 ses-M03 69.42 F 7.0 22.0 0.5 -sub-CLNC0922 ses-M06 CN 1.0 69.42 F 7.0 22.0 0.5 -sub-CLNC0922 ses-M24 CN 1.0 69.42 F 7.0 22.0 0.5 -sub-CLNC0923 ses-M00 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M03 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M12 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M18 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M24 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M30 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M36 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M48 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0923 ses-M60 MCI 2.0 75.72 M 6.0 25.0 1.0 -sub-CLNC0924 ses-M00 CN 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M06 CN 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M12 CN 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M18 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M24 CN 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M30 62.29 F 6.0 23.0 0.5 -sub-CLNC0924 ses-M36 62.29 F 6.0 23.0 0.5 -sub-CLNC0925 ses-M00 CN 1.0 66.17 F 5.0 25.0 0.0 -sub-CLNC0925 ses-M03 66.17 F 5.0 25.0 0.0 -sub-CLNC0925 ses-M06 CN 1.0 66.17 F 5.0 25.0 0.0 -sub-CLNC0925 ses-M12 CN 1.0 66.17 F 5.0 25.0 0.0 -sub-CLNC0925 ses-M18 66.17 F 5.0 25.0 0.0 -sub-CLNC0925 ses-M24 CN 1.0 66.17 F 5.0 25.0 0.0 -sub-CLNC0926 ses-M00 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M03 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M06 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M12 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M18 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M24 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M36 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0926 ses-M48 MCI 2.0 82.25 M 1.0 21.0 0.5 -sub-CLNC0927 ses-M00 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M06 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M108 CN 1.0 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M12 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M18 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M24 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M30 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M36 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M42 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M48 CN 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M54 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M60 CN 1.0 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M66 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M72 CN 1.0 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M78 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M84 CN 1.0 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M90 69.68 M 7.0 20.0 0.5 -sub-CLNC0927 ses-M96 CN 1.0 69.68 M 7.0 20.0 0.5 -sub-CLNC0928 ses-M00 MCI 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M06 MCI 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M12 MCI 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M18 MCI 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M24 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M30 76.62 F 9.0 29.0 2.0 -sub-CLNC0928 ses-M36 76.62 F 9.0 29.0 2.0 -sub-CLNC0929 ses-M00 MCI 2.0 80.84 F 6.0 27.0 0.0 -sub-CLNC0929 ses-M03 80.84 F 6.0 27.0 0.0 -sub-CLNC0929 ses-M06 MCI 2.0 80.84 F 6.0 27.0 0.0 -sub-CLNC0929 ses-M12 MCI 2.0 80.84 F 6.0 27.0 0.0 -sub-CLNC0929 ses-M24 CN 1.0 80.84 F 6.0 27.0 0.0 -sub-CLNC0930 ses-M00 AD 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M06 AD 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M12 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M18 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M24 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M30 78.65 M 3.0 23.0 0.5 -sub-CLNC0930 ses-M36 78.65 M 3.0 23.0 0.5 -sub-CLNC0931 ses-M00 AD 3.0 62.91 M 9.0 28.0 2.0 -sub-CLNC0931 ses-M03 62.91 M 9.0 28.0 2.0 -sub-CLNC0931 ses-M06 AD 3.0 62.91 M 9.0 28.0 2.0 -sub-CLNC0931 ses-M12 AD 3.0 62.91 M 9.0 28.0 2.0 -sub-CLNC0931 ses-M18 62.91 M 9.0 28.0 2.0 -sub-CLNC0932 ses-M00 AD 3.0 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M03 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M06 AD 3.0 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M12 AD 3.0 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M18 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M24 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M30 71.53 F 2.0 29.0 1.0 -sub-CLNC0932 ses-M42 71.53 F 2.0 29.0 1.0 -sub-CLNC0933 ses-M00 MCI 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M06 MCI 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M12 MCI 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M18 MCI 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M24 MCI 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M30 74.92 F 6.0 25.0 0.5 -sub-CLNC0933 ses-M36 AD 74.92 F 6.0 25.0 0.5 -sub-CLNC0934 ses-M00 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M06 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M108 4.0 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M12 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M120 MCI 2.0 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M18 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M24 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M30 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M36 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M42 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M48 CN 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M60 CN 1.0 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M66 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M72 CN 1.0 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M78 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M84 CN 1.0 69.87 M 3.0 29.0 3.0 -sub-CLNC0934 ses-M90 69.87 M 3.0 29.0 3.0 -sub-CLNC0935 ses-M00 AD 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M06 AD 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M12 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M18 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M24 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M30 71.65 F 2.0 20.0 3.0 -sub-CLNC0935 ses-M36 71.65 F 2.0 20.0 3.0 -sub-CLNC0936 ses-M00 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M03 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M06 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M12 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M18 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M24 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M30 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M36 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M48 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0936 ses-M60 MCI 2.0 72.47 M 5.0 22.0 1.0 -sub-CLNC0937 ses-M00 CN 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M06 CN 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M108 AD 3.0 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M12 CN 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M18 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M24 CN 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M30 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M36 CN 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M42 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M48 4.0 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M54 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M72 MCI 2.0 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M78 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M84 MCI 2.0 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M90 75.22 M 7.0 20.0 0.5 -sub-CLNC0937 ses-M96 5.0 75.22 M 7.0 20.0 0.5 -sub-CLNC0938 ses-M00 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M03 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M06 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M12 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M24 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M48 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0938 ses-M60 MCI 2.0 75.11 M 1.0 26.0 2.0 -sub-CLNC0939 ses-M00 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M03 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M06 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M12 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M18 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M24 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M36 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0939 ses-M48 MCI 2.0 74.18 M 3.0 27.0 3.0 -sub-CLNC0940 ses-M00 MCI 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M06 MCI 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M12 MCI 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M18 AD 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M24 AD 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M30 71.46 F 8.0 20.0 0.5 -sub-CLNC0940 ses-M36 AD 71.46 F 8.0 20.0 0.5 -sub-CLNC0941 ses-M00 MCI 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M06 MCI 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M102 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M12 AD 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M18 AD 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M24 AD 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M30 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M36 AD 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M42 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M48 AD 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M54 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M60 AD 3.0 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M66 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M72 AD 3.0 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M78 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M84 AD 3.0 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M90 78.65 M 9.0 23.0 3.0 -sub-CLNC0941 ses-M96 AD 3.0 78.65 M 9.0 23.0 3.0 -sub-CLNC0942 ses-M00 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M06 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M108 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M12 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M120 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M18 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M24 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M30 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M36 MCI 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M42 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M48 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M54 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M72 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M78 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M84 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M90 76.91 M 3.0 20.0 0.5 -sub-CLNC0942 ses-M96 MCI 2.0 76.91 M 3.0 20.0 0.5 -sub-CLNC0943 ses-M00 CN 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M06 CN 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M12 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M18 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M24 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M30 75.4 F 9.0 22.0 0.5 -sub-CLNC0943 ses-M36 75.4 F 9.0 22.0 0.5 -sub-CLNC0944 ses-M00 MCI 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M06 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M12 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M18 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M24 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M30 71.82 F 8.0 25.0 2.0 -sub-CLNC0944 ses-M36 71.82 F 8.0 25.0 2.0 -sub-CLNC0945 ses-M00 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M06 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M12 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M18 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M24 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M30 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M36 MCI 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M42 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M48 AD 3.0 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M54 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M60 AD 3.0 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M66 69.68 M 4.0 26.0 1.0 -sub-CLNC0945 ses-M78 69.68 M 4.0 26.0 1.0 -sub-CLNC0946 ses-M00 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M06 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M12 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M18 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M24 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M30 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M36 MCI 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M42 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M48 MCI 2.0 71.1 M 2.0 21.0 1.0 -sub-CLNC0946 ses-M54 71.1 M 2.0 21.0 1.0 -sub-CLNC0947 ses-M00 AD 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M06 AD 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M12 AD 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M18 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M24 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M30 82.77 F 1.0 20.0 1.0 -sub-CLNC0947 ses-M36 82.77 F 1.0 20.0 1.0 -sub-CLNC0948 ses-M00 MCI 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M06 AD 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M12 AD 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M18 AD 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M24 AD 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M30 73.35 F 3.0 27.0 0.5 -sub-CLNC0948 ses-M36 AD 73.35 F 3.0 27.0 0.5 -sub-CLNC0949 ses-M00 MCI 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M06 MCI 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M12 MCI 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M18 MCI 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M24 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M30 69.43 F 7.0 24.0 1.0 -sub-CLNC0949 ses-M36 69.43 F 7.0 24.0 1.0 -sub-CLNC0950 ses-M00 MCI 2.0 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M03 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M06 MCI 2.0 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M12 MCI 2.0 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M24 MCI 2.0 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M30 68.54 F 2.0 26.0 1.0 -sub-CLNC0950 ses-M36 68.54 F 2.0 26.0 1.0 -sub-CLNC0951 ses-M00 MCI 2.0 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M03 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M06 MCI 2.0 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M12 MCI 2.0 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M18 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M24 MCI 2.0 67.97 F 4.0 28.0 2.0 -sub-CLNC0951 ses-M36 MCI 2.0 67.97 F 4.0 28.0 2.0 -sub-CLNC0952 ses-M00 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M03 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M06 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M12 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M18 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M24 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M30 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M36 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0952 ses-M48 MCI 2.0 61.83 F 3.0 20.0 2.0 -sub-CLNC0953 ses-M00 AD 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M06 AD 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M12 AD 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M18 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M24 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M30 65.03 F 8.0 28.0 0.5 -sub-CLNC0953 ses-M36 65.03 F 8.0 28.0 0.5 -sub-CLNC0954 ses-M00 AD 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M06 AD 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M12 AD 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M18 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M24 AD 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M30 71.7 F 7.0 28.0 0.0 -sub-CLNC0954 ses-M36 71.7 F 7.0 28.0 0.0 -sub-CLNC0955 ses-M00 MCI 2.0 78.94 F 6.0 20.0 0.0 -sub-CLNC0955 ses-M03 78.94 F 6.0 20.0 0.0 -sub-CLNC0955 ses-M06 MCI 2.0 78.94 F 6.0 20.0 0.0 -sub-CLNC0955 ses-M12 MCI 2.0 78.94 F 6.0 20.0 0.0 -sub-CLNC0955 ses-M24 MCI 2.0 78.94 F 6.0 20.0 0.0 -sub-CLNC0955 ses-M36 78.94 F 6.0 20.0 0.0 -sub-CLNC0956 ses-M00 CN 1.0 70.96 F 9.0 25.0 0.5 -sub-CLNC0956 ses-M03 70.96 F 9.0 25.0 0.5 -sub-CLNC0956 ses-M06 CN 1.0 70.96 F 9.0 25.0 0.5 -sub-CLNC0956 ses-M12 CN 1.0 70.96 F 9.0 25.0 0.5 -sub-CLNC0956 ses-M18 70.96 F 9.0 25.0 0.5 -sub-CLNC0957 ses-M00 CN 1.0 86.26 F 5.0 26.0 1.0 -sub-CLNC0957 ses-M03 86.26 F 5.0 26.0 1.0 -sub-CLNC0957 ses-M06 CN 1.0 86.26 F 5.0 26.0 1.0 -sub-CLNC0957 ses-M12 CN 1.0 86.26 F 5.0 26.0 1.0 -sub-CLNC0957 ses-M18 86.26 F 5.0 26.0 1.0 -sub-CLNC0957 ses-M24 CN 1.0 86.26 F 5.0 26.0 1.0 -sub-CLNC0958 ses-M00 CN 1.0 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M03 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M06 CN 1.0 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M12 CN 1.0 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M18 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M24 CN 1.0 75.12 M 1.0 22.0 3.0 -sub-CLNC0958 ses-M48 CN 1.0 75.12 M 1.0 22.0 3.0 -sub-CLNC0959 ses-M00 CN 1.0 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M03 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M06 CN 1.0 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M12 CN 1.0 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M18 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M24 CN 1.0 73.51 M 6.0 26.0 0.5 -sub-CLNC0959 ses-M48 CN 1.0 73.51 M 6.0 26.0 0.5 -sub-CLNC0960 ses-M00 MCI 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M06 MCI 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M12 MCI 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M18 MCI 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M24 MCI 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M30 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M36 AD 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M42 72.82 F 9.0 23.0 2.0 -sub-CLNC0960 ses-M48 AD 72.82 F 9.0 23.0 2.0 -sub-CLNC0961 ses-M00 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M03 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M06 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M12 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M18 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M24 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M36 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0961 ses-M48 MCI 2.0 61.16 F 8.0 21.0 1.0 -sub-CLNC0962 ses-M00 CN 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M06 CN 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M108 CN 1.0 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M12 CN 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M18 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M24 CN 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M30 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M36 CN 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M60 CN 1.0 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M66 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M72 CN 1.0 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M78 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M84 CN 1.0 84.1 F 1.0 20.0 1.0 -sub-CLNC0962 ses-M90 84.1 F 1.0 20.0 1.0 -sub-CLNC0963 ses-M00 CN 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M06 CN 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M12 CN 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M18 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M24 CN 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M30 71.44 M 1.0 26.0 3.0 -sub-CLNC0963 ses-M36 CN 71.44 M 1.0 26.0 3.0 -sub-CLNC0964 ses-M00 AD 3.0 62.24 M 9.0 24.0 2.0 -sub-CLNC0964 ses-M03 62.24 M 9.0 24.0 2.0 -sub-CLNC0964 ses-M06 AD 3.0 62.24 M 9.0 24.0 2.0 -sub-CLNC0964 ses-M12 62.24 M 9.0 24.0 2.0 -sub-CLNC0964 ses-M18 62.24 M 9.0 24.0 2.0 -sub-CLNC0964 ses-M30 62.24 M 9.0 24.0 2.0 -sub-CLNC0965 ses-M00 AD 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M06 AD 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M12 AD 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M18 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M24 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M30 83.46 M 5.0 27.0 3.0 -sub-CLNC0965 ses-M36 83.46 M 5.0 27.0 3.0 -sub-CLNC0966 ses-M00 CN 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M06 CN 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M12 CN 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M18 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M24 CN 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M30 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M36 CN 85.63 M 6.0 29.0 0.0 -sub-CLNC0966 ses-M42 85.63 M 6.0 29.0 0.0 -sub-CLNC0967 ses-M00 CN 1.0 82.83 F 5.0 29.0 3.0 -sub-CLNC0967 ses-M03 82.83 F 5.0 29.0 3.0 -sub-CLNC0967 ses-M06 CN 1.0 82.83 F 5.0 29.0 3.0 -sub-CLNC0967 ses-M24 CN 1.0 82.83 F 5.0 29.0 3.0 -sub-CLNC0967 ses-M36 CN 1.0 82.83 F 5.0 29.0 3.0 -sub-CLNC0968 ses-M00 AD 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M06 AD 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M12 AD 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M18 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M24 AD 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M30 76.12 F 5.0 25.0 3.0 -sub-CLNC0968 ses-M36 76.12 F 5.0 25.0 3.0 -sub-CLNC0969 ses-M00 CN 1.0 82.28 F 9.0 29.0 0.5 -sub-CLNC0969 ses-M03 82.28 F 9.0 29.0 0.5 -sub-CLNC0969 ses-M06 CN 1.0 82.28 F 9.0 29.0 0.5 -sub-CLNC0969 ses-M12 CN 1.0 82.28 F 9.0 29.0 0.5 -sub-CLNC0969 ses-M24 CN 1.0 82.28 F 9.0 29.0 0.5 -sub-CLNC0970 ses-M00 CN 1.0 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M03 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M06 CN 1.0 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M12 CN 1.0 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M18 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M24 CN 1.0 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M30 76.02 F 3.0 29.0 3.0 -sub-CLNC0970 ses-M48 CN 1.0 76.02 F 3.0 29.0 3.0 -sub-CLNC0971 ses-M00 MCI 2.0 72.02 F 1.0 24.0 0.0 -sub-CLNC0971 ses-M03 72.02 F 1.0 24.0 0.0 -sub-CLNC0972 ses-M00 CN 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M06 CN 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M12 CN 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M18 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M24 CN 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M30 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M36 CN 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M42 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M48 CN 1.0 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M54 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M60 CN 1.0 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M66 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M72 CN 1.0 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M78 75.56 M 2.0 27.0 0.5 -sub-CLNC0972 ses-M84 CN 1.0 75.56 M 2.0 27.0 0.5 -sub-CLNC0973 ses-M00 MCI 2.0 83.78 F 4.0 29.0 0.5 -sub-CLNC0973 ses-M03 83.78 F 4.0 29.0 0.5 -sub-CLNC0973 ses-M06 MCI 2.0 83.78 F 4.0 29.0 0.5 -sub-CLNC0973 ses-M12 MCI 2.0 83.78 F 4.0 29.0 0.5 -sub-CLNC0973 ses-M18 83.78 F 4.0 29.0 0.5 -sub-CLNC0973 ses-M24 MCI 2.0 83.78 F 4.0 29.0 0.5 -sub-CLNC0974 ses-M00 MCI 2.0 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M03 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M06 MCI 2.0 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M12 MCI 2.0 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M18 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M24 MCI 2.0 71.97 F 6.0 24.0 3.0 -sub-CLNC0974 ses-M30 71.97 F 6.0 24.0 3.0 -sub-CLNC0975 ses-M00 AD 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M06 AD 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M12 AD 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M18 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M24 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M30 69.24 M 2.0 25.0 1.0 -sub-CLNC0975 ses-M36 69.24 M 2.0 25.0 1.0 -sub-CLNC0976 ses-M00 CN 1.0 79.7 M 5.0 26.0 2.0 -sub-CLNC0976 ses-M06 MCI 2.0 79.7 M 5.0 26.0 2.0 -sub-CLNC0976 ses-M12 MCI 2.0 79.7 M 5.0 26.0 2.0 -sub-CLNC0976 ses-M24 MCI 2.0 79.7 M 5.0 26.0 2.0 -sub-CLNC0976 ses-M36 79.7 M 5.0 26.0 2.0 -sub-CLNC0977 ses-M00 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M06 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M12 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M120 CN 1.0 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M18 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M24 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M30 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M36 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M48 CN 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M60 CN 1.0 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M66 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M72 CN 1.0 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M78 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M84 CN 1.0 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M90 84.12 F 2.0 27.0 1.0 -sub-CLNC0977 ses-M96 CN 1.0 84.12 F 2.0 27.0 1.0 -sub-CLNC0978 ses-M00 CN 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M06 CN 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M12 CN 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M120 CN 1.0 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M18 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M24 CN 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M30 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M36 CN 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M42 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M48 CN 1.0 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M54 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M60 CN 1.0 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M66 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M84 CN 1.0 66.83 M 8.0 27.0 0.0 -sub-CLNC0978 ses-M90 66.83 M 8.0 27.0 0.0 -sub-CLNC0979 ses-M00 AD 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M06 AD 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M12 AD 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M18 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M24 AD 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M30 70.04 F 5.0 21.0 3.0 -sub-CLNC0979 ses-M36 70.04 F 5.0 21.0 3.0 -sub-CLNC0980 ses-M00 CN 1.0 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M03 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M06 CN 1.0 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M12 CN 1.0 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M18 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M24 CN 1.0 78.86 F 7.0 22.0 0.5 -sub-CLNC0980 ses-M48 CN 1.0 78.86 F 7.0 22.0 0.5 -sub-CLNC0981 ses-M00 AD 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M06 AD 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M12 AD 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M18 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M24 AD 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M30 82.04 M 5.0 25.0 2.0 -sub-CLNC0981 ses-M36 82.04 M 5.0 25.0 2.0 -sub-CLNC0982 ses-M00 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M03 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M06 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M12 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M24 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M36 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M48 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0982 ses-M60 MCI 2.0 82.77 F 5.0 22.0 3.0 -sub-CLNC0983 ses-M00 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M03 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M06 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M12 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M18 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M24 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M30 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M36 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0983 ses-M48 MCI 2.0 71.41 M 8.0 21.0 0.0 -sub-CLNC0984 ses-M00 CN 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M06 CN 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M12 CN 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M18 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M24 CN 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M30 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M36 CN 77.58 F 8.0 20.0 3.0 -sub-CLNC0984 ses-M42 77.58 F 8.0 20.0 3.0 -sub-CLNC0985 ses-M00 CN 1.0 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M03 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M06 CN 1.0 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M12 CN 1.0 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M18 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M24 CN 1.0 68.1 F 7.0 21.0 0.0 -sub-CLNC0985 ses-M48 68.1 F 7.0 21.0 0.0 -sub-CLNC0986 ses-M00 CN 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M06 CN 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M12 CN 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M120 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M18 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M24 CN 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M30 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M36 CN 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M60 CN 1.0 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M66 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M72 CN 1.0 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M78 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M84 CN 1.0 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M90 76.44 F 2.0 29.0 0.0 -sub-CLNC0986 ses-M96 CN 1.0 76.44 F 2.0 29.0 0.0 -sub-CLNC0987 ses-M00 MCI 2.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M03 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M06 MCI 2.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M12 MCI 2.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M18 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M24 MCI 2.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M30 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M36 MCI 2.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M48 5.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0987 ses-M60 8.0 70.51 F 5.0 23.0 3.0 -sub-CLNC0988 ses-M00 AD 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M06 AD 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M12 AD 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M18 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M24 AD 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M30 79.17 M 4.0 27.0 2.0 -sub-CLNC0988 ses-M36 79.17 M 4.0 27.0 2.0 -sub-CLNC0989 ses-M00 AD 3.0 80.02 F 4.0 25.0 2.0 -sub-CLNC0989 ses-M03 80.02 F 4.0 25.0 2.0 -sub-CLNC0989 ses-M06 AD 3.0 80.02 F 4.0 25.0 2.0 -sub-CLNC0989 ses-M12 AD 3.0 80.02 F 4.0 25.0 2.0 -sub-CLNC0990 ses-M00 AD 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M06 AD 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M12 AD 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M18 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M24 AD 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M30 83.46 F 5.0 20.0 2.0 -sub-CLNC0990 ses-M36 83.46 F 5.0 20.0 2.0 -sub-CLNC0991 ses-M00 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M06 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M12 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M18 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M24 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M30 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M36 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M42 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M48 MCI 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M54 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M60 5.0 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M66 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M72 AD 3.0 77.23 F 4.0 20.0 2.0 -sub-CLNC0991 ses-M78 77.23 F 4.0 20.0 2.0 -sub-CLNC0992 ses-M00 AD 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M06 AD 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M12 AD 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M18 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M24 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M30 74.56 M 5.0 22.0 0.0 -sub-CLNC0992 ses-M36 74.56 M 5.0 22.0 0.0 -sub-CLNC0993 ses-M00 CN 1.0 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M03 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M06 CN 1.0 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M12 CN 1.0 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M18 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M24 CN 1.0 78.39 F 8.0 22.0 1.0 -sub-CLNC0993 ses-M48 CN 1.0 78.39 F 8.0 22.0 1.0 -sub-CLNC0994 ses-M00 MCI 2.0 79.97 M 8.0 27.0 2.0 -sub-CLNC0994 ses-M03 79.97 M 8.0 27.0 2.0 -sub-CLNC0994 ses-M06 MCI 2.0 79.97 M 8.0 27.0 2.0 -sub-CLNC0994 ses-M12 MCI 2.0 79.97 M 8.0 27.0 2.0 -sub-CLNC0995 ses-M00 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M03 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M06 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M12 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M18 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M24 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M30 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M36 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M48 MCI 2.0 80.3 M 7.0 20.0 0.0 -sub-CLNC0995 ses-M60 80.3 M 7.0 20.0 0.0 -sub-CLNC0996 ses-M00 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M06 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M12 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M18 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M24 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M30 92.11 F 5.0 24.0 0.5 -sub-CLNC0996 ses-M36 MCI 92.11 F 5.0 24.0 0.5 -sub-CLNC0997 ses-M00 AD 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M06 AD 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M12 AD 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M18 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M24 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M30 82.47 M 3.0 25.0 2.0 -sub-CLNC0997 ses-M36 82.47 M 3.0 25.0 2.0 -sub-CLNC0998 ses-M00 MCI 2.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M03 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M06 MCI 2.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M12 5.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M18 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M24 AD 3.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M30 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M36 AD 3.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0998 ses-M48 AD 3.0 59.52 M 2.0 27.0 0.5 -sub-CLNC0999 ses-M00 MCI 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M06 MCI 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M12 AD 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M18 AD 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M24 AD 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M30 62.4 F 4.0 25.0 2.0 -sub-CLNC0999 ses-M36 AD 62.4 F 4.0 25.0 2.0 -sub-CLNC1000 ses-M00 MCI 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M06 MCI 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M12 MCI 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M18 AD 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M24 AD 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M30 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M36 AD 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M42 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M48 AD 86.61 F 9.0 29.0 0.5 -sub-CLNC1000 ses-M54 86.61 F 9.0 29.0 0.5 -sub-CLNC1001 ses-M00 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M06 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M108 MCI 2.0 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M12 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M120 MCI 2.0 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M18 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M24 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M30 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M36 MCI 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M42 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M60 MCI 2.0 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M66 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M72 MCI 2.0 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M78 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M90 70.34 F 2.0 22.0 0.5 -sub-CLNC1001 ses-M96 MCI 2.0 70.34 F 2.0 22.0 0.5 -sub-CLNC1002 ses-M00 AD 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M06 AD 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M12 AD 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M18 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M24 AD 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M30 77.73 F 7.0 29.0 1.0 -sub-CLNC1002 ses-M36 77.73 F 7.0 29.0 1.0 -sub-CLNC1003 ses-M00 MCI 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M06 MCI 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M12 MCI 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M18 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M24 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M30 69.66 F 3.0 23.0 0.0 -sub-CLNC1003 ses-M36 69.66 F 3.0 23.0 0.0 -sub-CLNC1004 ses-M00 AD 3.0 61.97 M 2.0 24.0 2.0 -sub-CLNC1004 ses-M03 61.97 M 2.0 24.0 2.0 -sub-CLNC1004 ses-M06 AD 3.0 61.97 M 2.0 24.0 2.0 -sub-CLNC1004 ses-M12 AD 3.0 61.97 M 2.0 24.0 2.0 -sub-CLNC1004 ses-M18 61.97 M 2.0 24.0 2.0 -sub-CLNC1004 ses-M42 61.97 M 2.0 24.0 2.0 -sub-CLNC1005 ses-M00 MCI 2.0 66.38 M 2.0 27.0 2.0 -sub-CLNC1005 ses-M03 66.38 M 2.0 27.0 2.0 -sub-CLNC1005 ses-M06 MCI 2.0 66.38 M 2.0 27.0 2.0 -sub-CLNC1005 ses-M12 MCI 2.0 66.38 M 2.0 27.0 2.0 -sub-CLNC1005 ses-M18 66.38 M 2.0 27.0 2.0 -sub-CLNC1005 ses-M24 5.0 66.38 M 2.0 27.0 2.0 -sub-CLNC1006 ses-M00 MCI 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M06 MCI 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M12 MCI 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M18 AD 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M24 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M30 75.7 M 4.0 21.0 1.0 -sub-CLNC1006 ses-M36 75.7 M 4.0 21.0 1.0 -sub-CLNC1007 ses-M00 AD 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M06 AD 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M12 AD 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M18 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M24 AD 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M30 77.07 F 9.0 27.0 3.0 -sub-CLNC1007 ses-M36 77.07 F 9.0 27.0 3.0 -sub-CLNC1008 ses-M00 MCI 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M06 MCI 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M12 MCI 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M30 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M42 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M48 MCI 2.0 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M54 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M60 MCI 2.0 71.3 M 8.0 28.0 1.0 -sub-CLNC1008 ses-M66 71.3 M 8.0 28.0 1.0 -sub-CLNC1009 ses-M00 CN 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M06 CN 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M12 CN 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M18 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M24 CN 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M30 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M36 CN 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M60 CN 1.0 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M66 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M72 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M78 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M84 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M90 67.32 F 7.0 29.0 3.0 -sub-CLNC1009 ses-M96 67.32 F 7.0 29.0 3.0 -sub-CLNC1010 ses-M00 CN 1.0 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M03 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M06 CN 1.0 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M12 CN 1.0 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M18 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M24 CN 1.0 79.98 F 1.0 20.0 2.0 -sub-CLNC1010 ses-M48 79.98 F 1.0 20.0 2.0 -sub-CLNC1011 ses-M00 AD 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M06 AD 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M12 AD 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M18 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M24 AD 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M30 73.93 F 7.0 20.0 0.0 -sub-CLNC1011 ses-M36 73.93 F 7.0 20.0 0.0 -sub-CLNC1012 ses-M00 MCI 2.0 76.09 F 7.0 20.0 0.0 -sub-CLNC1012 ses-M03 76.09 F 7.0 20.0 0.0 -sub-CLNC1012 ses-M06 MCI 2.0 76.09 F 7.0 20.0 0.0 -sub-CLNC1012 ses-M12 MCI 2.0 76.09 F 7.0 20.0 0.0 -sub-CLNC1013 ses-M00 MCI 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M06 MCI 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M12 MCI 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M18 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M24 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M30 76.22 M 4.0 27.0 1.0 -sub-CLNC1013 ses-M36 76.22 M 4.0 27.0 1.0 -sub-CLNC1014 ses-M00 CN 1.0 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M03 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M06 CN 1.0 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M12 CN 1.0 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M18 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M24 CN 1.0 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M30 72.96 M 9.0 21.0 0.0 -sub-CLNC1014 ses-M48 CN 1.0 72.96 M 9.0 21.0 0.0 -sub-CLNC1015 ses-M00 MCI 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M06 MCI 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M12 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M18 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M24 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M30 81.41 M 9.0 29.0 3.0 -sub-CLNC1015 ses-M36 81.41 M 9.0 29.0 3.0 -sub-CLNC1016 ses-M00 MCI 2.0 89.29 F 1.0 27.0 1.0 -sub-CLNC1016 ses-M03 89.29 F 1.0 27.0 1.0 -sub-CLNC1016 ses-M06 MCI 2.0 89.29 F 1.0 27.0 1.0 -sub-CLNC1016 ses-M12 MCI 2.0 89.29 F 1.0 27.0 1.0 -sub-CLNC1016 ses-M24 MCI 2.0 89.29 F 1.0 27.0 1.0 -sub-CLNC1017 ses-M00 AD 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M06 AD 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M12 AD 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M18 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M24 AD 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M30 65.15 M 5.0 26.0 0.0 -sub-CLNC1017 ses-M36 65.15 M 5.0 26.0 0.0 -sub-CLNC1018 ses-M00 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M03 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M06 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M12 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M18 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M24 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M30 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M36 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M48 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1018 ses-M60 MCI 2.0 68.12 M 8.0 22.0 3.0 -sub-CLNC1019 ses-M00 MCI 79.37 M 9.0 22.0 0.0 -sub-CLNC1019 ses-M06 MCI 79.37 M 9.0 22.0 0.0 -sub-CLNC1019 ses-M12 MCI 79.37 M 9.0 22.0 0.0 -sub-CLNC1019 ses-M18 MCI 79.37 M 9.0 22.0 0.0 -sub-CLNC1019 ses-M24 MCI 79.37 M 9.0 22.0 0.0 -sub-CLNC1019 ses-M30 79.37 M 9.0 22.0 0.0 -sub-CLNC1020 ses-M00 CN 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M06 CN 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M12 CN 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M120 CN 1.0 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M18 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M24 CN 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M30 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M36 CN 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M72 CN 1.0 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M78 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M84 CN 1.0 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M90 59.26 F 1.0 23.0 1.0 -sub-CLNC1020 ses-M96 CN 1.0 59.26 F 1.0 23.0 1.0 -sub-CLNC1021 ses-M00 MCI 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M06 MCI 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M12 MCI 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M18 MCI 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M24 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M30 66.04 F 6.0 25.0 0.0 -sub-CLNC1021 ses-M36 66.04 F 6.0 25.0 0.0 -sub-CLNC1022 ses-M00 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M03 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M06 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M12 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M24 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M30 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M36 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1022 ses-M48 MCI 2.0 77.84 M 2.0 29.0 1.0 -sub-CLNC1023 ses-M00 MCI 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M06 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M12 MCI 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M18 MCI 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M24 MCI 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M30 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M36 MCI 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M42 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M48 MCI 2.0 72.13 M 4.0 28.0 0.5 -sub-CLNC1023 ses-M54 72.13 M 4.0 28.0 0.5 -sub-CLNC1024 ses-M00 CN 1.0 81.86 M 7.0 28.0 0.0 -sub-CLNC1024 ses-M06 CN 1.0 81.86 M 7.0 28.0 0.0 -sub-CLNC1024 ses-M12 CN 1.0 81.86 M 7.0 28.0 0.0 -sub-CLNC1024 ses-M18 81.86 M 7.0 28.0 0.0 -sub-CLNC1024 ses-M48 CN 1.0 81.86 M 7.0 28.0 0.0 -sub-CLNC1025 ses-M00 CN 1.0 61.12 F 3.0 22.0 0.5 -sub-CLNC1025 ses-M03 61.12 F 3.0 22.0 0.5 -sub-CLNC1025 ses-M06 CN 1.0 61.12 F 3.0 22.0 0.5 -sub-CLNC1025 ses-M12 CN 1.0 61.12 F 3.0 22.0 0.5 -sub-CLNC1025 ses-M18 61.12 F 3.0 22.0 0.5 -sub-CLNC1025 ses-M24 CN 1.0 61.12 F 3.0 22.0 0.5 -sub-CLNC1026 ses-M00 AD 3.0 70.85 F 3.0 28.0 1.0 -sub-CLNC1027 ses-M00 MCI 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M06 MCI 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M12 MCI 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M18 MCI 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M24 MCI 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M30 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M36 AD 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M42 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M48 AD 3.0 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M54 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M66 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M72 AD 3.0 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M78 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M84 AD 3.0 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M90 64.46 M 5.0 21.0 3.0 -sub-CLNC1027 ses-M96 AD 3.0 64.46 M 5.0 21.0 3.0 -sub-CLNC1028 ses-M00 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M06 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M12 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M18 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M24 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M30 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M36 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M42 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M48 CN 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M54 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M60 CN 1.0 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M66 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M72 CN 1.0 82.0 M 2.0 24.0 2.0 -sub-CLNC1028 ses-M78 82.0 M 2.0 24.0 2.0 -sub-CLNC1029 ses-M00 AD 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M06 AD 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M12 AD 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M18 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M24 AD 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M30 61.21 M 3.0 28.0 2.0 -sub-CLNC1029 ses-M36 AD 61.21 M 3.0 28.0 2.0 -sub-CLNC1030 ses-M00 AD 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M06 AD 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M12 AD 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M18 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M24 AD 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M30 72.03 F 7.0 27.0 1.0 -sub-CLNC1030 ses-M36 72.03 F 7.0 27.0 1.0 -sub-CLNC1031 ses-M00 MCI 82.31 M 1.0 21.0 1.0 -sub-CLNC1032 ses-M00 CN 1.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M03 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M06 4.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M12 MCI 2.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M18 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M24 CN 1.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M36 CN 1.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1032 ses-M48 CN 1.0 65.97 F 8.0 24.0 0.0 -sub-CLNC1033 ses-M00 CN 1.0 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M03 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M06 CN 1.0 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M12 CN 1.0 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M18 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M24 CN 1.0 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M30 76.62 M 6.0 24.0 2.0 -sub-CLNC1033 ses-M48 CN 1.0 76.62 M 6.0 24.0 2.0 -sub-CLNC1034 ses-M00 MCI 2.0 72.86 M 9.0 28.0 0.0 -sub-CLNC1034 ses-M03 72.86 M 9.0 28.0 0.0 -sub-CLNC1034 ses-M06 MCI 2.0 72.86 M 9.0 28.0 0.0 -sub-CLNC1034 ses-M12 MCI 2.0 72.86 M 9.0 28.0 0.0 -sub-CLNC1034 ses-M18 72.86 M 9.0 28.0 0.0 -sub-CLNC1034 ses-M24 MCI 2.0 72.86 M 9.0 28.0 0.0 -sub-CLNC1035 ses-M00 MCI 2.0 77.36 M 9.0 23.0 3.0 -sub-CLNC1035 ses-M03 77.36 M 9.0 23.0 3.0 -sub-CLNC1036 ses-M00 MCI 2.0 74.3 F 7.0 21.0 0.0 -sub-CLNC1036 ses-M03 74.3 F 7.0 21.0 0.0 -sub-CLNC1036 ses-M06 MCI 2.0 74.3 F 7.0 21.0 0.0 -sub-CLNC1036 ses-M12 5.0 74.3 F 7.0 21.0 0.0 -sub-CLNC1037 ses-M00 MCI 2.0 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M03 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M06 MCI 2.0 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M12 MCI 2.0 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M18 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M24 MCI 2.0 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M30 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M36 7.0 79.03 M 9.0 27.0 0.0 -sub-CLNC1037 ses-M48 79.03 M 9.0 27.0 0.0 -sub-CLNC1038 ses-M00 AD 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M06 AD 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M12 AD 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M18 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M24 AD 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M30 74.23 M 3.0 21.0 2.0 -sub-CLNC1038 ses-M36 74.23 M 3.0 21.0 2.0 -sub-CLNC1039 ses-M00 CN 1.0 80.21 F 7.0 28.0 2.0 -sub-CLNC1040 ses-M00 MCI 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M06 MCI 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M12 MCI 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M18 AD 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M24 AD 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M30 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M36 AD 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M42 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M48 AD 3.0 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M54 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M60 AD 3.0 81.52 F 8.0 20.0 2.0 -sub-CLNC1040 ses-M66 81.52 F 8.0 20.0 2.0 -sub-CLNC1041 ses-M00 MCI 2.0 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M03 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M06 MCI 2.0 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M12 MCI 2.0 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M18 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M24 MCI 2.0 71.15 F 7.0 21.0 3.0 -sub-CLNC1041 ses-M36 MCI 2.0 71.15 F 7.0 21.0 3.0 -sub-CLNC1042 ses-M00 AD 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M06 AD 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M12 AD 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M18 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M24 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M30 77.46 M 5.0 27.0 2.0 -sub-CLNC1042 ses-M36 77.46 M 5.0 27.0 2.0 -sub-CLNC1043 ses-M00 MCI 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M06 MCI 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M12 MCI 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M18 MCI 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M24 MCI 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M30 69.88 M 3.0 23.0 2.0 -sub-CLNC1043 ses-M36 69.88 M 3.0 23.0 2.0 -sub-CLNC1044 ses-M00 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M03 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M06 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M12 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M18 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M24 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M30 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M36 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M42 86.63 F 5.0 25.0 1.0 -sub-CLNC1044 ses-M48 MCI 2.0 86.63 F 5.0 25.0 1.0 -sub-CLNC1045 ses-M00 MCI 2.0 69.71 M 9.0 26.0 0.5 -sub-CLNC1045 ses-M03 69.71 M 9.0 26.0 0.5 -sub-CLNC1045 ses-M06 MCI 2.0 69.71 M 9.0 26.0 0.5 -sub-CLNC1045 ses-M12 MCI 2.0 69.71 M 9.0 26.0 0.5 -sub-CLNC1045 ses-M24 MCI 2.0 69.71 M 9.0 26.0 0.5 -sub-CLNC1045 ses-M36 MCI 2.0 69.71 M 9.0 26.0 0.5 -sub-CLNC1046 ses-M00 MCI 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M06 MCI 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M12 MCI 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M18 AD 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M24 AD 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M30 63.02 F 9.0 27.0 0.5 -sub-CLNC1046 ses-M36 63.02 F 9.0 27.0 0.5 -sub-CLNC1047 ses-M00 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M06 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M12 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M18 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M24 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M30 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M36 MCI 79.22 F 7.0 26.0 1.0 -sub-CLNC1047 ses-M42 79.22 F 7.0 26.0 1.0 -sub-CLNC1048 ses-M00 AD 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M06 AD 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M12 AD 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M18 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M24 AD 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M30 66.07 M 9.0 24.0 2.0 -sub-CLNC1048 ses-M36 66.07 M 9.0 24.0 2.0 -sub-CLNC1049 ses-M00 MCI 2.0 70.4 F 4.0 21.0 2.0 -sub-CLNC1049 ses-M03 70.4 F 4.0 21.0 2.0 -sub-CLNC1049 ses-M06 MCI 2.0 70.4 F 4.0 21.0 2.0 -sub-CLNC1049 ses-M12 MCI 2.0 70.4 F 4.0 21.0 2.0 -sub-CLNC1049 ses-M24 MCI 2.0 70.4 F 4.0 21.0 2.0 -sub-CLNC1050 ses-M00 AD 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M06 AD 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M12 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M18 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M24 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M30 87.14 F 1.0 26.0 0.0 -sub-CLNC1050 ses-M36 87.14 F 1.0 26.0 0.0 -sub-CLNC1051 ses-M00 MCI 2.0 79.1 M 9.0 26.0 3.0 -sub-CLNC1051 ses-M03 79.1 M 9.0 26.0 3.0 -sub-CLNC1051 ses-M12 MCI 2.0 79.1 M 9.0 26.0 3.0 -sub-CLNC1051 ses-M24 MCI 2.0 79.1 M 9.0 26.0 3.0 -sub-CLNC1051 ses-M36 MCI 2.0 79.1 M 9.0 26.0 3.0 -sub-CLNC1052 ses-M00 MCI 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M06 MCI 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M12 AD 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M18 AD 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M24 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M30 70.85 F 3.0 26.0 0.0 -sub-CLNC1052 ses-M36 70.85 F 3.0 26.0 0.0 -sub-CLNC1053 ses-M00 MCI 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M06 MCI 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M12 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M18 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M24 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M30 67.39 M 3.0 24.0 1.0 -sub-CLNC1053 ses-M36 67.39 M 3.0 24.0 1.0 -sub-CLNC1054 ses-M00 MCI 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M06 MCI 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M12 MCI 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M18 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M24 MCI 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M30 73.3 M 8.0 24.0 3.0 -sub-CLNC1054 ses-M36 73.3 M 8.0 24.0 3.0 -sub-CLNC1055 ses-M00 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M03 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M06 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M12 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M18 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M24 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M30 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M36 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M42 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M48 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1055 ses-M60 MCI 2.0 83.08 F 6.0 24.0 0.5 -sub-CLNC1056 ses-M00 AD 3.0 66.04 M 7.0 22.0 0.5 -sub-CLNC1057 ses-M00 CN 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M06 CN 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M12 CN 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M18 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M24 CN 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M30 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M36 CN 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M42 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M48 CN 1.0 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M60 CN 1.0 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M66 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M72 CN 1.0 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M78 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M84 CN 1.0 80.77 F 8.0 22.0 1.0 -sub-CLNC1057 ses-M96 CN 1.0 80.77 F 8.0 22.0 1.0 -sub-CLNC1058 ses-M00 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M06 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M108 AD 3.0 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M12 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M120 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M18 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M24 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M30 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M36 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M42 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M48 MCI 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M54 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M60 5.0 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M66 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M72 AD 3.0 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M78 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M84 AD 3.0 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M90 64.19 M 2.0 21.0 3.0 -sub-CLNC1058 ses-M96 AD 3.0 64.19 M 2.0 21.0 3.0 -sub-CLNC1059 ses-M00 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M03 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M06 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M12 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M18 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M24 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M30 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M36 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M48 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1059 ses-M60 MCI 2.0 83.5 F 3.0 22.0 0.5 -sub-CLNC1060 ses-M00 MCI 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M06 MCI 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M12 MCI 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M18 MCI 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M24 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M30 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M36 MCI 2.0 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M42 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M48 MCI 2.0 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M54 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M60 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M66 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M72 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M78 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M84 78.6 M 2.0 28.0 3.0 -sub-CLNC1060 ses-M96 78.6 M 2.0 28.0 3.0 -sub-CLNC1061 ses-M00 CN 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M06 CN 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M108 4.0 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M12 CN 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M18 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M24 CN 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M30 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M36 CN 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M42 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M48 CN 1.0 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M54 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M60 CN 1.0 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M66 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M72 CN 1.0 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M78 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M84 CN 1.0 76.43 F 6.0 25.0 0.0 -sub-CLNC1061 ses-M90 76.43 F 6.0 25.0 0.0 -sub-CLNC1062 ses-M00 MCI 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M06 MCI 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M12 MCI 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M18 AD 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M24 AD 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M36 AD 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M42 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M48 AD 3.0 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M54 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M60 AD 3.0 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M66 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M72 AD 3.0 76.03 F 3.0 23.0 2.0 -sub-CLNC1062 ses-M78 76.03 F 3.0 23.0 2.0 -sub-CLNC1063 ses-M00 MCI 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M06 MCI 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M12 MCI 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M18 MCI 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M24 CN 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M30 74.29 M 4.0 28.0 1.0 -sub-CLNC1063 ses-M36 74.29 M 4.0 28.0 1.0 -sub-CLNC1064 ses-M00 AD 3.0 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M03 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M06 AD 3.0 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M12 AD 3.0 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M18 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M24 AD 3.0 71.71 M 5.0 29.0 1.0 -sub-CLNC1064 ses-M30 71.71 M 5.0 29.0 1.0 -sub-CLNC1065 ses-M00 MCI 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M06 MCI 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M12 CN 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M18 CN 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M24 CN 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M30 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M36 CN 81.63 F 3.0 23.0 2.0 -sub-CLNC1065 ses-M42 81.63 F 3.0 23.0 2.0 -sub-CLNC1066 ses-M00 AD 3.0 73.53 M 9.0 27.0 0.5 -sub-CLNC1066 ses-M03 73.53 M 9.0 27.0 0.5 -sub-CLNC1066 ses-M06 AD 3.0 73.53 M 9.0 27.0 0.5 -sub-CLNC1066 ses-M12 AD 3.0 73.53 M 9.0 27.0 0.5 -sub-CLNC1067 ses-M00 CN 1.0 66.55 F 9.0 26.0 1.0 -sub-CLNC1067 ses-M06 CN 1.0 66.55 F 9.0 26.0 1.0 -sub-CLNC1067 ses-M24 CN 1.0 66.55 F 9.0 26.0 1.0 -sub-CLNC1068 ses-M00 MCI 2.0 74.31 M 2.0 26.0 2.0 -sub-CLNC1068 ses-M03 74.31 M 2.0 26.0 2.0 -sub-CLNC1068 ses-M06 MCI 2.0 74.31 M 2.0 26.0 2.0 -sub-CLNC1068 ses-M12 MCI 2.0 74.31 M 2.0 26.0 2.0 -sub-CLNC1068 ses-M18 74.31 M 2.0 26.0 2.0 -sub-CLNC1068 ses-M24 MCI 2.0 74.31 M 2.0 26.0 2.0 -sub-CLNC1069 ses-M00 MCI 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M06 MCI 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M12 MCI 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M18 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M24 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M30 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M36 MCI 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M60 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M66 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M72 72.93 F 4.0 29.0 3.0 -sub-CLNC1069 ses-M78 72.93 F 4.0 29.0 3.0 -sub-CLNC1070 ses-M00 CN 1.0 86.01 M 7.0 20.0 0.0 -sub-CLNC1070 ses-M03 86.01 M 7.0 20.0 0.0 -sub-CLNC1070 ses-M06 CN 1.0 86.01 M 7.0 20.0 0.0 -sub-CLNC1070 ses-M12 CN 1.0 86.01 M 7.0 20.0 0.0 -sub-CLNC1070 ses-M18 86.01 M 7.0 20.0 0.0 -sub-CLNC1070 ses-M24 CN 1.0 86.01 M 7.0 20.0 0.0 -sub-CLNC1071 ses-M00 MCI 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M06 AD 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M12 AD 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M18 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M24 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M30 77.42 F 7.0 26.0 0.0 -sub-CLNC1071 ses-M36 77.42 F 7.0 26.0 0.0 -sub-CLNC1072 ses-M00 MCI 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M06 MCI 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M12 MCI 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M18 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M24 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M30 70.8 M 6.0 29.0 1.0 -sub-CLNC1072 ses-M36 70.8 M 6.0 29.0 1.0 -sub-CLNC1073 ses-M00 AD 3.0 67.49 F 8.0 24.0 0.5 -sub-CLNC1073 ses-M03 67.49 F 8.0 24.0 0.5 -sub-CLNC1073 ses-M06 AD 3.0 67.49 F 8.0 24.0 0.5 -sub-CLNC1073 ses-M12 AD 3.0 67.49 F 8.0 24.0 0.5 -sub-CLNC1073 ses-M18 67.49 F 8.0 24.0 0.5 -sub-CLNC1073 ses-M24 AD 3.0 67.49 F 8.0 24.0 0.5 -sub-CLNC1074 ses-M00 MCI 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M06 MCI 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M12 MCI 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M18 AD 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M24 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M30 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M36 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M60 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M66 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M72 61.82 M 7.0 21.0 0.0 -sub-CLNC1074 ses-M78 61.82 M 7.0 21.0 0.0 -sub-CLNC1075 ses-M00 MCI 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M06 MCI 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M12 AD 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M18 AD 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M24 AD 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M30 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M36 AD 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M48 AD 3.0 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M60 86.65 F 7.0 28.0 0.5 -sub-CLNC1075 ses-M66 86.65 F 7.0 28.0 0.5 -sub-CLNC1076 ses-M00 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M06 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M108 MCI 2.0 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M12 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M18 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M24 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M30 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M36 MCI 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M42 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M60 MCI 2.0 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M72 MCI 2.0 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M78 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M84 MCI 2.0 80.92 M 6.0 29.0 1.0 -sub-CLNC1076 ses-M96 MCI 2.0 80.92 M 6.0 29.0 1.0 -sub-CLNC1077 ses-M00 AD 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M06 AD 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M12 AD 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M18 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M24 AD 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M30 74.88 F 7.0 22.0 2.0 -sub-CLNC1077 ses-M36 74.88 F 7.0 22.0 2.0 -sub-CLNC1078 ses-M00 CN 1.0 94.2 M 6.0 27.0 3.0 -sub-CLNC1078 ses-M03 94.2 M 6.0 27.0 3.0 -sub-CLNC1078 ses-M06 CN 1.0 94.2 M 6.0 27.0 3.0 -sub-CLNC1078 ses-M12 CN 1.0 94.2 M 6.0 27.0 3.0 -sub-CLNC1078 ses-M18 94.2 M 6.0 27.0 3.0 -sub-CLNC1078 ses-M24 CN 1.0 94.2 M 6.0 27.0 3.0 -sub-CLNC1079 ses-M00 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M06 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M12 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M18 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M24 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M30 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M36 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M42 90.0 M 7.0 24.0 1.0 -sub-CLNC1079 ses-M48 CN 90.0 M 7.0 24.0 1.0 -sub-CLNC1080 ses-M00 CN 1.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M03 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M06 4.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M12 7.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M18 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M24 4.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M36 MCI 2.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1080 ses-M48 MCI 2.0 76.0 F 7.0 21.0 1.0 -sub-CLNC1081 ses-M00 CN 1.0 68.11 M 5.0 21.0 2.0 -sub-CLNC1081 ses-M03 68.11 M 5.0 21.0 2.0 -sub-CLNC1081 ses-M06 CN 1.0 68.11 M 5.0 21.0 2.0 -sub-CLNC1081 ses-M24 CN 1.0 68.11 M 5.0 21.0 2.0 -sub-CLNC1082 ses-M00 CN 1.0 61.85 F 6.0 25.0 3.0 -sub-CLNC1082 ses-M03 61.85 F 6.0 25.0 3.0 -sub-CLNC1082 ses-M06 CN 1.0 61.85 F 6.0 25.0 3.0 -sub-CLNC1082 ses-M24 CN 1.0 61.85 F 6.0 25.0 3.0 -sub-CLNC1083 ses-M00 MCI 2.0 70.21 M 5.0 23.0 2.0 -sub-CLNC1083 ses-M03 70.21 M 5.0 23.0 2.0 -sub-CLNC1083 ses-M06 MCI 2.0 70.21 M 5.0 23.0 2.0 -sub-CLNC1083 ses-M12 MCI 2.0 70.21 M 5.0 23.0 2.0 -sub-CLNC1083 ses-M24 MCI 2.0 70.21 M 5.0 23.0 2.0 -sub-CLNC1083 ses-M36 70.21 M 5.0 23.0 2.0 -sub-CLNC1084 ses-M00 CN 1.0 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M03 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M06 CN 1.0 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M12 CN 1.0 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M18 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M24 CN 1.0 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M30 79.23 F 9.0 25.0 0.5 -sub-CLNC1084 ses-M48 CN 1.0 79.23 F 9.0 25.0 0.5 -sub-CLNC1085 ses-M00 CN 1.0 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M03 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M06 CN 1.0 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M12 CN 1.0 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M18 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M24 CN 1.0 78.9 F 3.0 24.0 0.0 -sub-CLNC1085 ses-M48 4.0 78.9 F 3.0 24.0 0.0 -sub-CLNC1086 ses-M00 AD 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M06 AD 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M12 AD 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M18 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M24 AD 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M30 70.0 F 2.0 27.0 3.0 -sub-CLNC1086 ses-M36 70.0 F 2.0 27.0 3.0 -sub-CLNC1087 ses-M00 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M06 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M12 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M18 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M24 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M30 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M36 MCI 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M60 MCI 2.0 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M66 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M72 MCI 2.0 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M78 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M84 MCI 2.0 75.45 M 7.0 22.0 2.0 -sub-CLNC1087 ses-M96 MCI 2.0 75.45 M 7.0 22.0 2.0 -sub-CLNC1088 ses-M00 MCI 2.0 59.54 M 9.0 25.0 3.0 -sub-CLNC1089 ses-M00 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M06 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M12 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M120 CN 1.0 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M18 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M24 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M30 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M36 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M42 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M48 CN 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M60 CN 1.0 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M66 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M72 CN 1.0 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M78 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M84 CN 1.0 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M90 64.67 F 5.0 29.0 0.0 -sub-CLNC1089 ses-M96 CN 1.0 64.67 F 5.0 29.0 0.0 -sub-CLNC1090 ses-M00 MCI 2.0 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M03 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M06 MCI 2.0 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M12 MCI 2.0 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M18 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M24 MCI 2.0 78.46 F 3.0 20.0 0.0 -sub-CLNC1090 ses-M36 78.46 F 3.0 20.0 0.0 -sub-CLNC1091 ses-M00 CN 1.0 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M03 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M06 CN 1.0 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M12 CN 1.0 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M18 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M24 CN 1.0 80.57 M 2.0 27.0 1.0 -sub-CLNC1091 ses-M48 CN 1.0 80.57 M 2.0 27.0 1.0 -sub-CLNC1092 ses-M00 AD 3.0 84.85 F 3.0 24.0 3.0 -sub-CLNC1092 ses-M03 84.85 F 3.0 24.0 3.0 -sub-CLNC1092 ses-M12 AD 3.0 84.85 F 3.0 24.0 3.0 -sub-CLNC1092 ses-M18 84.85 F 3.0 24.0 3.0 -sub-CLNC1093 ses-M00 AD 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M06 AD 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M12 AD 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M18 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M24 AD 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M30 74.98 M 3.0 27.0 2.0 -sub-CLNC1093 ses-M36 74.98 M 3.0 27.0 2.0 -sub-CLNC1094 ses-M00 CN 1.0 74.63 F 9.0 20.0 0.0 -sub-CLNC1095 ses-M00 MCI 2.0 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M03 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M06 MCI 2.0 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M12 MCI 2.0 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M18 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M24 MCI 2.0 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M36 MCI 2.0 72.52 M 3.0 27.0 2.0 -sub-CLNC1095 ses-M48 72.52 M 3.0 27.0 2.0 -sub-CLNC1096 ses-M00 AD 3.0 72.3 M 6.0 25.0 2.0 -sub-CLNC1096 ses-M03 72.3 M 6.0 25.0 2.0 -sub-CLNC1096 ses-M06 AD 3.0 72.3 M 6.0 25.0 2.0 -sub-CLNC1096 ses-M18 72.3 M 6.0 25.0 2.0 -sub-CLNC1096 ses-M24 AD 3.0 72.3 M 6.0 25.0 2.0 -sub-CLNC1097 ses-M00 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M03 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M06 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M12 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M18 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M24 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M30 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M36 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M48 MCI 2.0 73.45 M 6.0 23.0 1.0 -sub-CLNC1097 ses-M60 73.45 M 6.0 23.0 1.0 -sub-CLNC1098 ses-M00 AD 3.0 87.14 M 4.0 21.0 3.0 -sub-CLNC1099 ses-M00 MCI 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M06 AD 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M12 AD 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M18 AD 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M24 AD 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M30 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M36 AD 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M42 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M48 AD 3.0 86.84 M 1.0 24.0 0.5 -sub-CLNC1099 ses-M54 86.84 M 1.0 24.0 0.5 -sub-CLNC1100 ses-M00 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M06 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M108 CN 1.0 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M12 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M18 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M24 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M30 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M36 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M42 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M48 CN 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M54 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M60 CN 1.0 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M66 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M72 CN 1.0 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M78 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M84 CN 1.0 75.59 F 9.0 21.0 1.0 -sub-CLNC1100 ses-M90 75.59 F 9.0 21.0 1.0 -sub-CLNC1101 ses-M00 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M06 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M108 MCI 2.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M12 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M120 7.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M18 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M24 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M30 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M36 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M42 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M48 MCI 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M60 MCI 2.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M66 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M72 MCI 2.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M78 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M84 MCI 2.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M90 81.42 M 3.0 23.0 0.0 -sub-CLNC1101 ses-M96 MCI 2.0 81.42 M 3.0 23.0 0.0 -sub-CLNC1102 ses-M00 AD 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M06 AD 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M12 AD 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M18 AD 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M24 AD 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M30 71.92 M 9.0 22.0 1.0 -sub-CLNC1102 ses-M36 71.92 M 9.0 22.0 1.0 -sub-CLNC1103 ses-M00 MCI 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M06 MCI 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M12 AD 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M18 AD 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M24 AD 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M30 75.36 M 7.0 26.0 2.0 -sub-CLNC1103 ses-M36 75.36 M 7.0 26.0 2.0 -sub-CLNC1104 ses-M00 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M03 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M06 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M12 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M18 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M24 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M30 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M36 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1104 ses-M48 MCI 2.0 71.81 M 1.0 24.0 2.0 -sub-CLNC1105 ses-M00 AD 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M06 AD 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M12 AD 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M18 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M24 AD 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M30 69.07 F 3.0 22.0 2.0 -sub-CLNC1105 ses-M36 69.07 F 3.0 22.0 2.0 -sub-CLNC1106 ses-M00 MCI 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M06 MCI 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M12 MCI 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M18 MCI 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M24 MCI 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M30 86.66 M 7.0 24.0 0.5 -sub-CLNC1106 ses-M36 AD 86.66 M 7.0 24.0 0.5 -sub-CLNC1107 ses-M00 AD 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M06 AD 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M12 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M18 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M24 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M30 68.43 F 7.0 21.0 3.0 -sub-CLNC1107 ses-M36 68.43 F 7.0 21.0 3.0 -sub-CLNC1108 ses-M00 MCI 2.0 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M03 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M06 MCI 2.0 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M12 MCI 2.0 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M24 MCI 2.0 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M30 86.08 M 1.0 23.0 0.5 -sub-CLNC1108 ses-M36 MCI 2.0 86.08 M 1.0 23.0 0.5 -sub-CLNC1109 ses-M00 CN 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M06 CN 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M12 CN 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M18 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M24 CN 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M30 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M36 CN 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M42 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M60 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M66 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M72 CN 1.0 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M78 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M84 CN 1.0 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M90 73.14 M 2.0 24.0 1.0 -sub-CLNC1109 ses-M96 CN 1.0 73.14 M 2.0 24.0 1.0 -sub-CLNC1110 ses-M00 MCI 2.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M03 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M06 MCI 2.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M12 MCI 2.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M18 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M24 MCI 2.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M36 5.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1110 ses-M48 AD 3.0 76.23 F 3.0 29.0 1.0 -sub-CLNC1111 ses-M00 CN 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M06 CN 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M12 CN 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M18 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M24 CN 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M30 65.44 M 4.0 21.0 1.0 -sub-CLNC1111 ses-M36 CN 65.44 M 4.0 21.0 1.0 -sub-CLNC1112 ses-M00 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M06 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M12 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M18 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M24 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M30 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M36 MCI 64.9 F 5.0 22.0 3.0 -sub-CLNC1112 ses-M48 AD 64.9 F 5.0 22.0 3.0 -sub-CLNC1113 ses-M00 MCI 2.0 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M03 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M06 MCI 2.0 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M12 MCI 2.0 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M24 MCI 2.0 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M36 MCI 2.0 69.1 F 9.0 21.0 1.0 -sub-CLNC1113 ses-M48 69.1 F 9.0 21.0 1.0 -sub-CLNC1114 ses-M00 MCI 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M06 MCI 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M12 MCI 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M18 MCI 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M24 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M30 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M36 MCI 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M42 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M60 MCI 2.0 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M66 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M78 72.98 M 9.0 28.0 0.5 -sub-CLNC1114 ses-M84 MCI 2.0 72.98 M 9.0 28.0 0.5 -sub-CLNC1115 ses-M00 MCI 74.51 F 2.0 24.0 1.0 -sub-CLNC1115 ses-M06 MCI 74.51 F 2.0 24.0 1.0 -sub-CLNC1116 ses-M00 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M03 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M06 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M12 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M18 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M24 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M30 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M36 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M48 MCI 2.0 77.97 F 2.0 29.0 0.0 -sub-CLNC1116 ses-M60 77.97 F 2.0 29.0 0.0 -sub-CLNC1117 ses-M00 MCI 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M06 MCI 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M12 AD 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M18 AD 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M24 AD 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M30 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M36 AD 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M42 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M48 AD 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M54 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M60 AD 3.0 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M66 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M72 AD 3.0 71.47 M 6.0 26.0 2.0 -sub-CLNC1117 ses-M84 AD 3.0 71.47 M 6.0 26.0 2.0 -sub-CLNC1118 ses-M00 AD 3.0 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M03 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M06 AD 3.0 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M12 AD 3.0 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M18 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M24 AD 3.0 82.94 F 3.0 27.0 1.0 -sub-CLNC1118 ses-M30 82.94 F 3.0 27.0 1.0 -sub-CLNC1119 ses-M00 MCI 2.0 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M03 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M06 MCI 2.0 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M12 MCI 2.0 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M18 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M24 5.0 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M36 5.0 61.34 M 7.0 20.0 2.0 -sub-CLNC1119 ses-M42 61.34 M 7.0 20.0 2.0 -sub-CLNC1120 ses-M00 MCI 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M06 MCI 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M12 AD 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M18 AD 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M24 AD 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M30 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M36 AD 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M42 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M48 AD 3.0 69.37 F 3.0 20.0 1.0 -sub-CLNC1120 ses-M54 69.37 F 3.0 20.0 1.0 -sub-CLNC1121 ses-M00 MCI 2.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M06 5.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M12 AD 3.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M18 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M24 5.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M36 5.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1121 ses-M48 AD 3.0 78.91 M 3.0 24.0 0.5 -sub-CLNC1122 ses-M00 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M06 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M12 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M18 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M24 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M30 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M36 MCI 74.54 M 8.0 23.0 0.5 -sub-CLNC1122 ses-M42 74.54 M 8.0 23.0 0.5 -sub-CLNC1123 ses-M00 MCI 2.0 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M03 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M06 MCI 2.0 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M12 MCI 2.0 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M18 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M24 MCI 2.0 69.11 F 5.0 21.0 0.5 -sub-CLNC1123 ses-M36 MCI 2.0 69.11 F 5.0 21.0 0.5 -sub-CLNC1124 ses-M00 AD 3.0 71.43 F 5.0 23.0 2.0 -sub-CLNC1124 ses-M03 71.43 F 5.0 23.0 2.0 -sub-CLNC1124 ses-M06 AD 3.0 71.43 F 5.0 23.0 2.0 -sub-CLNC1124 ses-M12 AD 3.0 71.43 F 5.0 23.0 2.0 -sub-CLNC1124 ses-M18 71.43 F 5.0 23.0 2.0 -sub-CLNC1124 ses-M24 AD 3.0 71.43 F 5.0 23.0 2.0 -sub-CLNC1125 ses-M00 CN 1.0 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M03 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M06 CN 1.0 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M12 CN 1.0 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M18 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M24 CN 1.0 66.08 M 3.0 23.0 2.0 -sub-CLNC1125 ses-M48 CN 1.0 66.08 M 3.0 23.0 2.0 -sub-CLNC1126 ses-M00 AD 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M06 AD 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M12 AD 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M18 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M24 AD 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M30 74.67 M 7.0 28.0 0.5 -sub-CLNC1126 ses-M36 74.67 M 7.0 28.0 0.5 -sub-CLNC1127 ses-M00 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M03 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M06 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M12 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M18 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M24 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M36 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1127 ses-M48 MCI 2.0 81.71 M 7.0 21.0 1.0 -sub-CLNC1128 ses-M00 MCI 2.0 76.41 M 4.0 25.0 1.0 -sub-CLNC1128 ses-M03 76.41 M 4.0 25.0 1.0 -sub-CLNC1128 ses-M06 MCI 2.0 76.41 M 4.0 25.0 1.0 -sub-CLNC1128 ses-M12 MCI 2.0 76.41 M 4.0 25.0 1.0 -sub-CLNC1128 ses-M18 76.41 M 4.0 25.0 1.0 -sub-CLNC1128 ses-M24 MCI 2.0 76.41 M 4.0 25.0 1.0 -sub-CLNC1129 ses-M00 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M03 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M06 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M12 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M18 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M24 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M30 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M36 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M42 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M48 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1129 ses-M60 MCI 2.0 82.19 F 7.0 26.0 0.5 -sub-CLNC1130 ses-M00 MCI 2.0 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M03 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M06 MCI 2.0 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M12 MCI 2.0 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M18 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M24 MCI 2.0 74.04 F 2.0 20.0 0.0 -sub-CLNC1130 ses-M30 74.04 F 2.0 20.0 0.0 -sub-CLNC1131 ses-M00 MCI 2.0 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M03 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M06 MCI 2.0 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M12 MCI 2.0 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M18 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M24 MCI 2.0 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M30 74.67 F 5.0 23.0 0.5 -sub-CLNC1131 ses-M48 MCI 2.0 74.67 F 5.0 23.0 0.5 -sub-CLNC1132 ses-M00 MCI 2.0 62.71 F 4.0 28.0 2.0 -sub-CLNC1132 ses-M03 62.71 F 4.0 28.0 2.0 -sub-CLNC1132 ses-M06 MCI 2.0 62.71 F 4.0 28.0 2.0 -sub-CLNC1132 ses-M18 62.71 F 4.0 28.0 2.0 -sub-CLNC1133 ses-M00 CN 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M06 CN 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M12 CN 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M18 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M24 CN 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M30 71.07 F 5.0 26.0 0.0 -sub-CLNC1133 ses-M36 71.07 F 5.0 26.0 0.0 -sub-CLNC1134 ses-M00 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M06 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M12 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M18 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M24 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M30 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M36 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M42 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M48 CN 67.12 M 7.0 21.0 1.0 -sub-CLNC1134 ses-M54 67.12 M 7.0 21.0 1.0 -sub-CLNC1135 ses-M00 CN 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M06 CN 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M12 CN 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M18 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M24 CN 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M30 73.57 F 9.0 20.0 0.5 -sub-CLNC1135 ses-M36 CN 73.57 F 9.0 20.0 0.5 -sub-CLNC1136 ses-M00 AD 3.0 73.17 F 7.0 21.0 0.0 -sub-CLNC1136 ses-M03 73.17 F 7.0 21.0 0.0 -sub-CLNC1136 ses-M06 AD 3.0 73.17 F 7.0 21.0 0.0 -sub-CLNC1136 ses-M12 AD 3.0 73.17 F 7.0 21.0 0.0 -sub-CLNC1137 ses-M00 CN 1.0 77.83 F 6.0 24.0 2.0 -sub-CLNC1137 ses-M06 CN 1.0 77.83 F 6.0 24.0 2.0 -sub-CLNC1137 ses-M24 77.83 F 6.0 24.0 2.0 -sub-CLNC1138 ses-M00 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M06 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M12 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M18 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M24 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M30 72.88 M 3.0 24.0 0.5 -sub-CLNC1138 ses-M36 MCI 72.88 M 3.0 24.0 0.5 -sub-CLNC1139 ses-M00 CN 1.0 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M03 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M06 CN 1.0 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M12 CN 1.0 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M18 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M24 CN 1.0 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M36 77.63 F 9.0 25.0 2.0 -sub-CLNC1139 ses-M48 CN 1.0 77.63 F 9.0 25.0 2.0 -sub-CLNC1140 ses-M00 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M06 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M12 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M18 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M24 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M30 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M36 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M42 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M48 CN 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M54 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M60 CN 1.0 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M66 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M72 CN 1.0 62.71 M 7.0 29.0 0.0 -sub-CLNC1140 ses-M78 62.71 M 7.0 29.0 0.0 -sub-CLNC1141 ses-M00 CN 1.0 73.77 F 9.0 26.0 1.0 -sub-CLNC1141 ses-M03 73.77 F 9.0 26.0 1.0 -sub-CLNC1141 ses-M06 CN 1.0 73.77 F 9.0 26.0 1.0 -sub-CLNC1141 ses-M24 CN 1.0 73.77 F 9.0 26.0 1.0 -sub-CLNC1142 ses-M00 CN 1.0 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M03 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M06 CN 1.0 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M12 CN 1.0 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M18 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M24 CN 1.0 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M30 61.63 F 9.0 27.0 1.0 -sub-CLNC1142 ses-M48 CN 1.0 61.63 F 9.0 27.0 1.0 -sub-CLNC1143 ses-M00 AD 3.0 70.75 M 3.0 23.0 1.0 -sub-CLNC1143 ses-M03 70.75 M 3.0 23.0 1.0 -sub-CLNC1143 ses-M06 AD 3.0 70.75 M 3.0 23.0 1.0 -sub-CLNC1143 ses-M12 AD 3.0 70.75 M 3.0 23.0 1.0 -sub-CLNC1143 ses-M18 70.75 M 3.0 23.0 1.0 -sub-CLNC1144 ses-M00 CN 1.0 87.25 F 5.0 29.0 0.5 -sub-CLNC1144 ses-M03 87.25 F 5.0 29.0 0.5 -sub-CLNC1144 ses-M06 CN 1.0 87.25 F 5.0 29.0 0.5 -sub-CLNC1144 ses-M12 4.0 87.25 F 5.0 29.0 0.5 -sub-CLNC1144 ses-M18 87.25 F 5.0 29.0 0.5 -sub-CLNC1144 ses-M36 MCI 2.0 87.25 F 5.0 29.0 0.5 -sub-CLNC1145 ses-M00 AD 3.0 81.07 F 2.0 28.0 0.5 -sub-CLNC1145 ses-M03 81.07 F 2.0 28.0 0.5 -sub-CLNC1145 ses-M06 AD 3.0 81.07 F 2.0 28.0 0.5 -sub-CLNC1145 ses-M12 AD 3.0 81.07 F 2.0 28.0 0.5 -sub-CLNC1145 ses-M18 81.07 F 2.0 28.0 0.5 -sub-CLNC1146 ses-M00 CN 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M06 CN 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M12 CN 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M18 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M24 CN 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M30 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M36 MCI 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M42 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M48 MCI 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M60 MCI 2.0 74.84 F 3.0 26.0 3.0 -sub-CLNC1146 ses-M72 MCI 2.0 74.84 F 3.0 26.0 3.0 -sub-CLNC1147 ses-M00 CN 1.0 79.01 M 9.0 21.0 2.0 -sub-CLNC1147 ses-M03 79.01 M 9.0 21.0 2.0 -sub-CLNC1147 ses-M06 CN 1.0 79.01 M 9.0 21.0 2.0 -sub-CLNC1147 ses-M24 CN 1.0 79.01 M 9.0 21.0 2.0 -sub-CLNC1148 ses-M00 CN 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M06 CN 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M12 CN 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M18 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M24 CN 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M30 85.39 M 3.0 28.0 3.0 -sub-CLNC1148 ses-M36 85.39 M 3.0 28.0 3.0 -sub-CLNC1149 ses-M00 MCI 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M06 AD 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M12 AD 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M18 AD 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M24 AD 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M30 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M36 AD 67.11 F 9.0 28.0 2.0 -sub-CLNC1149 ses-M42 67.11 F 9.0 28.0 2.0 -sub-CLNC1150 ses-M00 MCI 2.0 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M03 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M06 MCI 2.0 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M12 MCI 2.0 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M18 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M30 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M36 MCI 2.0 80.17 M 9.0 27.0 2.0 -sub-CLNC1150 ses-M48 MCI 2.0 80.17 M 9.0 27.0 2.0 -sub-CLNC1151 ses-M00 AD 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M06 AD 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M12 AD 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M18 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M24 AD 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M30 79.18 F 1.0 20.0 3.0 -sub-CLNC1151 ses-M36 79.18 F 1.0 20.0 3.0 -sub-CLNC1152 ses-M00 CN 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M06 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M12 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M18 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M24 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M30 74.78 M 4.0 28.0 2.0 -sub-CLNC1152 ses-M36 74.78 M 4.0 28.0 2.0 -sub-CLNC1153 ses-M00 CN 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M06 CN 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M108 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M12 CN 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M18 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M24 CN 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M30 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M36 CN 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M42 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M48 CN 1.0 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M54 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M60 CN 1.0 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M66 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M72 4.0 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M78 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M84 MCI 2.0 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M90 65.78 F 5.0 23.0 2.0 -sub-CLNC1153 ses-M96 MCI 2.0 65.78 F 5.0 23.0 2.0 -sub-CLNC1154 ses-M00 CN 1.0 82.93 F 5.0 27.0 0.5 -sub-CLNC1154 ses-M06 CN 1.0 82.93 F 5.0 27.0 0.5 -sub-CLNC1154 ses-M24 CN 1.0 82.93 F 5.0 27.0 0.5 -sub-CLNC1155 ses-M00 MCI 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M06 MCI 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M12 MCI 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M18 CN 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M24 CN 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M30 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M36 MCI 71.76 M 6.0 29.0 0.0 -sub-CLNC1155 ses-M42 71.76 M 6.0 29.0 0.0 -sub-CLNC1156 ses-M00 CN 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M06 CN 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M12 CN 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M18 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M24 CN 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M30 74.63 F 9.0 29.0 3.0 -sub-CLNC1156 ses-M36 74.63 F 9.0 29.0 3.0 -sub-CLNC1157 ses-M00 MCI 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M06 MCI 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M12 MCI 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M18 AD 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M24 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M30 70.87 M 6.0 20.0 0.5 -sub-CLNC1157 ses-M36 70.87 M 6.0 20.0 0.5 -sub-CLNC1158 ses-M00 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M06 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M12 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M18 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M24 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M30 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M36 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M42 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M48 CN 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M54 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M60 CN 1.0 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M66 79.32 M 4.0 22.0 1.0 -sub-CLNC1158 ses-M72 CN 1.0 79.32 M 4.0 22.0 1.0 -sub-CLNC1159 ses-M00 MCI 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M06 MCI 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M12 MCI 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M18 AD 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M24 AD 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M30 59.85 M 3.0 27.0 1.0 -sub-CLNC1159 ses-M36 AD 59.85 M 3.0 27.0 1.0 -sub-CLNC1160 ses-M00 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M03 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M06 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M12 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M18 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M24 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M30 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M36 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M48 75.32 F 2.0 28.0 2.0 -sub-CLNC1160 ses-M60 MCI 2.0 75.32 F 2.0 28.0 2.0 -sub-CLNC1161 ses-M00 CN 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M06 CN 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M12 CN 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M18 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M24 CN 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M30 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M36 CN 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M42 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M48 CN 1.0 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M54 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M60 CN 1.0 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M66 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M72 4.0 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M78 69.36 M 3.0 28.0 1.0 -sub-CLNC1161 ses-M90 69.36 M 3.0 28.0 1.0 -sub-CLNC1162 ses-M00 AD 3.0 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M03 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M06 AD 3.0 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M12 AD 3.0 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M18 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M24 AD 3.0 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M30 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M36 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M42 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M48 74.35 F 2.0 28.0 2.0 -sub-CLNC1162 ses-M54 74.35 F 2.0 28.0 2.0 -sub-CLNC1163 ses-M00 MCI 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M06 MCI 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M12 MCI 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M18 MCI 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M24 AD 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M30 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M36 AD 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M42 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M48 AD 3.0 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M54 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M60 AD 3.0 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M66 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M72 AD 3.0 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M78 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M84 83.9 F 2.0 26.0 0.0 -sub-CLNC1163 ses-M96 83.9 F 2.0 26.0 0.0 -sub-CLNC1164 ses-M00 MCI 2.0 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M03 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M06 MCI 2.0 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M12 MCI 2.0 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M18 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M24 MCI 2.0 76.85 F 4.0 27.0 0.0 -sub-CLNC1164 ses-M36 MCI 2.0 76.85 F 4.0 27.0 0.0 -sub-CLNC1165 ses-M00 CN 1.0 81.13 M 4.0 24.0 0.5 -sub-CLNC1165 ses-M06 CN 1.0 81.13 M 4.0 24.0 0.5 -sub-CLNC1165 ses-M12 CN 1.0 81.13 M 4.0 24.0 0.5 -sub-CLNC1165 ses-M18 81.13 M 4.0 24.0 0.5 -sub-CLNC1165 ses-M24 CN 1.0 81.13 M 4.0 24.0 0.5 -sub-CLNC1165 ses-M48 CN 1.0 81.13 M 4.0 24.0 0.5 -sub-CLNC1166 ses-M00 MCI 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M06 MCI 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M12 MCI 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M18 MCI 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M24 MCI 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M30 66.33 M 1.0 20.0 0.5 -sub-CLNC1166 ses-M36 66.33 M 1.0 20.0 0.5 -sub-CLNC1167 ses-M00 MCI 2.0 72.91 M 6.0 24.0 0.0 -sub-CLNC1167 ses-M03 72.91 M 6.0 24.0 0.0 -sub-CLNC1167 ses-M06 MCI 2.0 72.91 M 6.0 24.0 0.0 -sub-CLNC1167 ses-M12 MCI 2.0 72.91 M 6.0 24.0 0.0 -sub-CLNC1167 ses-M18 72.91 M 6.0 24.0 0.0 -sub-CLNC1168 ses-M00 MCI 2.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M03 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M06 MCI 2.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M12 MCI 2.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M18 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M24 MCI 2.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M30 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M36 MCI 2.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M48 CN 1.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1168 ses-M60 CN 1.0 82.84 F 3.0 25.0 2.0 -sub-CLNC1169 ses-M00 CN 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M06 CN 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M12 CN 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M18 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M24 CN 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M30 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M36 CN 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M42 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M48 CN 1.0 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M54 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M60 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M66 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M72 64.98 F 2.0 28.0 0.0 -sub-CLNC1169 ses-M78 64.98 F 2.0 28.0 0.0 -sub-CLNC1170 ses-M00 MCI 2.0 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M03 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M06 MCI 2.0 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M12 MCI 2.0 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M18 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M24 MCI 2.0 74.78 M 6.0 25.0 0.0 -sub-CLNC1170 ses-M36 74.78 M 6.0 25.0 0.0 -sub-CLNC1171 ses-M00 MCI 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M06 MCI 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M12 MCI 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M18 MCI 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M24 AD 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M30 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M36 AD 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M42 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M48 AD 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M54 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M60 AD 3.0 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M66 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M72 AD 3.0 67.68 F 5.0 26.0 0.0 -sub-CLNC1171 ses-M90 67.68 F 5.0 26.0 0.0 -sub-CLNC1172 ses-M00 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M03 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M06 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M12 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M18 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M24 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M30 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M36 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M42 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M48 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1172 ses-M60 MCI 2.0 78.44 M 9.0 21.0 0.5 -sub-CLNC1173 ses-M00 MCI 2.0 76.66 F 2.0 28.0 3.0 -sub-CLNC1173 ses-M03 76.66 F 2.0 28.0 3.0 -sub-CLNC1173 ses-M06 MCI 2.0 76.66 F 2.0 28.0 3.0 -sub-CLNC1173 ses-M12 MCI 2.0 76.66 F 2.0 28.0 3.0 -sub-CLNC1173 ses-M24 MCI 2.0 76.66 F 2.0 28.0 3.0 -sub-CLNC1174 ses-M00 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M03 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M06 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M12 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M18 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M24 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M30 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M36 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M48 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1174 ses-M60 MCI 2.0 79.13 F 6.0 21.0 3.0 -sub-CLNC1175 ses-M00 MCI 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M06 MCI 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M12 AD 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M18 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M24 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M30 78.95 M 8.0 25.0 0.0 -sub-CLNC1175 ses-M36 78.95 M 8.0 25.0 0.0 -sub-CLNC1176 ses-M00 MCI 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M06 CN 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M108 MCI 2.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M12 MCI 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M120 5.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M18 MCI 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M24 CN 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M30 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M36 CN 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M42 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M48 CN 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M54 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M60 4.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M66 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M72 7.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M78 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M84 4.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M90 72.19 F 5.0 20.0 2.0 -sub-CLNC1176 ses-M96 MCI 2.0 72.19 F 5.0 20.0 2.0 -sub-CLNC1177 ses-M00 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M06 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M12 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M18 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M24 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M30 75.5 F 2.0 26.0 2.0 -sub-CLNC1177 ses-M36 MCI 75.5 F 2.0 26.0 2.0 -sub-CLNC1178 ses-M00 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M03 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M06 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M12 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M18 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M24 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M36 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1178 ses-M48 MCI 2.0 74.5 F 8.0 26.0 0.5 -sub-CLNC1179 ses-M00 MCI 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M06 MCI 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M108 AD 3.0 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M12 MCI 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M18 MCI 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M24 MCI 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M30 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M36 AD 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M60 MCI 2.0 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M66 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M72 5.0 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M78 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M84 AD 3.0 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M90 81.86 M 6.0 26.0 1.0 -sub-CLNC1179 ses-M96 AD 3.0 81.86 M 6.0 26.0 1.0 -sub-CLNC1180 ses-M00 CN 1.0 71.95 F 6.0 25.0 3.0 -sub-CLNC1180 ses-M03 71.95 F 6.0 25.0 3.0 -sub-CLNC1180 ses-M06 CN 1.0 71.95 F 6.0 25.0 3.0 -sub-CLNC1180 ses-M24 CN 1.0 71.95 F 6.0 25.0 3.0 -sub-CLNC1181 ses-M00 AD 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M06 AD 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M12 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M18 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M24 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M30 81.21 M 8.0 24.0 1.0 -sub-CLNC1181 ses-M36 81.21 M 8.0 24.0 1.0 -sub-CLNC1182 ses-M00 MCI 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M06 MCI 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M12 MCI 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M18 AD 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M24 AD 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M30 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M36 AD 85.02 M 8.0 23.0 0.0 -sub-CLNC1182 ses-M48 AD 85.02 M 8.0 23.0 0.0 -sub-CLNC1183 ses-M00 CN 1.0 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M03 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M06 CN 1.0 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M12 CN 1.0 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M18 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M24 CN 1.0 75.51 M 7.0 28.0 0.0 -sub-CLNC1183 ses-M48 CN 1.0 75.51 M 7.0 28.0 0.0 -sub-CLNC1184 ses-M00 AD 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M06 AD 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M12 AD 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M18 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M24 AD 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M30 85.8 M 9.0 26.0 2.0 -sub-CLNC1184 ses-M36 85.8 M 9.0 26.0 2.0 -sub-CLNC1185 ses-M00 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M06 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M12 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M120 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M18 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M24 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M30 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M36 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M48 CN 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M60 CN 1.0 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M66 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M72 CN 1.0 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M78 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M84 CN 1.0 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M90 67.76 F 6.0 29.0 2.0 -sub-CLNC1185 ses-M96 CN 1.0 67.76 F 6.0 29.0 2.0 -sub-CLNC1186 ses-M00 MCI 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M06 MCI 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M12 MCI 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M18 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M24 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M30 64.7 F 4.0 28.0 0.0 -sub-CLNC1186 ses-M36 64.7 F 4.0 28.0 0.0 -sub-CLNC1187 ses-M00 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M03 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M06 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M12 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M18 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M24 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M30 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M36 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M42 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M48 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1187 ses-M60 MCI 2.0 65.86 M 1.0 26.0 3.0 -sub-CLNC1188 ses-M00 CN 1.0 71.96 F 1.0 22.0 2.0 -sub-CLNC1188 ses-M03 71.96 F 1.0 22.0 2.0 -sub-CLNC1188 ses-M06 CN 1.0 71.96 F 1.0 22.0 2.0 -sub-CLNC1188 ses-M12 CN 1.0 71.96 F 1.0 22.0 2.0 -sub-CLNC1188 ses-M24 CN 1.0 71.96 F 1.0 22.0 2.0 -sub-CLNC1189 ses-M00 CN 1.0 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M03 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M06 CN 1.0 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M12 CN 1.0 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M18 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M24 CN 1.0 64.04 M 9.0 22.0 2.0 -sub-CLNC1189 ses-M48 CN 1.0 64.04 M 9.0 22.0 2.0 -sub-CLNC1190 ses-M00 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M06 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M108 CN 1.0 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M12 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M18 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M24 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M30 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M36 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M42 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M48 CN 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M54 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M60 CN 1.0 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M66 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M72 CN 1.0 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M78 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M84 CN 1.0 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M90 63.99 F 8.0 27.0 0.0 -sub-CLNC1190 ses-M96 CN 1.0 63.99 F 8.0 27.0 0.0 -sub-CLNC1191 ses-M00 CN 1.0 60.77 F 6.0 24.0 2.0 -sub-CLNC1191 ses-M24 CN 1.0 60.77 F 6.0 24.0 2.0 -sub-CLNC1192 ses-M00 AD 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M06 AD 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M12 AD 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M18 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M24 AD 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M30 65.21 F 2.0 25.0 1.0 -sub-CLNC1192 ses-M36 65.21 F 2.0 25.0 1.0 -sub-CLNC1193 ses-M00 CN 1.0 70.56 M 2.0 22.0 0.5 -sub-CLNC1193 ses-M03 70.56 M 2.0 22.0 0.5 -sub-CLNC1193 ses-M24 CN 1.0 70.56 M 2.0 22.0 0.5 -sub-CLNC1194 ses-M00 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M03 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M06 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M12 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M18 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M24 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M36 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1194 ses-M48 MCI 2.0 83.61 F 2.0 21.0 1.0 -sub-CLNC1195 ses-M00 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M03 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M06 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M12 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M18 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M24 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M30 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M36 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1195 ses-M48 MCI 2.0 72.8 M 2.0 27.0 0.5 -sub-CLNC1196 ses-M00 MCI 2.0 72.68 F 5.0 20.0 0.5 -sub-CLNC1196 ses-M03 72.68 F 5.0 20.0 0.5 -sub-CLNC1196 ses-M06 MCI 2.0 72.68 F 5.0 20.0 0.5 -sub-CLNC1196 ses-M12 MCI 2.0 72.68 F 5.0 20.0 0.5 -sub-CLNC1196 ses-M18 72.68 F 5.0 20.0 0.5 -sub-CLNC1196 ses-M24 MCI 2.0 72.68 F 5.0 20.0 0.5 -sub-CLNC1197 ses-M00 AD 3.0 74.46 M 7.0 26.0 0.0 -sub-CLNC1198 ses-M00 MCI 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M06 MCI 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M12 MCI 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M18 MCI 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M24 MCI 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M30 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M36 AD 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M48 5.0 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M54 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M60 5.0 67.33 M 2.0 28.0 2.0 -sub-CLNC1198 ses-M66 67.33 M 2.0 28.0 2.0 -sub-CLNC1199 ses-M00 MCI 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M06 MCI 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M12 AD 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M18 AD 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M24 AD 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M30 66.11 M 8.0 28.0 0.5 -sub-CLNC1199 ses-M36 66.11 M 8.0 28.0 0.5 -sub-CLNC1200 ses-M00 MCI 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M06 MCI 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M108 CN 1.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M12 MCI 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M18 CN 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M24 MCI 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M30 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M36 MCI 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M42 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M48 7.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M54 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M60 CN 1.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M66 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M72 CN 1.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M78 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M84 CN 1.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M90 70.62 F 8.0 21.0 0.5 -sub-CLNC1200 ses-M96 CN 1.0 70.62 F 8.0 21.0 0.5 -sub-CLNC1201 ses-M00 AD 3.0 66.33 M 7.0 24.0 0.0 -sub-CLNC1202 ses-M00 MCI 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M06 MCI 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M12 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M18 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M24 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M30 79.94 F 8.0 20.0 0.5 -sub-CLNC1202 ses-M36 79.94 F 8.0 20.0 0.5 -sub-CLNC1203 ses-M00 AD 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M06 AD 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M12 AD 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M18 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M24 AD 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M30 64.8 F 1.0 24.0 2.0 -sub-CLNC1203 ses-M36 64.8 F 1.0 24.0 2.0 -sub-CLNC1204 ses-M00 MCI 2.0 75.9 F 4.0 29.0 0.5 -sub-CLNC1205 ses-M00 CN 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M06 CN 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M12 CN 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M18 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M24 CN 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M30 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M36 CN 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M42 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M48 CN 1.0 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M54 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M60 CN 1.0 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M66 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M72 CN 1.0 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M78 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M84 CN 1.0 65.96 F 6.0 21.0 2.0 -sub-CLNC1205 ses-M96 65.96 F 6.0 21.0 2.0 -sub-CLNC1206 ses-M00 MCI 2.0 84.04 F 8.0 23.0 3.0 -sub-CLNC1206 ses-M03 84.04 F 8.0 23.0 3.0 -sub-CLNC1206 ses-M06 MCI 2.0 84.04 F 8.0 23.0 3.0 -sub-CLNC1206 ses-M12 MCI 2.0 84.04 F 8.0 23.0 3.0 -sub-CLNC1207 ses-M00 CN 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M06 CN 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M12 CN 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M18 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M24 CN 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M30 87.13 F 8.0 29.0 0.0 -sub-CLNC1207 ses-M36 CN 87.13 F 8.0 29.0 0.0 -sub-CLNC1208 ses-M00 CN 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M06 CN 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M12 CN 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M18 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M24 CN 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M30 76.97 F 5.0 24.0 1.0 -sub-CLNC1208 ses-M36 76.97 F 5.0 24.0 1.0 -sub-CLNC1209 ses-M00 MCI 2.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M03 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M06 MCI 2.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M12 MCI 2.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M18 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M24 7.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M30 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M36 CN 1.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1209 ses-M48 CN 1.0 71.31 F 6.0 20.0 3.0 -sub-CLNC1210 ses-M00 MCI 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M06 MCI 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M12 AD 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M18 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M24 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M30 64.21 M 1.0 21.0 3.0 -sub-CLNC1210 ses-M36 64.21 M 1.0 21.0 3.0 -sub-CLNC1211 ses-M00 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M03 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M06 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M12 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M18 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M24 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M30 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M36 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M48 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1211 ses-M60 MCI 2.0 74.32 F 8.0 29.0 2.0 -sub-CLNC1212 ses-M00 AD 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M06 AD 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M12 AD 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M18 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M24 AD 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M30 71.03 F 2.0 28.0 2.0 -sub-CLNC1212 ses-M36 71.03 F 2.0 28.0 2.0 -sub-CLNC1213 ses-M00 MCI 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M06 MCI 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M12 MCI 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M18 MCI 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M24 MCI 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M30 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M36 AD 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M42 79.79 F 1.0 29.0 0.5 -sub-CLNC1213 ses-M48 AD 3.0 79.79 F 1.0 29.0 0.5 -sub-CLNC1214 ses-M00 MCI 2.0 70.08 M 3.0 24.0 1.0 -sub-CLNC1214 ses-M03 70.08 M 3.0 24.0 1.0 -sub-CLNC1214 ses-M18 70.08 M 3.0 24.0 1.0 -sub-CLNC1214 ses-M24 70.08 M 3.0 24.0 1.0 -sub-CLNC1215 ses-M00 CN 1.0 87.3 M 2.0 25.0 2.0 -sub-CLNC1215 ses-M06 CN 1.0 87.3 M 2.0 25.0 2.0 -sub-CLNC1215 ses-M24 CN 1.0 87.3 M 2.0 25.0 2.0 -sub-CLNC1216 ses-M00 CN 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M06 CN 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M12 CN 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M18 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M24 CN 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M30 80.05 M 7.0 27.0 1.0 -sub-CLNC1216 ses-M36 CN 80.05 M 7.0 27.0 1.0 -sub-CLNC1217 ses-M00 CN 1.0 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M03 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M06 CN 1.0 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M12 CN 1.0 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M18 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M24 CN 1.0 55.11 F 5.0 23.0 0.5 -sub-CLNC1217 ses-M48 CN 1.0 55.11 F 5.0 23.0 0.5 -sub-CLNC1218 ses-M00 AD 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M06 AD 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M12 AD 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M18 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M24 AD 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M30 74.47 M 6.0 24.0 0.5 -sub-CLNC1218 ses-M36 74.47 M 6.0 24.0 0.5 -sub-CLNC1219 ses-M00 CN 1.0 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M03 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M06 CN 1.0 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M12 CN 1.0 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M18 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M24 CN 1.0 78.51 F 7.0 28.0 2.0 -sub-CLNC1219 ses-M48 CN 1.0 78.51 F 7.0 28.0 2.0 -sub-CLNC1220 ses-M00 MCI 2.0 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M03 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M06 MCI 2.0 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M12 MCI 2.0 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M18 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M24 MCI 2.0 89.43 F 6.0 29.0 0.0 -sub-CLNC1220 ses-M36 MCI 2.0 89.43 F 6.0 29.0 0.0 -sub-CLNC1221 ses-M00 AD 80.29 F 8.0 21.0 1.0 -sub-CLNC1221 ses-M06 AD 80.29 F 8.0 21.0 1.0 -sub-CLNC1221 ses-M12 AD 80.29 F 8.0 21.0 1.0 -sub-CLNC1221 ses-M18 80.29 F 8.0 21.0 1.0 -sub-CLNC1221 ses-M24 AD 80.29 F 8.0 21.0 1.0 -sub-CLNC1222 ses-M00 MCI 2.0 76.1 F 1.0 22.0 3.0 -sub-CLNC1222 ses-M03 76.1 F 1.0 22.0 3.0 -sub-CLNC1222 ses-M06 MCI 2.0 76.1 F 1.0 22.0 3.0 -sub-CLNC1222 ses-M12 MCI 2.0 76.1 F 1.0 22.0 3.0 -sub-CLNC1222 ses-M18 76.1 F 1.0 22.0 3.0 -sub-CLNC1222 ses-M24 MCI 2.0 76.1 F 1.0 22.0 3.0 -sub-CLNC1223 ses-M00 AD 3.0 63.14 M 3.0 25.0 1.0 -sub-CLNC1223 ses-M03 63.14 M 3.0 25.0 1.0 -sub-CLNC1223 ses-M06 AD 3.0 63.14 M 3.0 25.0 1.0 -sub-CLNC1223 ses-M12 AD 3.0 63.14 M 3.0 25.0 1.0 -sub-CLNC1224 ses-M00 CN 1.0 76.27 M 2.0 28.0 3.0 -sub-CLNC1224 ses-M06 CN 1.0 76.27 M 2.0 28.0 3.0 -sub-CLNC1225 ses-M00 AD 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M06 AD 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M12 AD 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M18 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M24 AD 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M30 74.97 M 1.0 20.0 0.0 -sub-CLNC1225 ses-M36 74.97 M 1.0 20.0 0.0 -sub-CLNC1226 ses-M00 MCI 2.0 81.64 M 8.0 24.0 3.0 -sub-CLNC1226 ses-M06 MCI 2.0 81.64 M 8.0 24.0 3.0 -sub-CLNC1226 ses-M12 MCI 2.0 81.64 M 8.0 24.0 3.0 -sub-CLNC1226 ses-M24 5.0 81.64 M 8.0 24.0 3.0 -sub-CLNC1226 ses-M36 AD 3.0 81.64 M 8.0 24.0 3.0 -sub-CLNC1227 ses-M00 MCI 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M06 MCI 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M12 MCI 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M18 AD 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M24 AD 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M30 76.83 M 7.0 29.0 0.5 -sub-CLNC1227 ses-M36 AD 76.83 M 7.0 29.0 0.5 -sub-CLNC1228 ses-M00 MCI 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M06 MCI 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M12 AD 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M18 AD 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M24 AD 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M30 80.52 M 1.0 24.0 2.0 -sub-CLNC1228 ses-M36 AD 80.52 M 1.0 24.0 2.0 -sub-CLNC1229 ses-M00 MCI 2.0 74.59 F 1.0 20.0 3.0 -sub-CLNC1229 ses-M03 74.59 F 1.0 20.0 3.0 -sub-CLNC1229 ses-M06 MCI 2.0 74.59 F 1.0 20.0 3.0 -sub-CLNC1229 ses-M12 MCI 2.0 74.59 F 1.0 20.0 3.0 -sub-CLNC1229 ses-M24 MCI 2.0 74.59 F 1.0 20.0 3.0 -sub-CLNC1230 ses-M00 MCI 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M06 MCI 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M108 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M12 MCI 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M18 MCI 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M24 AD 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M30 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M36 AD 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M42 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M48 AD 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M54 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M60 AD 3.0 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M66 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M72 AD 3.0 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M78 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M90 70.77 M 6.0 25.0 0.0 -sub-CLNC1230 ses-M96 70.77 M 6.0 25.0 0.0 -sub-CLNC1231 ses-M00 AD 3.0 78.51 M 9.0 22.0 0.5 -sub-CLNC1231 ses-M03 78.51 M 9.0 22.0 0.5 -sub-CLNC1231 ses-M06 AD 3.0 78.51 M 9.0 22.0 0.5 -sub-CLNC1231 ses-M12 AD 3.0 78.51 M 9.0 22.0 0.5 -sub-CLNC1231 ses-M18 78.51 M 9.0 22.0 0.5 -sub-CLNC1232 ses-M00 AD 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M06 AD 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M12 AD 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M18 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M24 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M30 67.83 F 7.0 28.0 1.0 -sub-CLNC1232 ses-M36 67.83 F 7.0 28.0 1.0 -sub-CLNC1233 ses-M00 MCI 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M06 MCI 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M12 MCI 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M18 MCI 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M24 MCI 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M30 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M36 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M48 MCI 2.0 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M54 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M60 MCI 2.0 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M66 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M72 MCI 2.0 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M78 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M84 MCI 2.0 68.14 F 8.0 28.0 1.0 -sub-CLNC1233 ses-M96 5.0 68.14 F 8.0 28.0 1.0 -sub-CLNC1234 ses-M00 CN 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M06 CN 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M12 CN 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M18 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M24 CN 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M30 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M36 CN 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M42 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M48 CN 1.0 70.68 M 6.0 20.0 0.0 -sub-CLNC1234 ses-M54 70.68 M 6.0 20.0 0.0 -sub-CLNC1235 ses-M00 MCI 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M06 MCI 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M12 MCI 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M18 MCI 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M24 MCI 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M30 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M36 AD 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M42 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M48 AD 3.0 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M54 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M60 AD 3.0 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M66 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M72 AD 3.0 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M78 76.56 M 9.0 25.0 1.0 -sub-CLNC1235 ses-M90 76.56 M 9.0 25.0 1.0 -sub-CLNC1236 ses-M00 MCI 2.0 77.34 M 8.0 24.0 2.0 -sub-CLNC1236 ses-M03 77.34 M 8.0 24.0 2.0 -sub-CLNC1236 ses-M06 MCI 2.0 77.34 M 8.0 24.0 2.0 -sub-CLNC1236 ses-M12 MCI 2.0 77.34 M 8.0 24.0 2.0 -sub-CLNC1236 ses-M18 77.34 M 8.0 24.0 2.0 -sub-CLNC1237 ses-M00 MCI 2.0 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M03 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M06 MCI 2.0 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M12 MCI 2.0 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M18 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M24 MCI 2.0 70.0 M 3.0 29.0 1.0 -sub-CLNC1237 ses-M36 5.0 70.0 M 3.0 29.0 1.0 -sub-CLNC1238 ses-M00 CN 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M06 CN 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M12 CN 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M18 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M24 CN 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M30 72.42 F 7.0 24.0 2.0 -sub-CLNC1238 ses-M36 72.42 F 7.0 24.0 2.0 -sub-CLNC1239 ses-M00 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M06 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M108 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M12 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M18 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M24 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M30 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M36 MCI 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M42 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M48 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M54 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M60 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M66 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M72 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M78 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M84 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1239 ses-M96 MCI 2.0 70.1 F 6.0 21.0 0.5 -sub-CLNC1240 ses-M00 AD 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M06 AD 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M12 AD 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M18 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M24 AD 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M30 64.2 F 5.0 25.0 2.0 -sub-CLNC1240 ses-M36 64.2 F 5.0 25.0 2.0 -sub-CLNC1241 ses-M00 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M06 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M12 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M18 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M24 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M30 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M36 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M48 CN 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M60 CN 1.0 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M66 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M72 CN 1.0 72.49 M 4.0 29.0 0.0 -sub-CLNC1241 ses-M84 72.49 M 4.0 29.0 0.0 -sub-CLNC1242 ses-M00 MCI 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M06 MCI 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M12 MCI 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M18 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M24 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M30 72.86 M 5.0 26.0 2.0 -sub-CLNC1242 ses-M36 72.86 M 5.0 26.0 2.0 -sub-CLNC1243 ses-M00 MCI 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M06 MCI 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M12 MCI 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M18 AD 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M24 AD 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M30 81.06 F 6.0 27.0 0.0 -sub-CLNC1243 ses-M36 81.06 F 6.0 27.0 0.0 -sub-CLNC1244 ses-M00 AD 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M06 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M12 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M18 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M24 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M30 82.97 F 4.0 29.0 0.5 -sub-CLNC1244 ses-M36 82.97 F 4.0 29.0 0.5 -sub-CLNC1245 ses-M00 MCI 2.0 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M12 MCI 2.0 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M18 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M24 MCI 2.0 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M30 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M36 MCI 2.0 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M48 MCI 2.0 75.04 F 3.0 25.0 3.0 -sub-CLNC1245 ses-M60 75.04 F 3.0 25.0 3.0 -sub-CLNC1246 ses-M00 CN 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M06 CN 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M12 CN 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M18 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M24 CN 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M30 79.13 M 4.0 29.0 2.0 -sub-CLNC1246 ses-M36 CN 79.13 M 4.0 29.0 2.0 -sub-CLNC1247 ses-M00 AD 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M06 AD 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M12 AD 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M18 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M24 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M30 83.79 F 7.0 23.0 3.0 -sub-CLNC1247 ses-M36 83.79 F 7.0 23.0 3.0 -sub-CLNC1248 ses-M00 CN 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M06 CN 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M12 CN 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M18 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M24 CN 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M30 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M36 CN 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M42 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M48 4.0 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M54 76.75 F 4.0 20.0 1.0 -sub-CLNC1248 ses-M60 76.75 F 4.0 20.0 1.0 -sub-CLNC1249 ses-M00 CN 1.0 70.34 M 2.0 27.0 1.0 -sub-CLNC1249 ses-M06 CN 1.0 70.34 M 2.0 27.0 1.0 -sub-CLNC1249 ses-M24 70.34 M 2.0 27.0 1.0 -sub-CLNC1250 ses-M00 CN 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M06 CN 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M12 CN 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M18 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M24 CN 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M30 76.05 M 4.0 25.0 2.0 -sub-CLNC1250 ses-M36 CN 76.05 M 4.0 25.0 2.0 -sub-CLNC1251 ses-M00 CN 1.0 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M03 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M06 CN 1.0 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M12 CN 1.0 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M18 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M24 CN 1.0 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M30 82.96 M 1.0 20.0 0.0 -sub-CLNC1251 ses-M48 CN 1.0 82.96 M 1.0 20.0 0.0 -sub-CLNC1252 ses-M00 CN 1.0 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M03 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M06 CN 1.0 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M12 CN 1.0 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M18 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M24 CN 1.0 65.01 M 3.0 22.0 2.0 -sub-CLNC1252 ses-M30 65.01 M 3.0 22.0 2.0 -sub-CLNC1253 ses-M00 MCI 2.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M03 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M06 MCI 2.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M12 MCI 2.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M18 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M24 MCI 2.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M36 5.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1253 ses-M48 AD 3.0 80.92 M 2.0 25.0 1.0 -sub-CLNC1254 ses-M00 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M03 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M06 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M12 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M18 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M24 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M36 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1254 ses-M48 MCI 2.0 79.27 M 5.0 22.0 0.0 -sub-CLNC1255 ses-M00 MCI 2.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M03 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M06 7.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M12 7.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M18 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M24 CN 1.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M36 CN 1.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1255 ses-M48 CN 1.0 77.06 F 8.0 27.0 0.5 -sub-CLNC1256 ses-M00 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M03 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M06 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M12 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M18 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M24 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M36 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1256 ses-M48 MCI 2.0 81.02 M 1.0 21.0 3.0 -sub-CLNC1257 ses-M00 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M06 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M12 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M18 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M24 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M30 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M36 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M42 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M48 CN 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M54 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M60 CN 1.0 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M66 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M72 CN 1.0 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M78 72.38 F 9.0 20.0 0.5 -sub-CLNC1257 ses-M84 4.0 72.38 F 9.0 20.0 0.5 -sub-CLNC1258 ses-M00 MCI 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M06 MCI 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M12 MCI 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M18 AD 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M24 AD 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M30 78.12 M 9.0 26.0 3.0 -sub-CLNC1258 ses-M36 AD 78.12 M 9.0 26.0 3.0 -sub-CLNC1259 ses-M00 MCI 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M06 AD 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M12 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M18 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M24 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M30 76.66 M 2.0 21.0 0.0 -sub-CLNC1259 ses-M36 76.66 M 2.0 21.0 0.0 -sub-CLNC1260 ses-M00 AD 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M06 AD 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M12 AD 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M18 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M24 AD 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M30 79.94 M 2.0 23.0 2.0 -sub-CLNC1260 ses-M36 79.94 M 2.0 23.0 2.0 -sub-CLNC1261 ses-M00 MCI 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M06 MCI 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M108 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M12 AD 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M18 AD 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M24 AD 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M30 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M36 AD 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M42 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M48 AD 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M54 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M60 AD 3.0 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M66 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M72 AD 3.0 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M78 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M84 AD 3.0 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M90 72.15 F 2.0 26.0 2.0 -sub-CLNC1261 ses-M96 72.15 F 2.0 26.0 2.0 -sub-CLNC1262 ses-M00 MCI 2.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M03 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M06 MCI 2.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M12 MCI 2.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M18 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M24 5.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M36 AD 3.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1262 ses-M48 AD 3.0 76.91 M 6.0 22.0 0.0 -sub-CLNC1263 ses-M00 CN 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M06 CN 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M12 CN 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M18 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M24 CN 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M30 71.32 F 5.0 28.0 3.0 -sub-CLNC1263 ses-M36 71.32 F 5.0 28.0 3.0 -sub-CLNC1264 ses-M00 MCI 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M06 MCI 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M12 MCI 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M18 AD 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M24 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M30 64.84 M 3.0 25.0 3.0 -sub-CLNC1264 ses-M36 AD 64.84 M 3.0 25.0 3.0 -sub-CLNC1265 ses-M00 AD 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M06 AD 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M12 AD 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M18 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M24 AD 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M30 74.96 M 3.0 27.0 0.0 -sub-CLNC1265 ses-M36 74.96 M 3.0 27.0 0.0 -sub-CLNC1266 ses-M00 CN 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M06 CN 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M12 CN 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M18 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M24 CN 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M30 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M36 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M48 CN 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M60 CN 1.0 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M72 CN 1.0 75.87 F 9.0 25.0 1.0 -sub-CLNC1266 ses-M84 CN 1.0 75.87 F 9.0 25.0 1.0 -sub-CLNC1267 ses-M00 MCI 2.0 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M03 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M06 MCI 2.0 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M12 MCI 2.0 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M18 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M24 MCI 2.0 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M30 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M36 68.38 F 1.0 25.0 3.0 -sub-CLNC1267 ses-M42 68.38 F 1.0 25.0 3.0 -sub-CLNC1268 ses-M00 CN 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M06 CN 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M108 AD 3.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M12 CN 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M120 AD 3.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M18 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M24 CN 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M30 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M36 CN 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M60 5.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M72 MCI 2.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M78 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M84 5.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M90 72.41 F 8.0 26.0 0.0 -sub-CLNC1268 ses-M96 5.0 72.41 F 8.0 26.0 0.0 -sub-CLNC1269 ses-M00 CN 1.0 68.31 M 9.0 20.0 0.0 -sub-CLNC1269 ses-M03 68.31 M 9.0 20.0 0.0 -sub-CLNC1269 ses-M06 4.0 68.31 M 9.0 20.0 0.0 -sub-CLNC1269 ses-M12 MCI 2.0 68.31 M 9.0 20.0 0.0 -sub-CLNC1269 ses-M24 MCI 2.0 68.31 M 9.0 20.0 0.0 -sub-CLNC1269 ses-M36 MCI 2.0 68.31 M 9.0 20.0 0.0 -sub-CLNC1270 ses-M00 CN 1.0 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M03 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M06 CN 1.0 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M12 CN 1.0 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M18 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M24 CN 1.0 73.77 M 1.0 24.0 1.0 -sub-CLNC1270 ses-M48 CN 1.0 73.77 M 1.0 24.0 1.0 -sub-CLNC1271 ses-M00 AD 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M06 AD 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M12 AD 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M18 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M24 AD 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M30 82.29 M 9.0 25.0 3.0 -sub-CLNC1271 ses-M36 82.29 M 9.0 25.0 3.0 -sub-CLNC1272 ses-M00 AD 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M06 AD 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M12 AD 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M18 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M24 AD 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M30 72.66 M 8.0 29.0 3.0 -sub-CLNC1272 ses-M36 72.66 M 8.0 29.0 3.0 -sub-CLNC1273 ses-M00 AD 3.0 69.74 M 9.0 20.0 1.0 -sub-CLNC1273 ses-M06 AD 3.0 69.74 M 9.0 20.0 1.0 -sub-CLNC1274 ses-M00 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M03 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M06 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M12 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M18 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M24 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M30 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M36 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M48 MCI 2.0 70.13 F 9.0 27.0 0.0 -sub-CLNC1274 ses-M60 70.13 F 9.0 27.0 0.0 -sub-CLNC1275 ses-M00 CN 1.0 63.94 M 9.0 24.0 2.0 -sub-CLNC1275 ses-M03 63.94 M 9.0 24.0 2.0 -sub-CLNC1275 ses-M06 CN 1.0 63.94 M 9.0 24.0 2.0 -sub-CLNC1275 ses-M12 CN 1.0 63.94 M 9.0 24.0 2.0 -sub-CLNC1275 ses-M24 MCI 2.0 63.94 M 9.0 24.0 2.0 -sub-CLNC1276 ses-M00 MCI 2.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M03 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M06 7.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M12 7.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M18 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M24 CN 1.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M30 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M36 CN 1.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M42 80.08 M 9.0 27.0 1.0 -sub-CLNC1276 ses-M60 CN 1.0 80.08 M 9.0 27.0 1.0 -sub-CLNC1277 ses-M00 CN 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M06 CN 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M108 5.0 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M12 CN 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M18 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M24 CN 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M30 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M36 CN 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M42 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M48 4.0 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M54 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M60 MCI 2.0 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M66 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M72 MCI 2.0 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M78 87.32 M 9.0 27.0 0.5 -sub-CLNC1277 ses-M96 MCI 2.0 87.32 M 9.0 27.0 0.5 -sub-CLNC1278 ses-M00 CN 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M06 CN 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M12 CN 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M18 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M24 CN 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M30 80.98 F 7.0 22.0 0.5 -sub-CLNC1278 ses-M36 80.98 F 7.0 22.0 0.5 -sub-CLNC1279 ses-M00 CN 1.0 82.71 M 2.0 29.0 3.0 -sub-CLNC1279 ses-M06 CN 1.0 82.71 M 2.0 29.0 3.0 -sub-CLNC1279 ses-M24 CN 1.0 82.71 M 2.0 29.0 3.0 -sub-CLNC1280 ses-M00 MCI 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M06 MCI 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M12 MCI 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M18 AD 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M24 AD 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M30 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M36 AD 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M42 67.78 F 8.0 22.0 1.0 -sub-CLNC1280 ses-M48 AD 67.78 F 8.0 22.0 1.0 -sub-CLNC1281 ses-M00 MCI 2.0 94.74 M 8.0 23.0 2.0 -sub-CLNC1281 ses-M03 94.74 M 8.0 23.0 2.0 -sub-CLNC1281 ses-M06 MCI 2.0 94.74 M 8.0 23.0 2.0 -sub-CLNC1281 ses-M12 MCI 2.0 94.74 M 8.0 23.0 2.0 -sub-CLNC1281 ses-M18 94.74 M 8.0 23.0 2.0 -sub-CLNC1281 ses-M24 MCI 2.0 94.74 M 8.0 23.0 2.0 -sub-CLNC1282 ses-M00 MCI 2.0 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M03 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M06 MCI 2.0 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M12 MCI 2.0 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M18 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M24 MCI 2.0 69.35 F 1.0 27.0 2.0 -sub-CLNC1282 ses-M36 MCI 2.0 69.35 F 1.0 27.0 2.0 -sub-CLNC1283 ses-M00 MCI 2.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M03 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M06 MCI 2.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M12 MCI 2.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M18 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M24 MCI 2.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M36 MCI 2.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1283 ses-M48 5.0 75.15 F 3.0 22.0 1.0 -sub-CLNC1284 ses-M00 MCI 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M06 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M12 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M18 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M24 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M30 78.63 F 9.0 26.0 2.0 -sub-CLNC1284 ses-M36 78.63 F 9.0 26.0 2.0 -sub-CLNC1285 ses-M00 AD 3.0 89.07 F 1.0 25.0 0.5 -sub-CLNC1285 ses-M03 89.07 F 1.0 25.0 0.5 -sub-CLNC1285 ses-M06 AD 3.0 89.07 F 1.0 25.0 0.5 -sub-CLNC1285 ses-M12 AD 3.0 89.07 F 1.0 25.0 0.5 -sub-CLNC1286 ses-M00 AD 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M06 AD 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M12 AD 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M18 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M24 AD 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M30 65.5 M 5.0 21.0 0.0 -sub-CLNC1286 ses-M36 65.5 M 5.0 21.0 0.0 -sub-CLNC1287 ses-M00 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M03 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M06 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M12 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M18 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M24 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M30 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M36 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M42 83.73 F 2.0 23.0 2.0 -sub-CLNC1287 ses-M48 MCI 2.0 83.73 F 2.0 23.0 2.0 -sub-CLNC1288 ses-M00 MCI 2.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M03 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M06 MCI 2.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M12 7.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M18 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M24 CN 1.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M36 CN 1.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1288 ses-M48 CN 1.0 71.96 F 3.0 25.0 3.0 -sub-CLNC1289 ses-M00 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M03 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M06 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M12 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M18 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M24 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M30 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M36 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1289 ses-M48 MCI 2.0 76.8 M 1.0 25.0 0.5 -sub-CLNC1290 ses-M00 MCI 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M06 MCI 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M12 AD 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M18 AD 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M24 AD 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M30 75.04 M 3.0 27.0 3.0 -sub-CLNC1290 ses-M36 75.04 M 3.0 27.0 3.0 -sub-CLNC1291 ses-M00 CN 1.0 84.84 F 7.0 21.0 0.0 -sub-CLNC1291 ses-M03 84.84 F 7.0 21.0 0.0 -sub-CLNC1291 ses-M06 CN 1.0 84.84 F 7.0 21.0 0.0 -sub-CLNC1291 ses-M12 CN 1.0 84.84 F 7.0 21.0 0.0 -sub-CLNC1291 ses-M24 CN 1.0 84.84 F 7.0 21.0 0.0 -sub-CLNC1291 ses-M36 84.84 F 7.0 21.0 0.0 -sub-CLNC1292 ses-M00 MCI 2.0 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M03 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M06 MCI 2.0 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M12 MCI 2.0 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M18 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M24 MCI 2.0 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M30 77.52 F 1.0 29.0 0.0 -sub-CLNC1292 ses-M36 5.0 77.52 F 1.0 29.0 0.0 -sub-CLNC1293 ses-M00 MCI 2.0 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M03 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M06 MCI 2.0 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M12 MCI 2.0 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M18 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M24 5.0 65.72 M 6.0 22.0 3.0 -sub-CLNC1293 ses-M30 65.72 M 6.0 22.0 3.0 -sub-CLNC1294 ses-M00 CN 1.0 51.09 M 2.0 27.0 3.0 -sub-CLNC1294 ses-M03 51.09 M 2.0 27.0 3.0 -sub-CLNC1294 ses-M06 CN 1.0 51.09 M 2.0 27.0 3.0 -sub-CLNC1294 ses-M12 CN 1.0 51.09 M 2.0 27.0 3.0 -sub-CLNC1294 ses-M24 CN 1.0 51.09 M 2.0 27.0 3.0 -sub-CLNC1295 ses-M00 MCI 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M06 MCI 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M12 AD 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M18 AD 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M24 AD 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M30 76.93 F 6.0 25.0 0.5 -sub-CLNC1295 ses-M36 76.93 F 6.0 25.0 0.5 -sub-CLNC1296 ses-M00 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M03 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M06 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M12 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M18 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M24 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M30 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M36 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M48 MCI 2.0 63.09 F 2.0 25.0 1.0 -sub-CLNC1296 ses-M60 63.09 F 2.0 25.0 1.0 -sub-CLNC1297 ses-M00 AD 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M06 AD 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M12 AD 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M18 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M24 AD 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M30 77.61 F 2.0 20.0 1.0 -sub-CLNC1297 ses-M36 77.61 F 2.0 20.0 1.0 -sub-CLNC1298 ses-M00 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M06 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M108 CN 1.0 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M12 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M18 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M24 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M30 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M36 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M48 CN 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M60 CN 1.0 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M66 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M72 CN 1.0 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M78 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M84 CN 1.0 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M90 70.7 M 4.0 21.0 0.0 -sub-CLNC1298 ses-M96 CN 1.0 70.7 M 4.0 21.0 0.0 -sub-CLNC1299 ses-M00 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M03 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M06 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M12 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M18 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M24 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M36 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1299 ses-M48 MCI 2.0 67.94 M 8.0 27.0 1.0 -sub-CLNC1300 ses-M00 MCI 2.0 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M03 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M06 MCI 2.0 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M12 MCI 2.0 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M18 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M24 5.0 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M36 5.0 71.83 F 1.0 25.0 0.0 -sub-CLNC1300 ses-M48 71.83 F 1.0 25.0 0.0 -sub-CLNC1301 ses-M00 MCI 2.0 80.96 M 2.0 26.0 2.0 -sub-CLNC1301 ses-M03 80.96 M 2.0 26.0 2.0 -sub-CLNC1301 ses-M06 MCI 2.0 80.96 M 2.0 26.0 2.0 -sub-CLNC1301 ses-M12 MCI 2.0 80.96 M 2.0 26.0 2.0 -sub-CLNC1301 ses-M24 MCI 2.0 80.96 M 2.0 26.0 2.0 -sub-CLNC1301 ses-M36 MCI 2.0 80.96 M 2.0 26.0 2.0 -sub-CLNC1302 ses-M00 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M03 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M06 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M12 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M18 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M24 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M30 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M36 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M42 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M48 MCI 2.0 66.69 M 9.0 25.0 3.0 -sub-CLNC1302 ses-M60 66.69 M 9.0 25.0 3.0 -sub-CLNC1303 ses-M00 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M03 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M06 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M12 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M18 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M24 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M30 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M36 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M42 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M48 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1303 ses-M60 MCI 2.0 81.36 F 2.0 24.0 1.0 -sub-CLNC1304 ses-M00 MCI 2.0 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M03 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M06 MCI 2.0 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M12 MCI 2.0 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M18 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M24 MCI 2.0 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M30 84.64 M 6.0 28.0 0.0 -sub-CLNC1304 ses-M48 MCI 2.0 84.64 M 6.0 28.0 0.0 -sub-CLNC1305 ses-M00 AD 3.0 79.11 F 1.0 23.0 2.0 -sub-CLNC1305 ses-M03 79.11 F 1.0 23.0 2.0 -sub-CLNC1306 ses-M00 MCI 2.0 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M06 MCI 2.0 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M12 MCI 2.0 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M18 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M24 MCI 2.0 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M30 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M36 MCI 2.0 73.45 F 3.0 20.0 0.5 -sub-CLNC1306 ses-M42 73.45 F 3.0 20.0 0.5 -sub-CLNC1307 ses-M00 AD 3.0 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M03 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M06 AD 3.0 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M12 AD 3.0 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M18 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M24 AD 3.0 67.53 F 5.0 25.0 3.0 -sub-CLNC1307 ses-M30 67.53 F 5.0 25.0 3.0 -sub-CLNC1308 ses-M00 MCI 2.0 71.02 M 4.0 26.0 3.0 -sub-CLNC1308 ses-M03 71.02 M 4.0 26.0 3.0 -sub-CLNC1308 ses-M06 MCI 2.0 71.02 M 4.0 26.0 3.0 -sub-CLNC1308 ses-M12 MCI 2.0 71.02 M 4.0 26.0 3.0 -sub-CLNC1308 ses-M36 MCI 2.0 71.02 M 4.0 26.0 3.0 -sub-CLNC1308 ses-M60 MCI 2.0 71.02 M 4.0 26.0 3.0 -sub-CLNC1309 ses-M00 CN 1.0 74.88 F 6.0 28.0 0.0 -sub-CLNC1309 ses-M03 74.88 F 6.0 28.0 0.0 -sub-CLNC1309 ses-M06 4.0 74.88 F 6.0 28.0 0.0 -sub-CLNC1310 ses-M00 MCI 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M06 MCI 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M108 AD 3.0 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M12 MCI 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M18 MCI 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M24 MCI 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M30 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M36 AD 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M42 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M48 AD 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M54 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M60 AD 3.0 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M72 AD 3.0 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M78 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M84 AD 3.0 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M90 72.69 M 5.0 23.0 1.0 -sub-CLNC1310 ses-M96 AD 3.0 72.69 M 5.0 23.0 1.0 -sub-CLNC1311 ses-M00 MCI 2.0 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M03 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M06 MCI 2.0 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M12 MCI 2.0 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M18 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M24 MCI 2.0 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M30 82.27 F 1.0 23.0 1.0 -sub-CLNC1311 ses-M36 MCI 2.0 82.27 F 1.0 23.0 1.0 -sub-CLNC1312 ses-M00 CN 1.0 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M03 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M06 CN 1.0 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M12 CN 1.0 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M18 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M24 4.0 65.95 F 7.0 21.0 3.0 -sub-CLNC1312 ses-M36 MCI 2.0 65.95 F 7.0 21.0 3.0 -sub-CLNC1313 ses-M00 CN 1.0 64.79 F 5.0 23.0 3.0 -sub-CLNC1313 ses-M03 64.79 F 5.0 23.0 3.0 -sub-CLNC1313 ses-M06 CN 1.0 64.79 F 5.0 23.0 3.0 -sub-CLNC1313 ses-M24 CN 1.0 64.79 F 5.0 23.0 3.0 -sub-CLNC1314 ses-M00 AD 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M06 AD 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M12 AD 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M18 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M24 AD 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M30 61.08 M 6.0 22.0 0.5 -sub-CLNC1314 ses-M36 61.08 M 6.0 22.0 0.5 -sub-CLNC1315 ses-M00 MCI 2.0 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M03 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M06 MCI 2.0 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M12 MCI 2.0 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M18 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M24 75.35 M 5.0 21.0 0.0 -sub-CLNC1315 ses-M30 75.35 M 5.0 21.0 0.0 -sub-CLNC1316 ses-M00 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M03 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M06 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M12 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M18 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M24 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M36 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1316 ses-M48 MCI 2.0 83.02 F 9.0 24.0 1.0 -sub-CLNC1317 ses-M00 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M06 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M108 MCI 2.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M12 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M120 5.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M18 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M24 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M30 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M36 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M48 MCI 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M60 MCI 2.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M66 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M72 MCI 2.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M78 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M84 MCI 2.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M90 73.66 M 9.0 22.0 0.0 -sub-CLNC1317 ses-M96 MCI 2.0 73.66 M 9.0 22.0 0.0 -sub-CLNC1318 ses-M00 MCI 2.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M03 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M06 5.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M12 MCI 2.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M18 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M24 AD 3.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M36 5.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1318 ses-M48 AD 3.0 66.26 M 2.0 23.0 1.0 -sub-CLNC1319 ses-M00 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M06 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M108 AD 3.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M12 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M18 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M24 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M36 MCI 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M42 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M48 5.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M54 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M60 AD 3.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M66 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M72 AD 3.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M78 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M84 AD 3.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M90 79.84 M 7.0 20.0 1.0 -sub-CLNC1319 ses-M96 AD 3.0 79.84 M 7.0 20.0 1.0 -sub-CLNC1320 ses-M00 MCI 2.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M03 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M06 MCI 2.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M12 MCI 2.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M18 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M24 MCI 2.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M36 MCI 2.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1320 ses-M48 7.0 81.12 M 6.0 23.0 1.0 -sub-CLNC1321 ses-M00 MCI 2.0 86.23 F 1.0 25.0 1.0 -sub-CLNC1321 ses-M03 86.23 F 1.0 25.0 1.0 -sub-CLNC1321 ses-M06 MCI 2.0 86.23 F 1.0 25.0 1.0 -sub-CLNC1321 ses-M12 MCI 2.0 86.23 F 1.0 25.0 1.0 -sub-CLNC1321 ses-M24 MCI 2.0 86.23 F 1.0 25.0 1.0 -sub-CLNC1321 ses-M48 MCI 2.0 86.23 F 1.0 25.0 1.0 -sub-CLNC1322 ses-M00 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M06 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M12 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M18 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M24 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M30 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M36 MCI 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M48 5.0 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M54 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M60 AD 3.0 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M66 72.72 F 1.0 22.0 0.0 -sub-CLNC1322 ses-M72 72.72 F 1.0 22.0 0.0 -sub-CLNC1323 ses-M00 MCI 2.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M03 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M06 MCI 2.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M12 MCI 2.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M18 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M24 MCI 2.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M30 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M36 MCI 2.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1323 ses-M48 7.0 79.03 F 9.0 26.0 0.5 -sub-CLNC1324 ses-M00 MCI 66.75 M 3.0 25.0 1.0 -sub-CLNC1324 ses-M06 AD 66.75 M 3.0 25.0 1.0 -sub-CLNC1325 ses-M00 MCI 2.0 70.0 F 4.0 23.0 0.5 -sub-CLNC1326 ses-M00 CN 1.0 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M03 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M06 CN 1.0 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M12 CN 1.0 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M18 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M24 CN 1.0 76.41 M 2.0 27.0 2.0 -sub-CLNC1326 ses-M48 CN 1.0 76.41 M 2.0 27.0 2.0 -sub-CLNC1327 ses-M00 MCI 2.0 76.19 F 4.0 23.0 3.0 -sub-CLNC1327 ses-M03 76.19 F 4.0 23.0 3.0 -sub-CLNC1327 ses-M06 MCI 2.0 76.19 F 4.0 23.0 3.0 -sub-CLNC1327 ses-M12 MCI 2.0 76.19 F 4.0 23.0 3.0 -sub-CLNC1327 ses-M18 76.19 F 4.0 23.0 3.0 -sub-CLNC1327 ses-M24 MCI 2.0 76.19 F 4.0 23.0 3.0 -sub-CLNC1328 ses-M00 CN 1.0 80.49 M 2.0 21.0 3.0 -sub-CLNC1328 ses-M03 80.49 M 2.0 21.0 3.0 -sub-CLNC1328 ses-M06 CN 1.0 80.49 M 2.0 21.0 3.0 -sub-CLNC1328 ses-M12 CN 1.0 80.49 M 2.0 21.0 3.0 -sub-CLNC1328 ses-M18 80.49 M 2.0 21.0 3.0 -sub-CLNC1329 ses-M00 MCI 2.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M03 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M06 MCI 2.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M12 MCI 2.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M18 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M24 MCI 2.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M36 MCI 2.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1329 ses-M48 AD 3.0 91.91 F 7.0 29.0 2.0 -sub-CLNC1330 ses-M00 CN 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M06 CN 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M12 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M18 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M24 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M30 68.47 F 4.0 24.0 2.0 -sub-CLNC1330 ses-M36 68.47 F 4.0 24.0 2.0 -sub-CLNC1331 ses-M00 AD 3.0 71.2 F 3.0 20.0 3.0 -sub-CLNC1331 ses-M03 71.2 F 3.0 20.0 3.0 -sub-CLNC1331 ses-M06 AD 3.0 71.2 F 3.0 20.0 3.0 -sub-CLNC1331 ses-M12 AD 3.0 71.2 F 3.0 20.0 3.0 -sub-CLNC1332 ses-M00 MCI 2.0 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M03 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M06 MCI 2.0 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M12 MCI 2.0 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M18 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M24 5.0 65.64 F 3.0 23.0 0.0 -sub-CLNC1332 ses-M36 AD 3.0 65.64 F 3.0 23.0 0.0 -sub-CLNC1333 ses-M00 CN 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M06 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M12 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M18 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M24 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M30 67.89 M 1.0 24.0 0.5 -sub-CLNC1333 ses-M36 67.89 M 1.0 24.0 0.5 -sub-CLNC1334 ses-M00 MCI 2.0 71.23 M 8.0 26.0 1.0 -sub-CLNC1334 ses-M03 71.23 M 8.0 26.0 1.0 -sub-CLNC1334 ses-M06 MCI 2.0 71.23 M 8.0 26.0 1.0 -sub-CLNC1334 ses-M12 MCI 2.0 71.23 M 8.0 26.0 1.0 -sub-CLNC1334 ses-M18 71.23 M 8.0 26.0 1.0 -sub-CLNC1334 ses-M24 MCI 2.0 71.23 M 8.0 26.0 1.0 -sub-CLNC1335 ses-M00 MCI 2.0 69.65 F 1.0 29.0 1.0 -sub-CLNC1335 ses-M03 69.65 F 1.0 29.0 1.0 -sub-CLNC1335 ses-M06 MCI 2.0 69.65 F 1.0 29.0 1.0 -sub-CLNC1335 ses-M12 MCI 2.0 69.65 F 1.0 29.0 1.0 -sub-CLNC1335 ses-M18 69.65 F 1.0 29.0 1.0 -sub-CLNC1336 ses-M00 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M03 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M06 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M12 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M18 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M24 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M36 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1336 ses-M48 MCI 2.0 85.12 M 5.0 27.0 0.0 -sub-CLNC1337 ses-M00 AD 3.0 82.97 M 2.0 28.0 1.0 -sub-CLNC1337 ses-M06 AD 3.0 82.97 M 2.0 28.0 1.0 -sub-CLNC1338 ses-M00 MCI 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M06 AD 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M12 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M18 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M24 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M30 80.52 F 6.0 20.0 1.0 -sub-CLNC1338 ses-M36 80.52 F 6.0 20.0 1.0 -sub-CLNC1339 ses-M00 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M03 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M06 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M12 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M18 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M24 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M30 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M36 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1339 ses-M48 MCI 2.0 71.26 F 4.0 23.0 0.5 -sub-CLNC1340 ses-M00 AD 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M06 AD 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M12 AD 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M18 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M24 AD 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M30 51.34 M 6.0 21.0 0.0 -sub-CLNC1340 ses-M36 51.34 M 6.0 21.0 0.0 -sub-CLNC1341 ses-M00 CN 1.0 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M06 CN 1.0 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M12 CN 1.0 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M18 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M24 CN 1.0 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M30 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M36 CN 1.0 79.07 M 3.0 24.0 1.0 -sub-CLNC1341 ses-M48 79.07 M 3.0 24.0 1.0 -sub-CLNC1342 ses-M00 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M06 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M108 AD 3.0 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M12 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M18 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M24 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M30 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M36 MCI 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M42 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M48 MCI 2.0 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M54 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M72 MCI 2.0 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M78 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M84 MCI 2.0 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M90 73.81 F 5.0 28.0 0.5 -sub-CLNC1342 ses-M96 5.0 73.81 F 5.0 28.0 0.5 -sub-CLNC1343 ses-M00 CN 1.0 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M03 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M06 CN 1.0 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M12 CN 1.0 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M18 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M24 CN 1.0 81.39 M 4.0 26.0 0.5 -sub-CLNC1343 ses-M48 CN 1.0 81.39 M 4.0 26.0 0.5 -sub-CLNC1344 ses-M00 MCI 2.0 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M03 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M06 5.0 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M12 AD 3.0 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M18 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M24 AD 3.0 69.88 M 6.0 28.0 2.0 -sub-CLNC1344 ses-M36 AD 3.0 69.88 M 6.0 28.0 2.0 -sub-CLNC1345 ses-M00 MCI 2.0 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M03 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M06 MCI 2.0 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M12 MCI 2.0 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M18 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M24 5.0 87.14 F 4.0 29.0 2.0 -sub-CLNC1345 ses-M36 AD 3.0 87.14 F 4.0 29.0 2.0 -sub-CLNC1346 ses-M00 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M06 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M12 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M18 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M24 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M30 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M36 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M42 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M48 CN 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M60 CN 1.0 80.31 F 8.0 27.0 2.0 -sub-CLNC1346 ses-M66 80.31 F 8.0 27.0 2.0 -sub-CLNC1347 ses-M00 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M06 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M12 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M18 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M24 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M30 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M36 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M42 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M48 MCI 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M72 MCI 2.0 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M78 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M84 MCI 2.0 72.79 M 7.0 23.0 0.5 -sub-CLNC1347 ses-M96 MCI 2.0 72.79 M 7.0 23.0 0.5 -sub-CLNC1348 ses-M00 CN 1.0 72.92 M 8.0 26.0 0.5 -sub-CLNC1348 ses-M06 CN 1.0 72.92 M 8.0 26.0 0.5 -sub-CLNC1348 ses-M24 72.92 M 8.0 26.0 0.5 -sub-CLNC1349 ses-M00 MCI 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M06 MCI 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M12 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M18 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M24 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M30 75.94 M 4.0 20.0 0.0 -sub-CLNC1349 ses-M36 75.94 M 4.0 20.0 0.0 -sub-CLNC1350 ses-M00 AD 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M06 AD 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M12 AD 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M18 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M24 AD 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M30 71.03 M 4.0 21.0 0.0 -sub-CLNC1350 ses-M36 71.03 M 4.0 21.0 0.0 -sub-CLNC1351 ses-M00 MCI 2.0 70.39 M 7.0 29.0 2.0 -sub-CLNC1351 ses-M06 MCI 2.0 70.39 M 7.0 29.0 2.0 -sub-CLNC1351 ses-M12 MCI 2.0 70.39 M 7.0 29.0 2.0 -sub-CLNC1351 ses-M18 70.39 M 7.0 29.0 2.0 -sub-CLNC1351 ses-M24 MCI 2.0 70.39 M 7.0 29.0 2.0 -sub-CLNC1351 ses-M36 MCI 2.0 70.39 M 7.0 29.0 2.0 -sub-CLNC1352 ses-M00 MCI 2.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M03 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M06 MCI 2.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M12 MCI 2.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M18 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M24 MCI 2.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M30 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M36 MCI 2.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M48 7.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1352 ses-M60 CN 1.0 65.99 M 4.0 20.0 3.0 -sub-CLNC1353 ses-M00 AD 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M06 AD 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M12 AD 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M18 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M24 AD 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M30 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M36 69.98 F 4.0 22.0 1.0 -sub-CLNC1353 ses-M72 69.98 F 4.0 22.0 1.0 -sub-CLNC1354 ses-M00 MCI 2.0 73.68 M 9.0 27.0 0.0 -sub-CLNC1355 ses-M00 CN 1.0 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M03 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M06 CN 1.0 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M12 CN 1.0 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M18 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M24 CN 1.0 78.22 M 5.0 23.0 0.5 -sub-CLNC1355 ses-M48 CN 1.0 78.22 M 5.0 23.0 0.5 -sub-CLNC1356 ses-M00 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M06 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M12 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M18 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M24 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M30 68.3 M 7.0 24.0 0.5 -sub-CLNC1356 ses-M36 MCI 68.3 M 7.0 24.0 0.5 -sub-CLNC1357 ses-M00 AD 3.0 62.63 M 9.0 23.0 2.0 -sub-CLNC1357 ses-M03 62.63 M 9.0 23.0 2.0 -sub-CLNC1357 ses-M06 AD 3.0 62.63 M 9.0 23.0 2.0 -sub-CLNC1358 ses-M00 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M06 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M12 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M18 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M24 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M30 71.68 M 2.0 24.0 0.0 -sub-CLNC1358 ses-M36 MCI 71.68 M 2.0 24.0 0.0 -sub-CLNC1359 ses-M00 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M03 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M06 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M12 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M18 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M24 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M36 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1359 ses-M48 MCI 2.0 76.61 M 4.0 22.0 0.0 -sub-CLNC1360 ses-M00 AD 3.0 68.09 M 5.0 28.0 2.0 -sub-CLNC1360 ses-M06 AD 3.0 68.09 M 5.0 28.0 2.0 -sub-CLNC1361 ses-M00 MCI 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M06 CN 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M12 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M18 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M24 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M30 78.96 M 6.0 22.0 1.0 -sub-CLNC1361 ses-M36 78.96 M 6.0 22.0 1.0 -sub-CLNC1362 ses-M00 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M03 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M06 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M12 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M18 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M24 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M30 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M36 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M42 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M48 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1362 ses-M60 MCI 2.0 66.45 F 1.0 20.0 0.5 -sub-CLNC1363 ses-M00 AD 3.0 84.0 F 9.0 28.0 3.0 -sub-CLNC1363 ses-M06 AD 3.0 84.0 F 9.0 28.0 3.0 -sub-CLNC1363 ses-M12 AD 3.0 84.0 F 9.0 28.0 3.0 -sub-CLNC1364 ses-M00 AD 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M06 AD 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M12 AD 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M18 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M24 AD 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M30 55.79 M 1.0 25.0 0.0 -sub-CLNC1364 ses-M36 55.79 M 1.0 25.0 0.0 -sub-CLNC1365 ses-M00 CN 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M06 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M12 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M18 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M24 CN 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M30 72.08 M 5.0 28.0 2.0 -sub-CLNC1365 ses-M36 CN 72.08 M 5.0 28.0 2.0 -sub-CLNC1366 ses-M00 AD 3.0 74.8 F 2.0 24.0 1.0 -sub-CLNC1366 ses-M06 AD 3.0 74.8 F 2.0 24.0 1.0 -sub-CLNC1367 ses-M00 AD 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M06 AD 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M12 AD 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M18 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M24 AD 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M30 63.64 M 1.0 24.0 3.0 -sub-CLNC1367 ses-M36 63.64 M 1.0 24.0 3.0 -sub-CLNC1368 ses-M00 MCI 2.0 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M03 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M06 MCI 2.0 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M12 MCI 2.0 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M18 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M24 MCI 2.0 68.23 F 1.0 21.0 0.0 -sub-CLNC1368 ses-M36 MCI 2.0 68.23 F 1.0 21.0 0.0 -sub-CLNC1369 ses-M00 CN 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M06 CN 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M12 CN 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M18 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M24 CN 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M30 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M36 MCI 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M42 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M48 MCI 2.0 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M54 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M60 MCI 2.0 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M66 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M72 5.0 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M78 65.92 M 3.0 26.0 0.0 -sub-CLNC1369 ses-M90 65.92 M 3.0 26.0 0.0 -sub-CLNC1370 ses-M00 AD 3.0 73.25 M 9.0 22.0 3.0 -sub-CLNC1370 ses-M06 AD 3.0 73.25 M 9.0 22.0 3.0 -sub-CLNC1370 ses-M12 AD 3.0 73.25 M 9.0 22.0 3.0 -sub-CLNC1371 ses-M00 CN 1.0 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M03 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M06 CN 1.0 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M12 CN 1.0 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M18 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M24 CN 1.0 79.64 M 3.0 25.0 0.5 -sub-CLNC1371 ses-M48 CN 1.0 79.64 M 3.0 25.0 0.5 -sub-CLNC1372 ses-M00 MCI 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M06 MCI 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M12 MCI 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M18 MCI 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M24 AD 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M30 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M36 AD 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M48 AD 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M60 AD 3.0 73.75 F 7.0 23.0 3.0 -sub-CLNC1372 ses-M72 AD 3.0 73.75 F 7.0 23.0 3.0 -sub-CLNC1373 ses-M00 AD 89.56 M 8.0 21.0 1.0 -sub-CLNC1373 ses-M06 AD 89.56 M 8.0 21.0 1.0 -sub-CLNC1374 ses-M00 AD 3.0 63.64 F 5.0 24.0 2.0 -sub-CLNC1374 ses-M03 63.64 F 5.0 24.0 2.0 -sub-CLNC1374 ses-M06 AD 3.0 63.64 F 5.0 24.0 2.0 -sub-CLNC1374 ses-M12 AD 3.0 63.64 F 5.0 24.0 2.0 -sub-CLNC1374 ses-M18 63.64 F 5.0 24.0 2.0 -sub-CLNC1375 ses-M00 CN 1.0 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M03 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M06 CN 1.0 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M12 CN 1.0 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M18 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M24 CN 1.0 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M30 79.78 M 5.0 26.0 1.0 -sub-CLNC1375 ses-M48 CN 1.0 79.78 M 5.0 26.0 1.0 -sub-CLNC1376 ses-M00 AD 3.0 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M03 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M06 AD 3.0 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M12 AD 3.0 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M18 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M24 AD 3.0 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M30 69.97 F 1.0 24.0 2.0 -sub-CLNC1376 ses-M36 69.97 F 1.0 24.0 2.0 -sub-CLNC1377 ses-M00 MCI 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M06 MCI 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M108 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M12 AD 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M18 AD 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M24 AD 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M30 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M36 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M42 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M48 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M60 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M66 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M72 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M78 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M84 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1377 ses-M96 AD 3.0 75.79 M 4.0 29.0 1.0 -sub-CLNC1378 ses-M00 MCI 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M06 MCI 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M12 MCI 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M18 MCI 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M24 MCI 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M30 70.06 M 2.0 21.0 1.0 -sub-CLNC1378 ses-M36 AD 70.06 M 2.0 21.0 1.0 -sub-CLNC1379 ses-M00 MCI 2.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M03 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M06 MCI 2.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M12 MCI 2.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M18 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M24 MCI 2.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M30 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M36 CN 1.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M42 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M48 MCI 2.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1379 ses-M60 5.0 72.19 F 1.0 26.0 3.0 -sub-CLNC1380 ses-M00 MCI 2.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M03 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M06 MCI 2.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M12 MCI 2.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M18 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M24 MCI 2.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M36 7.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1380 ses-M48 4.0 58.66 M 5.0 22.0 3.0 -sub-CLNC1381 ses-M00 AD 3.0 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M03 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M06 AD 3.0 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M12 AD 3.0 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M18 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M24 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M30 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M36 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M42 86.05 M 1.0 20.0 1.0 -sub-CLNC1381 ses-M48 86.05 M 1.0 20.0 1.0 -sub-CLNC1382 ses-M00 CN 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M06 CN 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M12 CN 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M18 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M24 CN 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M30 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M36 CN 73.0 M 9.0 22.0 0.0 -sub-CLNC1382 ses-M42 73.0 M 9.0 22.0 0.0 -sub-CLNC1383 ses-M00 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M06 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M12 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M18 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M24 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M30 70.32 F 9.0 26.0 0.5 -sub-CLNC1383 ses-M36 MCI 70.32 F 9.0 26.0 0.5 -sub-CLNC1384 ses-M00 MCI 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M06 MCI 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M12 MCI 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M18 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M24 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M30 77.83 F 4.0 29.0 2.0 -sub-CLNC1384 ses-M36 77.83 F 4.0 29.0 2.0 -sub-CLNC1385 ses-M00 MCI 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M06 MCI 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M12 AD 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M18 AD 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M24 AD 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M30 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M36 AD 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M48 AD 3.0 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M54 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M60 AD 3.0 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M66 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M72 AD 3.0 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M78 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M84 AD 3.0 65.01 F 2.0 20.0 1.0 -sub-CLNC1385 ses-M90 65.01 F 2.0 20.0 1.0 -sub-CLNC1386 ses-M00 MCI 2.0 69.99 M 3.0 28.0 0.5 -sub-CLNC1386 ses-M03 69.99 M 3.0 28.0 0.5 -sub-CLNC1386 ses-M18 69.99 M 3.0 28.0 0.5 -sub-CLNC1387 ses-M00 CN 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M06 CN 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M12 CN 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M120 4.0 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M18 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M24 CN 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M30 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M36 CN 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M72 CN 1.0 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M78 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M84 CN 1.0 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M90 85.57 M 1.0 25.0 0.0 -sub-CLNC1387 ses-M96 CN 1.0 85.57 M 1.0 25.0 0.0 -sub-CLNC1388 ses-M00 MCI 2.0 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M03 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M06 MCI 2.0 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M12 MCI 2.0 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M24 MCI 2.0 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M30 73.89 M 5.0 22.0 2.0 -sub-CLNC1388 ses-M36 MCI 2.0 73.89 M 5.0 22.0 2.0 -sub-CLNC1389 ses-M00 MCI 2.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M03 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M06 MCI 2.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M12 MCI 2.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M18 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M24 5.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M30 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M36 AD 3.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1389 ses-M48 AD 3.0 71.96 M 5.0 23.0 0.5 -sub-CLNC1390 ses-M00 MCI 2.0 67.8 F 5.0 26.0 0.0 -sub-CLNC1390 ses-M03 67.8 F 5.0 26.0 0.0 -sub-CLNC1390 ses-M06 MCI 2.0 67.8 F 5.0 26.0 0.0 -sub-CLNC1390 ses-M12 MCI 2.0 67.8 F 5.0 26.0 0.0 -sub-CLNC1390 ses-M36 5.0 67.8 F 5.0 26.0 0.0 -sub-CLNC1390 ses-M48 67.8 F 5.0 26.0 0.0 -sub-CLNC1391 ses-M00 MCI 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M06 MCI 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M12 AD 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M18 AD 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M24 AD 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M30 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M36 AD 62.14 M 8.0 29.0 1.0 -sub-CLNC1391 ses-M48 AD 62.14 M 8.0 29.0 1.0 -sub-CLNC1392 ses-M00 AD 3.0 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M03 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M06 AD 3.0 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M12 AD 3.0 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M18 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M24 AD 3.0 78.64 F 8.0 29.0 0.5 -sub-CLNC1392 ses-M30 78.64 F 8.0 29.0 0.5 -sub-CLNC1393 ses-M00 CN 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M06 CN 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M12 CN 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M18 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M24 CN 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M30 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M36 CN 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M42 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M48 4.0 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M54 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M60 MCI 2.0 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M66 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M72 5.0 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M78 78.38 M 2.0 24.0 3.0 -sub-CLNC1393 ses-M90 78.38 M 2.0 24.0 3.0 -sub-CLNC1394 ses-M00 CN 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M06 CN 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M12 CN 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M18 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M24 CN 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M30 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M36 CN 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M42 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M48 CN 1.0 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M54 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M60 4.0 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M66 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M72 MCI 2.0 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M78 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M84 MCI 2.0 71.25 F 6.0 29.0 2.0 -sub-CLNC1394 ses-M96 5.0 71.25 F 6.0 29.0 2.0 -sub-CLNC1395 ses-M00 MCI 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M06 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M12 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M18 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M24 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M30 74.69 M 7.0 28.0 1.0 -sub-CLNC1395 ses-M36 74.69 M 7.0 28.0 1.0 -sub-CLNC1396 ses-M00 MCI 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M06 MCI 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M12 MCI 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M18 MCI 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M24 MCI 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M30 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M36 AD 61.96 M 6.0 23.0 0.0 -sub-CLNC1396 ses-M42 61.96 M 6.0 23.0 0.0 -sub-CLNC1397 ses-M00 MCI 2.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M03 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M06 MCI 2.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M12 MCI 2.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M24 MCI 2.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M48 MCI 2.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1397 ses-M60 7.0 82.32 F 4.0 26.0 2.0 -sub-CLNC1398 ses-M00 MCI 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M06 AD 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M12 AD 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M18 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M24 AD 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M30 66.47 M 9.0 29.0 2.0 -sub-CLNC1398 ses-M36 AD 66.47 M 9.0 29.0 2.0 -sub-CLNC1399 ses-M00 CN 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M06 CN 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M108 MCI 2.0 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M12 CN 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M18 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M24 CN 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M30 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M36 CN 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M48 CN 1.0 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M54 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M66 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M72 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M84 4.0 80.52 F 9.0 28.0 0.5 -sub-CLNC1399 ses-M96 MCI 2.0 80.52 F 9.0 28.0 0.5 -sub-CLNC1400 ses-M00 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M03 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M06 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M12 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M18 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M24 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M30 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M36 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M42 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M48 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1400 ses-M60 MCI 2.0 74.12 F 4.0 25.0 1.0 -sub-CLNC1401 ses-M00 AD 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M06 AD 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M12 AD 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M18 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M24 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M30 57.8 F 1.0 23.0 3.0 -sub-CLNC1401 ses-M36 57.8 F 1.0 23.0 3.0 -sub-CLNC1402 ses-M00 MCI 2.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M03 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M06 MCI 2.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M12 7.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M18 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M24 CN 1.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M30 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M36 4.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1402 ses-M48 MCI 2.0 74.66 M 8.0 28.0 0.0 -sub-CLNC1403 ses-M00 AD 3.0 68.34 F 1.0 28.0 0.5 -sub-CLNC1403 ses-M03 68.34 F 1.0 28.0 0.5 -sub-CLNC1403 ses-M06 AD 3.0 68.34 F 1.0 28.0 0.5 -sub-CLNC1403 ses-M12 AD 3.0 68.34 F 1.0 28.0 0.5 -sub-CLNC1403 ses-M18 68.34 F 1.0 28.0 0.5 -sub-CLNC1403 ses-M24 AD 3.0 68.34 F 1.0 28.0 0.5 -sub-CLNC1404 ses-M00 AD 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M06 AD 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M12 AD 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M18 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M24 AD 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M30 78.03 M 6.0 28.0 1.0 -sub-CLNC1404 ses-M36 78.03 M 6.0 28.0 1.0 -sub-CLNC1405 ses-M00 CN 1.0 69.29 F 5.0 20.0 1.0 -sub-CLNC1405 ses-M03 69.29 F 5.0 20.0 1.0 -sub-CLNC1405 ses-M06 CN 1.0 69.29 F 5.0 20.0 1.0 -sub-CLNC1405 ses-M12 CN 1.0 69.29 F 5.0 20.0 1.0 -sub-CLNC1405 ses-M18 69.29 F 5.0 20.0 1.0 -sub-CLNC1405 ses-M24 CN 1.0 69.29 F 5.0 20.0 1.0 -sub-CLNC1406 ses-M00 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M03 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M06 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M12 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M18 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M24 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M30 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M36 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1406 ses-M48 MCI 2.0 83.84 F 1.0 26.0 3.0 -sub-CLNC1407 ses-M00 AD 3.0 73.15 M 2.0 27.0 3.0 -sub-CLNC1407 ses-M03 73.15 M 2.0 27.0 3.0 -sub-CLNC1407 ses-M06 AD 3.0 73.15 M 2.0 27.0 3.0 -sub-CLNC1407 ses-M12 AD 3.0 73.15 M 2.0 27.0 3.0 -sub-CLNC1407 ses-M18 73.15 M 2.0 27.0 3.0 -sub-CLNC1408 ses-M00 AD 3.0 67.16 F 8.0 27.0 0.0 -sub-CLNC1409 ses-M00 MCI 2.0 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M03 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M06 MCI 2.0 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M12 MCI 2.0 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M18 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M24 MCI 2.0 75.3 M 3.0 25.0 0.0 -sub-CLNC1409 ses-M36 MCI 2.0 75.3 M 3.0 25.0 0.0 -sub-CLNC1410 ses-M00 AD 3.0 74.55 F 1.0 23.0 0.0 -sub-CLNC1410 ses-M03 74.55 F 1.0 23.0 0.0 -sub-CLNC1410 ses-M06 AD 3.0 74.55 F 1.0 23.0 0.0 -sub-CLNC1410 ses-M12 AD 3.0 74.55 F 1.0 23.0 0.0 -sub-CLNC1410 ses-M18 74.55 F 1.0 23.0 0.0 -sub-CLNC1410 ses-M24 8.0 74.55 F 1.0 23.0 0.0 -sub-CLNC1411 ses-M00 AD 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M06 AD 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M12 AD 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M18 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M24 AD 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M30 71.82 F 8.0 23.0 0.0 -sub-CLNC1411 ses-M36 71.82 F 8.0 23.0 0.0 -sub-CLNC1412 ses-M00 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M03 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M06 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M12 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M18 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M24 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M30 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M36 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M42 79.01 F 6.0 22.0 1.0 -sub-CLNC1412 ses-M60 MCI 2.0 79.01 F 6.0 22.0 1.0 -sub-CLNC1413 ses-M00 CN 1.0 77.68 F 2.0 29.0 3.0 -sub-CLNC1414 ses-M00 CN 1.0 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M03 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M06 CN 1.0 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M12 CN 1.0 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M18 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M24 CN 1.0 92.89 F 3.0 27.0 0.5 -sub-CLNC1414 ses-M48 CN 1.0 92.89 F 3.0 27.0 0.5 -sub-CLNC1415 ses-M00 MCI 2.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M03 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M06 MCI 2.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M12 MCI 2.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M18 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M24 MCI 2.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M36 MCI 2.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1415 ses-M48 5.0 72.55 F 2.0 27.0 2.0 -sub-CLNC1416 ses-M00 MCI 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M06 MCI 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M12 AD 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M18 AD 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M24 AD 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M30 80.6 M 8.0 28.0 3.0 -sub-CLNC1416 ses-M36 80.6 M 8.0 28.0 3.0 -sub-CLNC1417 ses-M00 MCI 2.0 65.76 M 6.0 24.0 0.0 -sub-CLNC1417 ses-M03 65.76 M 6.0 24.0 0.0 -sub-CLNC1417 ses-M06 MCI 2.0 65.76 M 6.0 24.0 0.0 -sub-CLNC1417 ses-M12 MCI 2.0 65.76 M 6.0 24.0 0.0 -sub-CLNC1417 ses-M18 65.76 M 6.0 24.0 0.0 -sub-CLNC1418 ses-M00 MCI 2.0 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M03 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M06 MCI 2.0 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M12 MCI 2.0 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M18 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M24 MCI 2.0 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M36 MCI 2.0 78.25 M 8.0 26.0 1.0 -sub-CLNC1418 ses-M60 78.25 M 8.0 26.0 1.0 -sub-CLNC1419 ses-M00 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M06 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M12 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M18 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M24 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M30 70.41 F 9.0 23.0 0.5 -sub-CLNC1419 ses-M36 MCI 70.41 F 9.0 23.0 0.5 -sub-CLNC1420 ses-M00 MCI 2.0 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M03 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M06 MCI 2.0 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M12 MCI 2.0 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M18 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M24 MCI 2.0 78.3 F 9.0 26.0 2.0 -sub-CLNC1420 ses-M36 MCI 2.0 78.3 F 9.0 26.0 2.0 -sub-CLNC1421 ses-M00 AD 3.0 73.67 M 2.0 22.0 1.0 -sub-CLNC1421 ses-M06 AD 3.0 73.67 M 2.0 22.0 1.0 -sub-CLNC1421 ses-M12 AD 3.0 73.67 M 2.0 22.0 1.0 -sub-CLNC1421 ses-M18 73.67 M 2.0 22.0 1.0 -sub-CLNC1422 ses-M00 MCI 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M06 MCI 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M12 MCI 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M18 AD 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M24 AD 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M30 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M36 AD 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M42 61.85 F 2.0 22.0 0.5 -sub-CLNC1422 ses-M48 AD 3.0 61.85 F 2.0 22.0 0.5 -sub-CLNC1423 ses-M00 MCI 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M06 AD 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M12 AD 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M18 AD 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M24 AD 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M30 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M36 AD 73.94 F 3.0 28.0 1.0 -sub-CLNC1423 ses-M42 73.94 F 3.0 28.0 1.0 -sub-CLNC1424 ses-M00 CN 1.0 74.09 F 9.0 21.0 0.5 -sub-CLNC1424 ses-M06 CN 1.0 74.09 F 9.0 21.0 0.5 -sub-CLNC1424 ses-M36 CN 1.0 74.09 F 9.0 21.0 0.5 -sub-CLNC1425 ses-M00 AD 3.0 97.63 F 1.0 25.0 2.0 -sub-CLNC1425 ses-M06 AD 3.0 97.63 F 1.0 25.0 2.0 -sub-CLNC1425 ses-M12 AD 3.0 97.63 F 1.0 25.0 2.0 -sub-CLNC1425 ses-M18 97.63 F 1.0 25.0 2.0 -sub-CLNC1426 ses-M00 MCI 2.0 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M03 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M06 MCI 2.0 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M12 MCI 2.0 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M18 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M24 MCI 2.0 84.29 M 2.0 29.0 2.0 -sub-CLNC1426 ses-M36 MCI 2.0 84.29 M 2.0 29.0 2.0 -sub-CLNC1427 ses-M00 MCI 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M06 MCI 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M12 MCI 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M18 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M24 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M30 72.47 F 2.0 22.0 2.0 -sub-CLNC1427 ses-M36 72.47 F 2.0 22.0 2.0 -sub-CLNC1428 ses-M00 CN 1.0 72.96 M 9.0 25.0 0.0 -sub-CLNC1428 ses-M03 72.96 M 9.0 25.0 0.0 -sub-CLNC1428 ses-M12 CN 1.0 72.96 M 9.0 25.0 0.0 -sub-CLNC1428 ses-M18 72.96 M 9.0 25.0 0.0 -sub-CLNC1428 ses-M24 CN 1.0 72.96 M 9.0 25.0 0.0 -sub-CLNC1428 ses-M36 CN 1.0 72.96 M 9.0 25.0 0.0 -sub-CLNC1429 ses-M00 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M06 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M108 CN 1.0 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M12 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M18 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M24 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M30 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M36 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M42 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M48 CN 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M54 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M60 CN 1.0 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M66 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M72 CN 1.0 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M78 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M84 CN 1.0 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M90 79.3 M 9.0 25.0 1.0 -sub-CLNC1429 ses-M96 CN 1.0 79.3 M 9.0 25.0 1.0 -sub-CLNC1430 ses-M00 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M06 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M108 CN 1.0 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M12 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M18 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M24 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M30 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M36 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M42 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M48 CN 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M60 CN 1.0 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M66 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M72 CN 1.0 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M78 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M84 CN 1.0 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M90 59.43 M 1.0 23.0 0.5 -sub-CLNC1430 ses-M96 CN 1.0 59.43 M 1.0 23.0 0.5 -sub-CLNC1431 ses-M00 AD 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M06 AD 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M12 AD 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M18 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M24 AD 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M30 79.73 M 7.0 20.0 0.0 -sub-CLNC1431 ses-M36 79.73 M 7.0 20.0 0.0 -sub-CLNC1432 ses-M00 MCI 2.0 82.32 M 8.0 28.0 2.0 -sub-CLNC1432 ses-M03 82.32 M 8.0 28.0 2.0 -sub-CLNC1432 ses-M06 MCI 2.0 82.32 M 8.0 28.0 2.0 -sub-CLNC1432 ses-M12 MCI 2.0 82.32 M 8.0 28.0 2.0 -sub-CLNC1432 ses-M18 82.32 M 8.0 28.0 2.0 -sub-CLNC1432 ses-M24 5.0 82.32 M 8.0 28.0 2.0 -sub-CLNC1433 ses-M00 CN 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M06 CN 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M108 CN 1.0 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M12 CN 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M18 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M24 CN 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M30 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M36 CN 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M42 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M48 CN 1.0 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M60 CN 1.0 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M66 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M72 CN 1.0 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M78 76.02 F 6.0 23.0 3.0 -sub-CLNC1433 ses-M96 76.02 F 6.0 23.0 3.0 -sub-CLNC1434 ses-M00 MCI 2.0 57.73 F 6.0 25.0 1.0 -sub-CLNC1434 ses-M03 57.73 F 6.0 25.0 1.0 -sub-CLNC1434 ses-M06 MCI 2.0 57.73 F 6.0 25.0 1.0 -sub-CLNC1434 ses-M12 MCI 2.0 57.73 F 6.0 25.0 1.0 -sub-CLNC1434 ses-M24 5.0 57.73 F 6.0 25.0 1.0 -sub-CLNC1435 ses-M00 MCI 2.0 76.84 F 4.0 25.0 1.0 -sub-CLNC1435 ses-M03 76.84 F 4.0 25.0 1.0 -sub-CLNC1435 ses-M12 MCI 2.0 76.84 F 4.0 25.0 1.0 -sub-CLNC1436 ses-M00 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M03 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M06 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M12 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M18 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M24 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M30 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M36 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1436 ses-M48 MCI 2.0 80.49 F 2.0 22.0 0.5 -sub-CLNC1437 ses-M00 MCI 2.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M03 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M06 7.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M12 4.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M18 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M24 MCI 2.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M30 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M36 MCI 2.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1437 ses-M48 7.0 60.64 M 5.0 28.0 3.0 -sub-CLNC1438 ses-M00 AD 3.0 81.48 F 9.0 22.0 2.0 -sub-CLNC1438 ses-M06 AD 3.0 81.48 F 9.0 22.0 2.0 -sub-CLNC1438 ses-M12 AD 3.0 81.48 F 9.0 22.0 2.0 -sub-CLNC1438 ses-M18 81.48 F 9.0 22.0 2.0 -sub-CLNC1439 ses-M00 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M06 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M12 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M18 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M24 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M30 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M36 MCI 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M48 MCI 2.0 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M54 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M60 MCI 2.0 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M66 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M72 MCI 2.0 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M78 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M84 MCI 2.0 72.36 F 3.0 29.0 3.0 -sub-CLNC1439 ses-M96 72.36 F 3.0 29.0 3.0 -sub-CLNC1440 ses-M00 CN 1.0 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M03 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M06 CN 1.0 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M12 CN 1.0 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M18 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M24 CN 1.0 76.66 F 5.0 25.0 1.0 -sub-CLNC1440 ses-M48 CN 1.0 76.66 F 5.0 25.0 1.0 -sub-CLNC1441 ses-M00 AD 3.0 75.45 F 1.0 23.0 1.0 -sub-CLNC1441 ses-M03 75.45 F 1.0 23.0 1.0 -sub-CLNC1441 ses-M06 AD 3.0 75.45 F 1.0 23.0 1.0 -sub-CLNC1441 ses-M12 AD 3.0 75.45 F 1.0 23.0 1.0 -sub-CLNC1442 ses-M00 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M06 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M12 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M18 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M24 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M30 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M36 MCI 80.87 F 9.0 21.0 0.0 -sub-CLNC1442 ses-M42 80.87 F 9.0 21.0 0.0 -sub-CLNC1443 ses-M00 MCI 2.0 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M03 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M06 MCI 2.0 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M12 7.0 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M18 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M24 CN 1.0 74.12 M 8.0 25.0 3.0 -sub-CLNC1443 ses-M36 CN 1.0 74.12 M 8.0 25.0 3.0 -sub-CLNC1444 ses-M00 AD 3.0 68.36 M 5.0 27.0 0.5 -sub-CLNC1444 ses-M03 68.36 M 5.0 27.0 0.5 -sub-CLNC1444 ses-M06 AD 3.0 68.36 M 5.0 27.0 0.5 -sub-CLNC1444 ses-M12 AD 3.0 68.36 M 5.0 27.0 0.5 -sub-CLNC1445 ses-M00 AD 3.0 66.94 F 6.0 21.0 2.0 -sub-CLNC1445 ses-M03 66.94 F 6.0 21.0 2.0 -sub-CLNC1445 ses-M06 AD 3.0 66.94 F 6.0 21.0 2.0 -sub-CLNC1445 ses-M12 AD 3.0 66.94 F 6.0 21.0 2.0 -sub-CLNC1445 ses-M18 66.94 F 6.0 21.0 2.0 -sub-CLNC1445 ses-M24 AD 3.0 66.94 F 6.0 21.0 2.0 -sub-CLNC1446 ses-M00 AD 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M06 AD 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M12 AD 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M18 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M24 AD 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M30 76.58 F 5.0 21.0 2.0 -sub-CLNC1446 ses-M36 76.58 F 5.0 21.0 2.0 -sub-CLNC1447 ses-M00 CN 1.0 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M03 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M06 CN 1.0 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M12 CN 1.0 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M18 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M24 CN 1.0 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M30 83.78 F 2.0 26.0 1.0 -sub-CLNC1447 ses-M36 CN 1.0 83.78 F 2.0 26.0 1.0 -sub-CLNC1448 ses-M00 AD 3.0 65.78 M 2.0 24.0 0.5 -sub-CLNC1448 ses-M06 AD 3.0 65.78 M 2.0 24.0 0.5 -sub-CLNC1448 ses-M12 AD 3.0 65.78 M 2.0 24.0 0.5 -sub-CLNC1449 ses-M00 CN 1.0 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M03 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M06 CN 1.0 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M12 CN 1.0 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M18 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M24 CN 1.0 65.14 F 3.0 24.0 1.0 -sub-CLNC1449 ses-M48 CN 1.0 65.14 F 3.0 24.0 1.0 -sub-CLNC1450 ses-M00 CN 1.0 65.07 F 1.0 25.0 1.0 -sub-CLNC1450 ses-M03 65.07 F 1.0 25.0 1.0 -sub-CLNC1450 ses-M06 CN 1.0 65.07 F 1.0 25.0 1.0 -sub-CLNC1450 ses-M12 CN 1.0 65.07 F 1.0 25.0 1.0 -sub-CLNC1450 ses-M18 65.07 F 1.0 25.0 1.0 -sub-CLNC1450 ses-M24 CN 1.0 65.07 F 1.0 25.0 1.0 -sub-CLNC1451 ses-M00 AD 3.0 66.35 M 4.0 26.0 0.0 -sub-CLNC1451 ses-M06 AD 3.0 66.35 M 4.0 26.0 0.0 -sub-CLNC1452 ses-M00 MCI 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M06 MCI 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M12 MCI 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M18 MCI 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M24 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M30 70.9 M 6.0 22.0 0.5 -sub-CLNC1452 ses-M36 70.9 M 6.0 22.0 0.5 -sub-CLNC1453 ses-M00 AD 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M06 AD 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M12 AD 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M18 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M24 AD 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M30 70.3 M 1.0 21.0 0.5 -sub-CLNC1453 ses-M36 70.3 M 1.0 21.0 0.5 -sub-CLNC1454 ses-M00 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M03 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M06 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M12 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M18 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M24 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M36 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1454 ses-M48 MCI 2.0 80.47 M 9.0 21.0 0.5 -sub-CLNC1455 ses-M00 MCI 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M06 MCI 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M12 MCI 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M18 MCI 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M24 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M30 77.76 M 9.0 24.0 2.0 -sub-CLNC1455 ses-M36 77.76 M 9.0 24.0 2.0 -sub-CLNC1456 ses-M00 CN 1.0 65.74 M 6.0 27.0 0.5 -sub-CLNC1456 ses-M03 65.74 M 6.0 27.0 0.5 -sub-CLNC1456 ses-M06 CN 1.0 65.74 M 6.0 27.0 0.5 -sub-CLNC1456 ses-M12 CN 1.0 65.74 M 6.0 27.0 0.5 -sub-CLNC1456 ses-M18 65.74 M 6.0 27.0 0.5 -sub-CLNC1457 ses-M00 CN 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M06 CN 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M108 CN 1.0 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M12 CN 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M120 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M18 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M24 CN 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M30 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M36 CN 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M60 CN 1.0 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M66 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M72 4.0 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M78 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M90 79.3 M 2.0 22.0 0.0 -sub-CLNC1457 ses-M96 7.0 79.3 M 2.0 22.0 0.0 -sub-CLNC1458 ses-M00 CN 1.0 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M03 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M06 CN 1.0 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M12 CN 1.0 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M18 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M24 CN 1.0 65.24 M 2.0 20.0 1.0 -sub-CLNC1458 ses-M30 65.24 M 2.0 20.0 1.0 -sub-CLNC1459 ses-M00 MCI 2.0 71.03 F 2.0 23.0 0.5 -sub-CLNC1459 ses-M03 71.03 F 2.0 23.0 0.5 -sub-CLNC1460 ses-M00 MCI 2.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M03 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M06 MCI 2.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M12 MCI 2.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M18 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M30 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M36 5.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M48 MCI 2.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M60 MCI 2.0 61.81 F 1.0 21.0 3.0 -sub-CLNC1460 ses-M66 61.81 F 1.0 21.0 3.0 -sub-CLNC1461 ses-M00 AD 3.0 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M03 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M06 AD 3.0 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M12 AD 3.0 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M18 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M24 AD 3.0 66.28 M 7.0 26.0 3.0 -sub-CLNC1461 ses-M30 66.28 M 7.0 26.0 3.0 -sub-CLNC1462 ses-M00 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M03 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M06 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M12 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M18 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M24 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M30 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M36 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1462 ses-M48 MCI 2.0 80.34 M 7.0 29.0 0.5 -sub-CLNC1463 ses-M00 MCI 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M06 MCI 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M12 MCI 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M18 AD 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M24 AD 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M30 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M36 AD 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M42 73.32 F 3.0 24.0 0.0 -sub-CLNC1463 ses-M48 AD 3.0 73.32 F 3.0 24.0 0.0 -sub-CLNC1464 ses-M00 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M06 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M12 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M18 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M24 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M30 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M36 MCI 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M42 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M48 5.0 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M54 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M60 AD 3.0 72.7 M 9.0 26.0 1.0 -sub-CLNC1464 ses-M66 72.7 M 9.0 26.0 1.0 -sub-CLNC1465 ses-M00 CN 1.0 73.74 M 3.0 21.0 2.0 -sub-CLNC1465 ses-M03 73.74 M 3.0 21.0 2.0 -sub-CLNC1465 ses-M06 CN 1.0 73.74 M 3.0 21.0 2.0 -sub-CLNC1465 ses-M12 CN 1.0 73.74 M 3.0 21.0 2.0 -sub-CLNC1465 ses-M18 73.74 M 3.0 21.0 2.0 -sub-CLNC1465 ses-M24 CN 1.0 73.74 M 3.0 21.0 2.0 -sub-CLNC1466 ses-M00 MCI 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M06 MCI 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M12 MCI 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M18 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M24 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M30 63.41 F 7.0 22.0 3.0 -sub-CLNC1466 ses-M36 63.41 F 7.0 22.0 3.0 -sub-CLNC1467 ses-M00 AD 3.0 69.91 F 4.0 26.0 1.0 -sub-CLNC1467 ses-M06 AD 3.0 69.91 F 4.0 26.0 1.0 -sub-CLNC1467 ses-M12 69.91 F 4.0 26.0 1.0 -sub-CLNC1467 ses-M18 69.91 F 4.0 26.0 1.0 -sub-CLNC1467 ses-M24 69.91 F 4.0 26.0 1.0 -sub-CLNC1467 ses-M30 69.91 F 4.0 26.0 1.0 -sub-CLNC1468 ses-M00 MCI 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M06 MCI 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M12 MCI 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M120 AD 3.0 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M18 MCI 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M24 AD 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M30 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M36 AD 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M42 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M48 AD 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M60 AD 3.0 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M72 AD 3.0 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M78 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M84 AD 3.0 89.15 F 6.0 24.0 2.0 -sub-CLNC1468 ses-M96 AD 3.0 89.15 F 6.0 24.0 2.0 -sub-CLNC1469 ses-M00 CN 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M06 CN 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M108 CN 1.0 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M12 CN 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M18 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M24 CN 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M30 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M36 CN 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M42 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M48 CN 1.0 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M54 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M72 CN 1.0 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M84 CN 1.0 77.71 F 6.0 28.0 0.0 -sub-CLNC1469 ses-M96 CN 1.0 77.71 F 6.0 28.0 0.0 -sub-CLNC1470 ses-M00 CN 1.0 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M03 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M06 CN 1.0 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M12 CN 1.0 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M18 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M24 CN 1.0 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M30 76.67 M 8.0 23.0 2.0 -sub-CLNC1470 ses-M48 CN 1.0 76.67 M 8.0 23.0 2.0 -sub-CLNC1471 ses-M00 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M03 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M06 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M12 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M18 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M24 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M30 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M36 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M48 MCI 2.0 82.56 M 6.0 21.0 3.0 -sub-CLNC1471 ses-M60 82.56 M 6.0 21.0 3.0 -sub-CLNC1472 ses-M00 AD 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M06 AD 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M12 AD 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M18 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M24 AD 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M30 71.59 M 6.0 24.0 3.0 -sub-CLNC1472 ses-M36 71.59 M 6.0 24.0 3.0 -sub-CLNC1473 ses-M00 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M06 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M12 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M18 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M24 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M30 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M36 MCI 72.89 M 1.0 22.0 0.0 -sub-CLNC1473 ses-M48 AD 72.89 M 1.0 22.0 0.0 -sub-CLNC1474 ses-M00 CN 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M06 CN 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M12 CN 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M18 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M24 CN 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M30 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M36 CN 70.98 M 1.0 25.0 3.0 -sub-CLNC1474 ses-M42 70.98 M 1.0 25.0 3.0 -sub-CLNC1475 ses-M00 MCI 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M06 MCI 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M12 MCI 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M18 MCI 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M24 MCI 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M30 78.9 F 1.0 29.0 2.0 -sub-CLNC1475 ses-M36 AD 78.9 F 1.0 29.0 2.0 -sub-CLNC1476 ses-M00 CN 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M06 CN 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M12 CN 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M18 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M24 CN 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M30 77.56 M 6.0 20.0 0.5 -sub-CLNC1476 ses-M36 77.56 M 6.0 20.0 0.5 -sub-CLNC1477 ses-M00 CN 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M06 CN 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M102 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M12 CN 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M120 4.0 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M18 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M24 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M30 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M36 CN 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M48 CN 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M60 CN 1.0 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M66 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M72 CN 1.0 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M78 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M84 CN 1.0 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M90 67.48 F 8.0 24.0 0.5 -sub-CLNC1477 ses-M96 CN 1.0 67.48 F 8.0 24.0 0.5 -sub-CLNC1478 ses-M00 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M03 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M06 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M12 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M18 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M24 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M30 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M36 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M48 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1478 ses-M60 MCI 2.0 81.72 F 8.0 23.0 2.0 -sub-CLNC1479 ses-M00 MCI 2.0 68.95 F 1.0 28.0 2.0 -sub-CLNC1479 ses-M03 68.95 F 1.0 28.0 2.0 -sub-CLNC1480 ses-M00 AD 3.0 77.37 F 5.0 25.0 0.0 -sub-CLNC1480 ses-M06 AD 3.0 77.37 F 5.0 25.0 0.0 -sub-CLNC1480 ses-M12 AD 3.0 77.37 F 5.0 25.0 0.0 -sub-CLNC1480 ses-M18 77.37 F 5.0 25.0 0.0 -sub-CLNC1481 ses-M00 7.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M03 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M06 CN 1.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M12 7.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M18 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M24 CN 1.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M30 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M36 CN 1.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M48 CN 1.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1481 ses-M60 CN 1.0 71.65 M 8.0 23.0 3.0 -sub-CLNC1482 ses-M00 MCI 2.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M03 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M06 MCI 2.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M12 MCI 2.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M18 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M24 MCI 2.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M36 5.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1482 ses-M48 AD 3.0 61.4 M 8.0 29.0 0.5 -sub-CLNC1483 ses-M00 CN 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M06 CN 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M12 CN 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M18 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M24 CN 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M30 71.47 F 8.0 24.0 3.0 -sub-CLNC1483 ses-M36 71.47 F 8.0 24.0 3.0 -sub-CLNC1484 ses-M00 MCI 2.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M03 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M06 MCI 2.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M12 MCI 2.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M18 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M24 MCI 2.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M30 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M36 MCI 2.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M48 AD 3.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1484 ses-M60 5.0 81.59 F 8.0 28.0 0.5 -sub-CLNC1485 ses-M00 MCI 2.0 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M03 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M06 5.0 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M12 AD 3.0 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M18 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M24 AD 3.0 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M30 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M36 AD 3.0 88.37 M 1.0 27.0 0.5 -sub-CLNC1485 ses-M48 88.37 M 1.0 27.0 0.5 -sub-CLNC1486 ses-M00 CN 1.0 78.38 M 8.0 25.0 0.5 -sub-CLNC1486 ses-M06 CN 1.0 78.38 M 8.0 25.0 0.5 -sub-CLNC1486 ses-M36 CN 1.0 78.38 M 8.0 25.0 0.5 -sub-CLNC1487 ses-M00 MCI 2.0 72.52 M 5.0 28.0 1.0 -sub-CLNC1487 ses-M03 72.52 M 5.0 28.0 1.0 -sub-CLNC1487 ses-M06 MCI 2.0 72.52 M 5.0 28.0 1.0 -sub-CLNC1487 ses-M12 MCI 2.0 72.52 M 5.0 28.0 1.0 -sub-CLNC1487 ses-M24 MCI 2.0 72.52 M 5.0 28.0 1.0 -sub-CLNC1487 ses-M36 MCI 2.0 72.52 M 5.0 28.0 1.0 -sub-CLNC1488 ses-M00 AD 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M06 AD 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M12 AD 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M18 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M24 AD 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M30 89.42 F 9.0 28.0 1.0 -sub-CLNC1488 ses-M36 89.42 F 9.0 28.0 1.0 -sub-CLNC1489 ses-M00 AD 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M06 AD 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M12 AD 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M18 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M24 AD 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M30 64.23 M 2.0 22.0 2.0 -sub-CLNC1489 ses-M36 64.23 M 2.0 22.0 2.0 -sub-CLNC1490 ses-M00 MCI 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M06 MCI 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M12 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M18 MCI 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M24 MCI 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M30 72.28 F 2.0 28.0 2.0 -sub-CLNC1490 ses-M36 72.28 F 2.0 28.0 2.0 -sub-CLNC1491 ses-M00 AD 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M06 AD 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M12 AD 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M18 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M24 AD 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M30 78.16 F 4.0 26.0 1.0 -sub-CLNC1491 ses-M36 78.16 F 4.0 26.0 1.0 -sub-CLNC1492 ses-M00 CN 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M06 CN 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M108 MCI 2.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M12 CN 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M120 4.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M18 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M24 CN 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M30 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M36 CN 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M42 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M48 CN 1.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M54 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M72 4.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M78 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M84 MCI 2.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M90 82.81 M 7.0 28.0 0.0 -sub-CLNC1492 ses-M96 MCI 2.0 82.81 M 7.0 28.0 0.0 -sub-CLNC1493 ses-M00 MCI 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M06 MCI 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M12 MCI 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M18 MCI 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M24 MCI 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M30 75.91 F 2.0 23.0 1.0 -sub-CLNC1493 ses-M36 75.91 F 2.0 23.0 1.0 -sub-CLNC1494 ses-M00 AD 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M06 AD 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M12 AD 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M18 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M24 AD 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M30 74.49 F 6.0 25.0 0.5 -sub-CLNC1494 ses-M36 74.49 F 6.0 25.0 0.5 -sub-CLNC1495 ses-M00 AD 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M06 AD 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M12 AD 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M18 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M24 AD 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M30 80.4 M 9.0 22.0 3.0 -sub-CLNC1495 ses-M36 80.4 M 9.0 22.0 3.0 -sub-CLNC1496 ses-M00 CN 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M06 CN 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M108 MCI 2.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M12 CN 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M18 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M24 CN 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M30 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M36 CN 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M42 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M48 4.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M54 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M60 CN 1.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M66 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M72 4.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M78 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M84 MCI 2.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M90 82.91 F 1.0 22.0 2.0 -sub-CLNC1496 ses-M96 MCI 2.0 82.91 F 1.0 22.0 2.0 -sub-CLNC1497 ses-M00 MCI 2.0 72.5 F 2.0 27.0 0.0 -sub-CLNC1498 ses-M00 MCI 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M06 MCI 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M12 MCI 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M18 MCI 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M24 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M30 78.35 M 9.0 25.0 2.0 -sub-CLNC1498 ses-M36 78.35 M 9.0 25.0 2.0 -sub-CLNC1499 ses-M00 MCI 2.0 71.76 M 9.0 20.0 0.5 -sub-CLNC1499 ses-M06 MCI 2.0 71.76 M 9.0 20.0 0.5 -sub-CLNC1500 ses-M00 MCI 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M06 MCI 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M12 MCI 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M18 MCI 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M24 MCI 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M30 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M36 66.78 F 5.0 22.0 0.0 -sub-CLNC1500 ses-M60 5.0 66.78 F 5.0 22.0 0.0 -sub-CLNC1501 ses-M00 MCI 2.0 73.7 F 3.0 20.0 3.0 -sub-CLNC1501 ses-M03 73.7 F 3.0 20.0 3.0 -sub-CLNC1501 ses-M06 MCI 2.0 73.7 F 3.0 20.0 3.0 -sub-CLNC1501 ses-M12 MCI 2.0 73.7 F 3.0 20.0 3.0 -sub-CLNC1501 ses-M18 73.7 F 3.0 20.0 3.0 -sub-CLNC1502 ses-M00 CN 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M06 CN 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M12 CN 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M18 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M24 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M30 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M36 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M60 CN 1.0 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M66 75.42 M 5.0 28.0 3.0 -sub-CLNC1502 ses-M72 CN 1.0 75.42 M 5.0 28.0 3.0 -sub-CLNC1503 ses-M00 AD 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M06 AD 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M12 AD 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M18 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M24 AD 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M30 70.99 M 4.0 26.0 0.5 -sub-CLNC1503 ses-M36 70.99 M 4.0 26.0 0.5 -sub-CLNC1504 ses-M00 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M06 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M108 MCI 2.0 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M12 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M18 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M24 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M30 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M36 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M42 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M48 MCI 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M60 MCI 2.0 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M66 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M72 MCI 2.0 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M78 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M84 MCI 2.0 81.18 M 2.0 20.0 3.0 -sub-CLNC1504 ses-M96 MCI 2.0 81.18 M 2.0 20.0 3.0 -sub-CLNC1505 ses-M00 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M06 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M12 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M18 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M24 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M30 77.68 F 5.0 26.0 1.0 -sub-CLNC1505 ses-M36 MCI 77.68 F 5.0 26.0 1.0 -sub-CLNC1506 ses-M00 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M03 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M06 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M12 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M18 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M24 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M36 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1506 ses-M48 MCI 2.0 70.21 F 7.0 22.0 0.5 -sub-CLNC1507 ses-M00 AD 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M06 AD 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M12 AD 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M18 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M24 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M30 71.42 M 2.0 23.0 0.0 -sub-CLNC1507 ses-M36 71.42 M 2.0 23.0 0.0 -sub-CLNC1508 ses-M00 CN 1.0 77.54 F 2.0 25.0 2.0 -sub-CLNC1508 ses-M03 77.54 F 2.0 25.0 2.0 -sub-CLNC1508 ses-M06 CN 1.0 77.54 F 2.0 25.0 2.0 -sub-CLNC1508 ses-M12 CN 1.0 77.54 F 2.0 25.0 2.0 -sub-CLNC1508 ses-M18 77.54 F 2.0 25.0 2.0 -sub-CLNC1508 ses-M30 77.54 F 2.0 25.0 2.0 -sub-CLNC1509 ses-M00 AD 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M06 AD 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M12 AD 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M18 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M24 AD 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M30 77.43 M 6.0 27.0 3.0 -sub-CLNC1509 ses-M36 77.43 M 6.0 27.0 3.0 -sub-CLNC1510 ses-M00 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M03 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M06 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M12 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M18 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M24 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M30 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M36 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1510 ses-M48 MCI 2.0 62.36 M 8.0 25.0 2.0 -sub-CLNC1511 ses-M00 AD 3.0 80.41 F 9.0 28.0 0.5 -sub-CLNC1511 ses-M03 80.41 F 9.0 28.0 0.5 -sub-CLNC1511 ses-M06 AD 3.0 80.41 F 9.0 28.0 0.5 -sub-CLNC1511 ses-M12 AD 3.0 80.41 F 9.0 28.0 0.5 -sub-CLNC1511 ses-M18 80.41 F 9.0 28.0 0.5 -sub-CLNC1512 ses-M00 MCI 2.0 67.85 M 8.0 28.0 0.5 -sub-CLNC1512 ses-M03 67.85 M 8.0 28.0 0.5 -sub-CLNC1512 ses-M06 MCI 2.0 67.85 M 8.0 28.0 0.5 -sub-CLNC1512 ses-M12 MCI 2.0 67.85 M 8.0 28.0 0.5 -sub-CLNC1512 ses-M18 67.85 M 8.0 28.0 0.5 -sub-CLNC1513 ses-M00 MCI 2.0 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M03 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M06 5.0 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M12 AD 3.0 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M18 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M24 AD 3.0 70.82 M 5.0 29.0 1.0 -sub-CLNC1513 ses-M30 70.82 M 5.0 29.0 1.0 -sub-CLNC1514 ses-M00 MCI 2.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M03 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M06 MCI 2.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M12 MCI 2.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M18 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M24 5.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M30 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M36 AD 3.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1514 ses-M48 AD 3.0 74.43 F 9.0 22.0 1.0 -sub-CLNC1515 ses-M00 AD 3.0 72.51 M 9.0 27.0 0.0 -sub-CLNC1515 ses-M03 72.51 M 9.0 27.0 0.0 -sub-CLNC1515 ses-M06 AD 3.0 72.51 M 9.0 27.0 0.0 -sub-CLNC1515 ses-M12 AD 3.0 72.51 M 9.0 27.0 0.0 -sub-CLNC1515 ses-M30 72.51 M 9.0 27.0 0.0 -sub-CLNC1516 ses-M00 MCI 2.0 73.91 F 3.0 29.0 1.0 -sub-CLNC1517 ses-M00 MCI 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M06 MCI 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M12 MCI 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M18 MCI 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M24 AD 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M30 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M36 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M72 69.8 F 4.0 21.0 2.0 -sub-CLNC1517 ses-M78 69.8 F 4.0 21.0 2.0 -sub-CLNC1518 ses-M00 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M06 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M12 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M120 CN 1.0 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M18 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M24 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M30 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M36 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M42 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M48 CN 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M60 CN 1.0 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M66 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M72 CN 1.0 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M78 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M84 CN 1.0 65.23 F 4.0 24.0 3.0 -sub-CLNC1518 ses-M96 CN 1.0 65.23 F 4.0 24.0 3.0 -sub-CLNC1519 ses-M00 AD 3.0 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M03 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M06 AD 3.0 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M12 AD 3.0 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M18 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M24 AD 3.0 65.09 M 7.0 27.0 3.0 -sub-CLNC1519 ses-M36 65.09 M 7.0 27.0 3.0 -sub-CLNC1520 ses-M00 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M06 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M12 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M18 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M24 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M30 69.33 F 6.0 22.0 2.0 -sub-CLNC1520 ses-M36 MCI 69.33 F 6.0 22.0 2.0 -sub-CLNC1521 ses-M00 CN 1.0 82.38 M 3.0 28.0 0.5 -sub-CLNC1521 ses-M03 82.38 M 3.0 28.0 0.5 -sub-CLNC1521 ses-M06 CN 1.0 82.38 M 3.0 28.0 0.5 -sub-CLNC1521 ses-M24 4.0 82.38 M 3.0 28.0 0.5 -sub-CLNC1522 ses-M00 AD 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M06 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M12 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M18 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M24 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M30 75.85 M 3.0 29.0 0.5 -sub-CLNC1522 ses-M36 75.85 M 3.0 29.0 0.5 -sub-CLNC1523 ses-M00 MCI 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M06 MCI 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M12 MCI 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M18 MCI 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M24 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M30 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M36 AD 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M60 AD 3.0 91.43 M 7.0 26.0 0.0 -sub-CLNC1523 ses-M66 91.43 M 7.0 26.0 0.0 -sub-CLNC1524 ses-M00 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M03 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M06 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M12 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M18 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M24 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M36 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M48 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1524 ses-M60 MCI 2.0 89.53 M 9.0 23.0 0.0 -sub-CLNC1525 ses-M00 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M03 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M06 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M12 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M18 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M24 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M36 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1525 ses-M48 MCI 2.0 73.71 F 1.0 29.0 3.0 -sub-CLNC1526 ses-M00 MCI 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M06 MCI 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M12 MCI 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M18 AD 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M24 AD 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M30 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M36 AD 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M42 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M48 AD 3.0 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M54 67.93 F 4.0 28.0 0.5 -sub-CLNC1526 ses-M60 AD 3.0 67.93 F 4.0 28.0 0.5 -sub-CLNC1527 ses-M00 MCI 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M06 MCI 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M12 MCI 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M18 MCI 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M24 AD 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M30 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M36 AD 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M42 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M48 AD 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M60 AD 3.0 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M66 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M72 AD 3.0 74.51 F 5.0 22.0 3.0 -sub-CLNC1527 ses-M84 AD 3.0 74.51 F 5.0 22.0 3.0 -sub-CLNC1528 ses-M00 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M06 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M12 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M18 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M24 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M36 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1528 ses-M48 MCI 2.0 78.16 M 5.0 20.0 0.0 -sub-CLNC1529 ses-M00 CN 1.0 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M03 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M06 CN 1.0 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M12 CN 1.0 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M18 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M24 CN 1.0 79.91 F 2.0 29.0 0.5 -sub-CLNC1529 ses-M48 CN 1.0 79.91 F 2.0 29.0 0.5 -sub-CLNC1530 ses-M00 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M06 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M108 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M12 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M18 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M24 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M30 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M36 MCI 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M42 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M48 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M54 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M60 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M66 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M72 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M78 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M84 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1530 ses-M96 MCI 2.0 71.14 F 5.0 20.0 0.0 -sub-CLNC1531 ses-M00 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M06 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M12 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M18 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M24 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M30 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M36 MCI 85.29 F 5.0 26.0 0.5 -sub-CLNC1531 ses-M42 85.29 F 5.0 26.0 0.5 -sub-CLNC1532 ses-M00 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M03 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M06 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M12 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M18 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M24 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M30 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M36 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1532 ses-M48 MCI 2.0 76.78 F 5.0 21.0 0.5 -sub-CLNC1533 ses-M00 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M06 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M12 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M18 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M24 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M30 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M36 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M42 70.64 M 9.0 28.0 0.5 -sub-CLNC1533 ses-M48 CN 70.64 M 9.0 28.0 0.5 -sub-CLNC1534 ses-M00 MCI 2.0 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M03 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M06 MCI 2.0 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M12 7.0 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M18 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M24 CN 1.0 81.2 F 7.0 23.0 0.5 -sub-CLNC1534 ses-M36 81.2 F 7.0 23.0 0.5 -sub-CLNC1535 ses-M00 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M03 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M06 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M12 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M18 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M24 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M36 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M42 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M48 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1535 ses-M60 MCI 2.0 81.26 F 4.0 25.0 3.0 -sub-CLNC1536 ses-M00 AD 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M06 AD 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M12 AD 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M18 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M24 AD 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M30 69.49 M 9.0 29.0 3.0 -sub-CLNC1536 ses-M36 AD 69.49 M 9.0 29.0 3.0 -sub-CLNC1537 ses-M00 MCI 2.0 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M03 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M06 MCI 2.0 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M12 MCI 2.0 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M18 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M24 5.0 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M30 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M36 AD 3.0 73.65 M 2.0 21.0 0.0 -sub-CLNC1537 ses-M42 73.65 M 2.0 21.0 0.0 -sub-CLNC1538 ses-M00 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M06 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M12 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M18 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M24 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M30 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M36 MCI 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M42 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M48 MCI 2.0 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M54 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M60 MCI 2.0 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M66 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M72 5.0 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M78 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M84 80.45 F 2.0 22.0 2.0 -sub-CLNC1538 ses-M90 80.45 F 2.0 22.0 2.0 -sub-CLNC1539 ses-M00 AD 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M06 AD 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M12 AD 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M18 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M24 AD 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M30 78.43 F 2.0 23.0 3.0 -sub-CLNC1539 ses-M36 78.43 F 2.0 23.0 3.0 -sub-CLNC1540 ses-M00 CN 1.0 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M03 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M06 CN 1.0 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M12 CN 1.0 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M18 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M24 CN 1.0 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M30 61.87 F 6.0 25.0 0.5 -sub-CLNC1540 ses-M48 CN 1.0 61.87 F 6.0 25.0 0.5 -sub-CLNC1541 ses-M00 CN 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M06 CN 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M12 CN 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M18 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M24 CN 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M30 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M36 CN 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M48 CN 1.0 81.73 F 2.0 22.0 0.5 -sub-CLNC1541 ses-M60 CN 1.0 81.73 F 2.0 22.0 0.5 -sub-CLNC1542 ses-M00 MCI 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M06 MCI 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M12 MCI 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M18 MCI 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M24 MCI 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M30 89.36 M 3.0 29.0 2.0 -sub-CLNC1542 ses-M36 89.36 M 3.0 29.0 2.0 -sub-CLNC1543 ses-M00 MCI 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M06 MCI 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M12 AD 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M18 MCI 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M24 MCI 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M30 81.08 M 7.0 27.0 0.5 -sub-CLNC1543 ses-M36 MCI 81.08 M 7.0 27.0 0.5 -sub-CLNC1544 ses-M00 AD 3.0 71.81 M 1.0 24.0 3.0 -sub-CLNC1544 ses-M03 71.81 M 1.0 24.0 3.0 -sub-CLNC1544 ses-M06 AD 3.0 71.81 M 1.0 24.0 3.0 -sub-CLNC1544 ses-M12 AD 3.0 71.81 M 1.0 24.0 3.0 -sub-CLNC1544 ses-M36 71.81 M 1.0 24.0 3.0 -sub-CLNC1545 ses-M00 AD 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M06 AD 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M12 AD 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M18 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M24 AD 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M30 77.65 M 1.0 23.0 0.0 -sub-CLNC1545 ses-M36 77.65 M 1.0 23.0 0.0 -sub-CLNC1546 ses-M00 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M06 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M12 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M18 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M24 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M30 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M36 MCI 77.4 M 7.0 28.0 0.5 -sub-CLNC1546 ses-M42 77.4 M 7.0 28.0 0.5 -sub-CLNC1547 ses-M00 CN 1.0 68.35 F 3.0 22.0 0.5 -sub-CLNC1547 ses-M03 68.35 F 3.0 22.0 0.5 -sub-CLNC1547 ses-M06 CN 1.0 68.35 F 3.0 22.0 0.5 -sub-CLNC1547 ses-M24 CN 1.0 68.35 F 3.0 22.0 0.5 -sub-CLNC1548 ses-M00 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M06 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M12 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M18 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M24 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M30 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M36 89.3 M 7.0 26.0 0.5 -sub-CLNC1548 ses-M48 MCI 89.3 M 7.0 26.0 0.5 -sub-CLNC1549 ses-M00 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M06 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M12 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M18 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M24 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M30 69.12 F 6.0 20.0 3.0 -sub-CLNC1549 ses-M36 MCI 69.12 F 6.0 20.0 3.0 -sub-CLNC1550 ses-M00 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M06 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M12 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M18 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M24 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M30 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M36 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M42 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M48 MCI 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M54 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M60 MCI 2.0 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M66 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M72 MCI 2.0 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M78 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M84 MCI 2.0 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M90 78.78 F 1.0 25.0 0.5 -sub-CLNC1550 ses-M96 MCI 2.0 78.78 F 1.0 25.0 0.5 -sub-CLNC1551 ses-M00 AD 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M06 AD 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M12 AD 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M18 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M24 AD 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M30 76.93 M 6.0 24.0 2.0 -sub-CLNC1551 ses-M36 AD 76.93 M 6.0 24.0 2.0 -sub-CLNC1552 ses-M00 CN 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M06 CN 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M12 CN 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M18 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M24 CN 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M30 73.59 F 2.0 28.0 0.0 -sub-CLNC1552 ses-M36 CN 73.59 F 2.0 28.0 0.0 -sub-CLNC1553 ses-M00 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M06 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M12 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M18 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M24 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M30 73.85 F 3.0 21.0 0.5 -sub-CLNC1553 ses-M36 MCI 73.85 F 3.0 21.0 0.5 -sub-CLNC1554 ses-M00 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M03 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M06 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M12 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M18 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M24 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M36 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1554 ses-M48 MCI 2.0 75.67 F 9.0 29.0 0.0 -sub-CLNC1555 ses-M00 MCI 2.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M03 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M06 MCI 2.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M12 MCI 2.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M18 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M24 MCI 2.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M30 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M36 7.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1555 ses-M48 CN 1.0 75.65 F 8.0 27.0 0.0 -sub-CLNC1556 ses-M00 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M06 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M12 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M18 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M24 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M30 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M36 MCI 73.3 F 6.0 21.0 1.0 -sub-CLNC1556 ses-M42 73.3 F 6.0 21.0 1.0 -sub-CLNC1557 ses-M00 CN 1.0 73.95 M 9.0 27.0 2.0 -sub-CLNC1557 ses-M03 73.95 M 9.0 27.0 2.0 -sub-CLNC1557 ses-M06 4.0 73.95 M 9.0 27.0 2.0 -sub-CLNC1557 ses-M12 MCI 2.0 73.95 M 9.0 27.0 2.0 -sub-CLNC1557 ses-M18 73.95 M 9.0 27.0 2.0 -sub-CLNC1557 ses-M24 MCI 2.0 73.95 M 9.0 27.0 2.0 -sub-CLNC1558 ses-M00 CN 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M06 CN 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M12 CN 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M18 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M24 CN 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M30 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M36 CN 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M42 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M48 CN 1.0 75.08 F 3.0 22.0 3.0 -sub-CLNC1558 ses-M54 75.08 F 3.0 22.0 3.0 -sub-CLNC1559 ses-M00 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M06 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M12 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M18 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M24 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M30 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M36 MCI 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M42 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M48 MCI 2.0 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M54 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M60 MCI 2.0 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M66 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M84 MCI 2.0 62.45 F 5.0 21.0 0.5 -sub-CLNC1559 ses-M90 62.45 F 5.0 21.0 0.5 -sub-CLNC1560 ses-M00 AD 3.0 76.63 M 8.0 26.0 0.5 -sub-CLNC1561 ses-M00 MCI 2.0 57.9 F 1.0 27.0 2.0 -sub-CLNC1561 ses-M03 57.9 F 1.0 27.0 2.0 -sub-CLNC1561 ses-M06 MCI 2.0 57.9 F 1.0 27.0 2.0 -sub-CLNC1561 ses-M12 5.0 57.9 F 1.0 27.0 2.0 -sub-CLNC1561 ses-M48 5.0 57.9 F 1.0 27.0 2.0 -sub-CLNC1562 ses-M00 AD 3.0 71.53 M 4.0 20.0 0.5 -sub-CLNC1562 ses-M03 71.53 M 4.0 20.0 0.5 -sub-CLNC1563 ses-M00 CN 1.0 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M03 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M06 CN 1.0 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M12 CN 1.0 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M18 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M24 CN 1.0 70.23 F 1.0 25.0 0.5 -sub-CLNC1563 ses-M36 70.23 F 1.0 25.0 0.5 -sub-CLNC1564 ses-M00 CN 1.0 85.24 M 9.0 27.0 0.5 -sub-CLNC1564 ses-M06 CN 1.0 85.24 M 9.0 27.0 0.5 -sub-CLNC1565 ses-M00 CN 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M06 CN 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M12 CN 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M18 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M24 CN 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M30 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M36 CN 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M42 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M48 CN 1.0 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M54 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M60 CN 1.0 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M66 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M72 CN 1.0 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M78 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M84 CN 1.0 89.64 M 4.0 20.0 2.0 -sub-CLNC1565 ses-M90 89.64 M 4.0 20.0 2.0 -sub-CLNC1566 ses-M00 CN 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M06 CN 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M12 CN 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M18 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M24 CN 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M30 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M36 CN 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M42 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M48 CN 1.0 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M54 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M60 CN 1.0 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M66 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M72 CN 1.0 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M78 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M84 CN 1.0 78.53 F 9.0 24.0 0.5 -sub-CLNC1566 ses-M96 CN 1.0 78.53 F 9.0 24.0 0.5 -sub-CLNC1567 ses-M00 AD 3.0 78.25 M 9.0 20.0 3.0 -sub-CLNC1567 ses-M03 78.25 M 9.0 20.0 3.0 -sub-CLNC1567 ses-M06 AD 3.0 78.25 M 9.0 20.0 3.0 -sub-CLNC1567 ses-M12 AD 3.0 78.25 M 9.0 20.0 3.0 -sub-CLNC1568 ses-M00 CN 1.0 64.91 M 4.0 25.0 3.0 -sub-CLNC1568 ses-M06 CN 1.0 64.91 M 4.0 25.0 3.0 -sub-CLNC1568 ses-M12 CN 1.0 64.91 M 4.0 25.0 3.0 -sub-CLNC1569 ses-M00 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M03 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M06 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M12 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M18 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M24 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M30 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M36 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1569 ses-M48 MCI 2.0 64.49 F 6.0 26.0 2.0 -sub-CLNC1570 ses-M00 CN 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M06 CN 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M12 CN 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M18 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M24 CN 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M30 66.58 M 9.0 22.0 2.0 -sub-CLNC1570 ses-M36 CN 66.58 M 9.0 22.0 2.0 -sub-CLNC1571 ses-M00 AD 3.0 72.78 F 8.0 27.0 2.0 -sub-CLNC1571 ses-M03 72.78 F 8.0 27.0 2.0 -sub-CLNC1571 ses-M06 AD 3.0 72.78 F 8.0 27.0 2.0 -sub-CLNC1571 ses-M12 AD 3.0 72.78 F 8.0 27.0 2.0 -sub-CLNC1572 ses-M00 AD 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M06 AD 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M12 AD 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M18 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M24 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M30 83.37 M 2.0 27.0 0.5 -sub-CLNC1572 ses-M36 83.37 M 2.0 27.0 0.5 -sub-CLNC1573 ses-M00 CN 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M06 CN 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M108 CN 1.0 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M12 CN 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M18 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M24 CN 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M30 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M36 CN 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M60 CN 1.0 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M66 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M72 CN 1.0 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M78 76.25 M 2.0 25.0 1.0 -sub-CLNC1573 ses-M84 CN 1.0 76.25 M 2.0 25.0 1.0 -sub-CLNC1574 ses-M00 CN 1.0 65.28 F 5.0 20.0 0.0 -sub-CLNC1574 ses-M24 CN 1.0 65.28 F 5.0 20.0 0.0 -sub-CLNC1575 ses-M00 MCI 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M06 MCI 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M108 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M12 MCI 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M18 MCI 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M24 AD 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M30 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M36 AD 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M42 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M48 AD 3.0 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M54 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M60 AD 3.0 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M66 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M72 AD 3.0 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M78 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M84 AD 3.0 65.88 M 3.0 23.0 0.5 -sub-CLNC1575 ses-M96 65.88 M 3.0 23.0 0.5 -sub-CLNC1576 ses-M00 MCI 2.0 75.89 F 4.0 26.0 3.0 -sub-CLNC1577 ses-M00 MCI 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M06 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M12 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M18 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M24 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M30 82.66 M 2.0 27.0 3.0 -sub-CLNC1577 ses-M36 82.66 M 2.0 27.0 3.0 -sub-CLNC1578 ses-M00 CN 1.0 79.57 M 9.0 28.0 0.0 -sub-CLNC1578 ses-M06 79.57 M 9.0 28.0 0.0 -sub-CLNC1578 ses-M12 CN 1.0 79.57 M 9.0 28.0 0.0 -sub-CLNC1578 ses-M24 CN 1.0 79.57 M 9.0 28.0 0.0 -sub-CLNC1579 ses-M00 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M06 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M12 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M18 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M24 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M30 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M36 MCI 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M48 MCI 2.0 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M54 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M60 MCI 2.0 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M66 64.77 F 2.0 24.0 2.0 -sub-CLNC1579 ses-M72 5.0 64.77 F 2.0 24.0 2.0 -sub-CLNC1580 ses-M00 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M06 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M108 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M12 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M120 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M18 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M24 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M30 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M36 MCI 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M42 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M48 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M60 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M66 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M72 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M78 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M84 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1580 ses-M96 MCI 2.0 73.56 F 9.0 22.0 0.0 -sub-CLNC1581 ses-M00 AD 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M06 AD 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M12 AD 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M18 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M24 AD 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M30 72.28 F 7.0 24.0 0.0 -sub-CLNC1581 ses-M36 72.28 F 7.0 24.0 0.0 -sub-CLNC1582 ses-M00 MCI 2.0 68.86 M 3.0 26.0 0.0 -sub-CLNC1583 ses-M00 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M03 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M06 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M12 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M24 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M30 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M36 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M42 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M48 MCI 2.0 73.22 M 1.0 29.0 3.0 -sub-CLNC1583 ses-M60 73.22 M 1.0 29.0 3.0 -sub-CLNC1584 ses-M00 MCI 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M06 MCI 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M12 MCI 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M18 MCI 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M24 AD 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M30 71.19 F 7.0 21.0 0.0 -sub-CLNC1584 ses-M36 71.19 F 7.0 21.0 0.0 -sub-CLNC1585 ses-M00 CN 1.0 69.29 F 1.0 26.0 2.0 -sub-CLNC1585 ses-M03 69.29 F 1.0 26.0 2.0 -sub-CLNC1585 ses-M06 CN 1.0 69.29 F 1.0 26.0 2.0 -sub-CLNC1585 ses-M12 CN 1.0 69.29 F 1.0 26.0 2.0 -sub-CLNC1585 ses-M18 69.29 F 1.0 26.0 2.0 -sub-CLNC1585 ses-M24 CN 1.0 69.29 F 1.0 26.0 2.0 -sub-CLNC1586 ses-M00 MCI 2.0 71.87 M 6.0 20.0 1.0 -sub-CLNC1586 ses-M03 71.87 M 6.0 20.0 1.0 -sub-CLNC1586 ses-M06 MCI 2.0 71.87 M 6.0 20.0 1.0 -sub-CLNC1586 ses-M12 MCI 2.0 71.87 M 6.0 20.0 1.0 -sub-CLNC1586 ses-M18 71.87 M 6.0 20.0 1.0 -sub-CLNC1587 ses-M00 MCI 2.0 81.22 F 1.0 24.0 0.5 -sub-CLNC1588 ses-M00 MCI 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M06 MCI 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M12 MCI 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M18 MCI 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M24 AD 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M30 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M36 AD 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M42 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M48 AD 3.0 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M54 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M60 AD 3.0 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M66 75.8 F 9.0 20.0 1.0 -sub-CLNC1588 ses-M72 AD 3.0 75.8 F 9.0 20.0 1.0 -sub-CLNC1589 ses-M00 AD 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M06 AD 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M12 AD 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M18 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M24 AD 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M30 75.79 F 6.0 20.0 3.0 -sub-CLNC1589 ses-M36 75.79 F 6.0 20.0 3.0 -sub-CLNC1590 ses-M00 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M03 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M06 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M12 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M18 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M24 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M30 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M36 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M48 MCI 2.0 76.63 M 9.0 24.0 2.0 -sub-CLNC1590 ses-M60 76.63 M 9.0 24.0 2.0 -sub-CLNC1591 ses-M00 CN 1.0 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M03 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M06 CN 1.0 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M12 CN 1.0 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M18 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M24 4.0 69.71 F 8.0 25.0 3.0 -sub-CLNC1591 ses-M36 MCI 2.0 69.71 F 8.0 25.0 3.0 -sub-CLNC1592 ses-M00 MCI 2.0 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M03 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M06 MCI 2.0 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M12 MCI 2.0 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M18 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M24 MCI 2.0 76.21 M 2.0 25.0 2.0 -sub-CLNC1592 ses-M36 MCI 2.0 76.21 M 2.0 25.0 2.0 -sub-CLNC1593 ses-M00 MCI 2.0 71.11 M 4.0 20.0 0.5 -sub-CLNC1593 ses-M03 71.11 M 4.0 20.0 0.5 -sub-CLNC1593 ses-M06 MCI 2.0 71.11 M 4.0 20.0 0.5 -sub-CLNC1593 ses-M12 5.0 71.11 M 4.0 20.0 0.5 -sub-CLNC1593 ses-M18 71.11 M 4.0 20.0 0.5 -sub-CLNC1594 ses-M00 MCI 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M06 MCI 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M12 MCI 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M18 AD 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M24 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M30 71.31 M 2.0 26.0 1.0 -sub-CLNC1594 ses-M36 71.31 M 2.0 26.0 1.0 -sub-CLNC1595 ses-M00 MCI 2.0 75.59 F 3.0 26.0 1.0 -sub-CLNC1595 ses-M03 75.59 F 3.0 26.0 1.0 -sub-CLNC1595 ses-M06 MCI 2.0 75.59 F 3.0 26.0 1.0 -sub-CLNC1595 ses-M12 MCI 2.0 75.59 F 3.0 26.0 1.0 -sub-CLNC1595 ses-M24 75.59 F 3.0 26.0 1.0 -sub-CLNC1596 ses-M00 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M03 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M06 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M12 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M24 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M36 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1596 ses-M48 MCI 2.0 56.98 M 8.0 24.0 3.0 -sub-CLNC1597 ses-M00 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M06 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M108 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M12 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M18 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M24 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M30 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M36 MCI 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M48 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M54 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M60 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M66 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M72 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M78 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M84 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1597 ses-M96 MCI 2.0 80.02 M 4.0 21.0 3.0 -sub-CLNC1598 ses-M00 CN 1.0 80.69 M 5.0 22.0 0.0 -sub-CLNC1598 ses-M03 80.69 M 5.0 22.0 0.0 -sub-CLNC1598 ses-M06 CN 1.0 80.69 M 5.0 22.0 0.0 -sub-CLNC1598 ses-M24 CN 1.0 80.69 M 5.0 22.0 0.0 -sub-CLNC1599 ses-M00 MCI 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M06 MCI 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M12 MCI 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M18 MCI 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M24 AD 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M30 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M36 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M48 AD 3.0 74.31 F 2.0 29.0 2.0 -sub-CLNC1599 ses-M54 74.31 F 2.0 29.0 2.0 -sub-CLNC1600 ses-M00 CN 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M06 CN 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M12 CN 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M18 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M24 CN 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M30 72.04 M 9.0 20.0 0.5 -sub-CLNC1600 ses-M36 CN 72.04 M 9.0 20.0 0.5 -sub-CLNC1601 ses-M00 MCI 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M06 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M12 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M18 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M24 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M30 75.55 F 9.0 21.0 0.5 -sub-CLNC1601 ses-M36 75.55 F 9.0 21.0 0.5 -sub-CLNC1602 ses-M00 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M06 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M12 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M18 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M24 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M30 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M36 MCI 68.42 M 6.0 24.0 0.5 -sub-CLNC1602 ses-M42 68.42 M 6.0 24.0 0.5 -sub-CLNC1603 ses-M00 MCI 2.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M03 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M06 MCI 2.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M12 5.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M18 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M24 AD 3.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M30 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M36 AD 3.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1603 ses-M48 AD 3.0 62.24 M 4.0 25.0 0.0 -sub-CLNC1604 ses-M00 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M06 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M12 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M18 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M24 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M30 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M36 MCI 70.24 M 3.0 27.0 3.0 -sub-CLNC1604 ses-M42 70.24 M 3.0 27.0 3.0 -sub-CLNC1605 ses-M00 MCI 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M06 MCI 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M12 MCI 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M18 MCI 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M24 MCI 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M30 76.94 F 3.0 26.0 2.0 -sub-CLNC1605 ses-M36 AD 76.94 F 3.0 26.0 2.0 -sub-CLNC1606 ses-M00 MCI 2.0 83.72 M 6.0 25.0 3.0 -sub-CLNC1607 ses-M00 CN 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M06 CN 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M12 CN 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M120 4.0 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M18 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M24 CN 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M30 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M36 CN 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M42 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M48 CN 1.0 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M72 CN 1.0 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M78 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M84 CN 1.0 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M90 82.14 M 3.0 22.0 1.0 -sub-CLNC1607 ses-M96 CN 1.0 82.14 M 3.0 22.0 1.0 -sub-CLNC1608 ses-M00 MCI 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M06 MCI 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M12 MCI 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M18 MCI 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M24 AD 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M30 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M36 AD 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M60 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M66 75.44 F 4.0 23.0 2.0 -sub-CLNC1608 ses-M72 75.44 F 4.0 23.0 2.0 -sub-CLNC1609 ses-M00 MCI 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M06 MCI 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M12 MCI 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M18 MCI 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M24 AD 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M30 65.65 F 6.0 29.0 3.0 -sub-CLNC1609 ses-M36 AD 65.65 F 6.0 29.0 3.0 -sub-CLNC1610 ses-M00 AD 3.0 87.22 F 9.0 29.0 3.0 -sub-CLNC1610 ses-M03 87.22 F 9.0 29.0 3.0 -sub-CLNC1610 ses-M06 AD 3.0 87.22 F 9.0 29.0 3.0 -sub-CLNC1610 ses-M12 AD 3.0 87.22 F 9.0 29.0 3.0 -sub-CLNC1611 ses-M00 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M06 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M108 AD 3.0 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M12 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M18 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M24 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M30 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M36 MCI 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M42 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M54 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M60 MCI 2.0 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M72 MCI 2.0 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M78 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M84 5.0 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M90 68.62 M 3.0 24.0 0.5 -sub-CLNC1611 ses-M96 AD 3.0 68.62 M 3.0 24.0 0.5 -sub-CLNC1612 ses-M00 MCI 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M06 MCI 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M108 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M12 MCI 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M18 MCI 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M24 AD 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M30 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M36 AD 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M42 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M48 AD 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M60 AD 3.0 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M66 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M72 AD 3.0 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M78 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M84 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M90 74.06 M 8.0 23.0 0.0 -sub-CLNC1612 ses-M96 74.06 M 8.0 23.0 0.0 -sub-CLNC1613 ses-M00 MCI 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M06 MCI 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M12 AD 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M18 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M24 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M30 69.06 M 3.0 22.0 0.0 -sub-CLNC1613 ses-M36 69.06 M 3.0 22.0 0.0 -sub-CLNC1614 ses-M00 MCI 2.0 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M03 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M06 MCI 2.0 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M12 MCI 2.0 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M18 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M24 5.0 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M36 AD 3.0 74.32 M 7.0 20.0 2.0 -sub-CLNC1614 ses-M48 74.32 M 7.0 20.0 2.0 -sub-CLNC1615 ses-M00 MCI 2.0 71.33 F 8.0 23.0 0.0 -sub-CLNC1615 ses-M03 71.33 F 8.0 23.0 0.0 -sub-CLNC1615 ses-M06 MCI 2.0 71.33 F 8.0 23.0 0.0 -sub-CLNC1615 ses-M12 MCI 2.0 71.33 F 8.0 23.0 0.0 -sub-CLNC1615 ses-M18 71.33 F 8.0 23.0 0.0 -sub-CLNC1616 ses-M00 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M06 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M12 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M18 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M24 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M30 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M36 MCI 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M42 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M48 5.0 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M54 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M60 AD 3.0 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M66 79.0 F 6.0 25.0 0.5 -sub-CLNC1616 ses-M72 5.0 79.0 F 6.0 25.0 0.5 -sub-CLNC1617 ses-M00 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M06 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M12 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M18 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M24 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M30 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M36 MCI 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M42 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M48 MCI 2.0 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M54 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M60 MCI 2.0 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M66 71.64 F 3.0 26.0 3.0 -sub-CLNC1617 ses-M78 71.64 F 3.0 26.0 3.0 -sub-CLNC1618 ses-M00 MCI 2.0 68.79 F 4.0 26.0 1.0 -sub-CLNC1618 ses-M03 68.79 F 4.0 26.0 1.0 -sub-CLNC1618 ses-M12 MCI 2.0 68.79 F 4.0 26.0 1.0 -sub-CLNC1618 ses-M36 MCI 2.0 68.79 F 4.0 26.0 1.0 -sub-CLNC1618 ses-M48 MCI 2.0 68.79 F 4.0 26.0 1.0 -sub-CLNC1619 ses-M00 AD 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M06 AD 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M12 AD 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M18 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M24 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M30 65.44 M 1.0 26.0 3.0 -sub-CLNC1619 ses-M36 65.44 M 1.0 26.0 3.0 -sub-CLNC1620 ses-M00 MCI 2.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M03 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M06 MCI 2.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M12 7.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M18 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M24 4.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M36 7.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1620 ses-M48 CN 1.0 58.62 F 4.0 20.0 3.0 -sub-CLNC1621 ses-M00 AD 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M06 AD 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M12 AD 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M18 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M24 AD 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M30 73.76 M 3.0 24.0 0.0 -sub-CLNC1621 ses-M36 73.76 M 3.0 24.0 0.0 -sub-CLNC1622 ses-M00 CN 1.0 84.93 M 5.0 26.0 1.0 -sub-CLNC1622 ses-M06 CN 1.0 84.93 M 5.0 26.0 1.0 -sub-CLNC1622 ses-M24 CN 1.0 84.93 M 5.0 26.0 1.0 -sub-CLNC1623 ses-M00 MCI 2.0 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M03 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M06 MCI 2.0 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M12 MCI 2.0 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M18 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M24 MCI 2.0 87.67 M 3.0 29.0 0.0 -sub-CLNC1623 ses-M30 87.67 M 3.0 29.0 0.0 -sub-CLNC1624 ses-M00 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M03 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M06 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M12 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M18 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M24 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M30 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M36 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M42 65.39 F 6.0 24.0 2.0 -sub-CLNC1624 ses-M48 MCI 2.0 65.39 F 6.0 24.0 2.0 -sub-CLNC1625 ses-M00 MCI 2.0 70.62 F 6.0 25.0 0.5 -sub-CLNC1625 ses-M03 70.62 F 6.0 25.0 0.5 -sub-CLNC1625 ses-M06 MCI 2.0 70.62 F 6.0 25.0 0.5 -sub-CLNC1626 ses-M00 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M03 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M06 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M12 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M18 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M24 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M36 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1626 ses-M48 MCI 2.0 64.35 M 6.0 28.0 0.5 -sub-CLNC1627 ses-M00 AD 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M06 AD 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M12 AD 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M18 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M24 AD 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M30 75.28 F 5.0 25.0 3.0 -sub-CLNC1627 ses-M36 75.28 F 5.0 25.0 3.0 -sub-CLNC1628 ses-M00 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M06 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M108 MCI 2.0 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M12 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M18 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M24 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M30 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M36 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M42 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M48 MCI 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M54 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M60 MCI 2.0 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M66 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M72 MCI 2.0 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M78 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M84 MCI 2.0 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M90 85.59 F 4.0 21.0 2.0 -sub-CLNC1628 ses-M96 MCI 2.0 85.59 F 4.0 21.0 2.0 -sub-CLNC1629 ses-M00 CN 1.0 73.7 M 2.0 27.0 0.0 -sub-CLNC1629 ses-M06 CN 1.0 73.7 M 2.0 27.0 0.0 -sub-CLNC1629 ses-M24 CN 1.0 73.7 M 2.0 27.0 0.0 -sub-CLNC1630 ses-M00 MCI 2.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M03 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M06 MCI 2.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M12 MCI 2.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M18 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M24 5.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M36 AD 3.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1630 ses-M48 AD 3.0 64.65 F 2.0 20.0 1.0 -sub-CLNC1631 ses-M00 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M03 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M06 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M12 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M24 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M36 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1631 ses-M48 MCI 2.0 74.45 M 9.0 21.0 2.0 -sub-CLNC1632 ses-M00 MCI 2.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M03 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M06 MCI 2.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M12 MCI 2.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M18 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M24 MCI 2.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M30 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M36 7.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M48 4.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1632 ses-M60 MCI 2.0 66.34 M 6.0 28.0 0.0 -sub-CLNC1633 ses-M00 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M06 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M102 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M12 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M120 CN 1.0 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M18 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M24 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M30 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M36 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M42 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M48 CN 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M54 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M60 CN 1.0 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M66 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M72 CN 1.0 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M78 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M84 CN 1.0 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M90 70.99 F 2.0 28.0 1.0 -sub-CLNC1633 ses-M96 CN 1.0 70.99 F 2.0 28.0 1.0 -sub-CLNC1634 ses-M00 AD 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M06 AD 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M12 AD 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M18 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M24 AD 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M30 73.34 M 7.0 29.0 0.0 -sub-CLNC1634 ses-M36 73.34 M 7.0 29.0 0.0 -sub-CLNC1635 ses-M00 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M03 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M06 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M12 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M18 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M24 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M30 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M36 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M42 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M48 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1635 ses-M60 MCI 2.0 68.67 F 5.0 25.0 0.5 -sub-CLNC1636 ses-M00 CN 1.0 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M03 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M06 CN 1.0 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M12 CN 1.0 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M18 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M24 CN 1.0 69.18 M 1.0 23.0 2.0 -sub-CLNC1636 ses-M36 CN 1.0 69.18 M 1.0 23.0 2.0 -sub-CLNC1637 ses-M00 CN 1.0 69.25 F 2.0 23.0 2.0 -sub-CLNC1637 ses-M03 69.25 F 2.0 23.0 2.0 -sub-CLNC1637 ses-M06 CN 1.0 69.25 F 2.0 23.0 2.0 -sub-CLNC1637 ses-M12 CN 1.0 69.25 F 2.0 23.0 2.0 -sub-CLNC1637 ses-M24 CN 1.0 69.25 F 2.0 23.0 2.0 -sub-CLNC1638 ses-M00 CN 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M06 CN 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M108 CN 1.0 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M12 CN 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M18 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M24 CN 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M30 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M36 CN 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M42 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M48 CN 1.0 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M54 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M60 CN 1.0 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M66 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M72 CN 1.0 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M78 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M84 CN 1.0 76.09 F 8.0 23.0 2.0 -sub-CLNC1638 ses-M90 76.09 F 8.0 23.0 2.0 -sub-CLNC1639 ses-M00 MCI 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M06 MCI 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M12 MCI 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M18 MCI 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M24 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M30 65.5 M 4.0 23.0 1.0 -sub-CLNC1639 ses-M36 65.5 M 4.0 23.0 1.0 -sub-CLNC1640 ses-M00 CN 1.0 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M03 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M06 CN 1.0 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M12 CN 1.0 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M18 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M24 CN 1.0 69.77 F 9.0 25.0 0.5 -sub-CLNC1640 ses-M48 CN 1.0 69.77 F 9.0 25.0 0.5 -sub-CLNC1641 ses-M00 MCI 2.0 82.01 F 5.0 26.0 3.0 -sub-CLNC1641 ses-M03 82.01 F 5.0 26.0 3.0 -sub-CLNC1641 ses-M06 MCI 2.0 82.01 F 5.0 26.0 3.0 -sub-CLNC1641 ses-M12 5.0 82.01 F 5.0 26.0 3.0 -sub-CLNC1641 ses-M18 82.01 F 5.0 26.0 3.0 -sub-CLNC1641 ses-M24 AD 3.0 82.01 F 5.0 26.0 3.0 -sub-CLNC1642 ses-M00 MCI 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M06 MCI 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M12 MCI 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M18 MCI 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M24 AD 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M30 77.21 M 8.0 29.0 0.5 -sub-CLNC1642 ses-M36 AD 77.21 M 8.0 29.0 0.5 -sub-CLNC1643 ses-M00 MCI 2.0 79.87 M 7.0 23.0 0.5 -sub-CLNC1643 ses-M03 79.87 M 7.0 23.0 0.5 -sub-CLNC1643 ses-M06 MCI 2.0 79.87 M 7.0 23.0 0.5 -sub-CLNC1644 ses-M00 AD 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M06 AD 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M12 AD 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M18 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M24 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M30 71.48 F 7.0 22.0 3.0 -sub-CLNC1644 ses-M36 71.48 F 7.0 22.0 3.0 -sub-CLNC1645 ses-M00 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M03 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M06 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M12 7.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M18 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M24 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M30 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M36 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M48 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1645 ses-M60 MCI 2.0 76.77 M 7.0 29.0 2.0 -sub-CLNC1646 ses-M00 CN 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M06 CN 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M12 CN 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M18 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M24 CN 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M30 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M36 CN 68.68 M 1.0 28.0 2.0 -sub-CLNC1646 ses-M42 68.68 M 1.0 28.0 2.0 -sub-CLNC1647 ses-M00 MCI 2.0 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M03 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M06 MCI 2.0 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M12 MCI 2.0 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M18 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M24 MCI 2.0 89.19 M 3.0 20.0 0.5 -sub-CLNC1647 ses-M30 89.19 M 3.0 20.0 0.5 -sub-CLNC1648 ses-M00 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M03 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M06 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M12 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M18 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M24 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M30 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M36 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1648 ses-M48 MCI 2.0 73.49 F 8.0 28.0 1.0 -sub-CLNC1649 ses-M00 MCI 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M06 MCI 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M12 MCI 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M18 MCI 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M24 MCI 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M30 69.51 M 5.0 22.0 0.5 -sub-CLNC1649 ses-M36 69.51 M 5.0 22.0 0.5 -sub-CLNC1650 ses-M00 MCI 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M06 MCI 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M12 MCI 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M18 MCI 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M24 MCI 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M30 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M36 AD 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M42 79.63 F 2.0 20.0 1.0 -sub-CLNC1650 ses-M48 AD 79.63 F 2.0 20.0 1.0 -sub-CLNC1651 ses-M00 MCI 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M06 MCI 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M12 AD 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M18 AD 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M24 AD 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M30 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M36 AD 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M42 68.69 M 8.0 24.0 3.0 -sub-CLNC1651 ses-M48 AD 68.69 M 8.0 24.0 3.0 -sub-CLNC1652 ses-M00 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M03 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M06 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M12 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M18 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M24 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M36 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1652 ses-M48 MCI 2.0 72.35 F 6.0 29.0 2.0 -sub-CLNC1653 ses-M00 AD 64.08 M 7.0 25.0 1.0 -sub-CLNC1653 ses-M06 AD 64.08 M 7.0 25.0 1.0 -sub-CLNC1654 ses-M00 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M06 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M108 CN 1.0 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M12 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M18 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M24 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M30 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M36 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M42 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M48 CN 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M60 CN 1.0 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M66 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M72 CN 1.0 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M78 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M84 CN 1.0 83.65 M 5.0 27.0 0.0 -sub-CLNC1654 ses-M90 83.65 M 5.0 27.0 0.0 -sub-CLNC1655 ses-M00 MCI 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M06 MCI 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M12 AD 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M18 AD 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M24 AD 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M30 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M36 AD 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M42 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M48 AD 3.0 76.88 M 9.0 29.0 3.0 -sub-CLNC1655 ses-M54 76.88 M 9.0 29.0 3.0 -sub-CLNC1656 ses-M00 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M03 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M06 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M12 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M18 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M24 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M30 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M36 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1656 ses-M48 MCI 2.0 66.48 F 7.0 29.0 3.0 -sub-CLNC1657 ses-M00 MCI 2.0 77.54 M 8.0 21.0 1.0 -sub-CLNC1657 ses-M03 77.54 M 8.0 21.0 1.0 -sub-CLNC1657 ses-M06 MCI 2.0 77.54 M 8.0 21.0 1.0 -sub-CLNC1657 ses-M12 MCI 2.0 77.54 M 8.0 21.0 1.0 -sub-CLNC1657 ses-M18 77.54 M 8.0 21.0 1.0 -sub-CLNC1658 ses-M00 CN 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M06 CN 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M108 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M12 CN 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M18 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M24 CN 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M30 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M36 CN 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M42 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M48 CN 1.0 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M54 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M60 CN 1.0 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M66 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M72 CN 1.0 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M78 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M84 67.58 M 6.0 21.0 1.0 -sub-CLNC1658 ses-M90 67.58 M 6.0 21.0 1.0 -sub-CLNC1659 ses-M00 CN 1.0 76.35 F 5.0 27.0 2.0 -sub-CLNC1659 ses-M06 CN 1.0 76.35 F 5.0 27.0 2.0 -sub-CLNC1659 ses-M24 CN 1.0 76.35 F 5.0 27.0 2.0 -sub-CLNC1660 ses-M00 CN 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M06 CN 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M108 MCI 2.0 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M12 CN 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M18 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M24 CN 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M30 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M36 CN 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M42 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M48 CN 1.0 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M60 4.0 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M66 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M72 MCI 2.0 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M78 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M84 MCI 2.0 68.76 M 1.0 22.0 1.0 -sub-CLNC1660 ses-M90 68.76 M 1.0 22.0 1.0 -sub-CLNC1661 ses-M00 MCI 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M06 MCI 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M12 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M18 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M24 MCI 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M30 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M36 5.0 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M48 AD 3.0 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M54 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M60 AD 3.0 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M66 66.08 M 3.0 21.0 1.0 -sub-CLNC1661 ses-M72 AD 3.0 66.08 M 3.0 21.0 1.0 -sub-CLNC1662 ses-M00 CN 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M06 CN 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M12 CN 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M18 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M24 CN 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M30 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M36 CN 74.1 M 1.0 25.0 2.0 -sub-CLNC1662 ses-M42 74.1 M 1.0 25.0 2.0 -sub-CLNC1663 ses-M00 CN 1.0 66.39 M 4.0 21.0 2.0 -sub-CLNC1663 ses-M06 CN 1.0 66.39 M 4.0 21.0 2.0 -sub-CLNC1663 ses-M24 CN 1.0 66.39 M 4.0 21.0 2.0 -sub-CLNC1664 ses-M00 MCI 2.0 76.35 F 2.0 26.0 3.0 -sub-CLNC1664 ses-M03 76.35 F 2.0 26.0 3.0 -sub-CLNC1664 ses-M06 MCI 2.0 76.35 F 2.0 26.0 3.0 -sub-CLNC1664 ses-M12 MCI 2.0 76.35 F 2.0 26.0 3.0 -sub-CLNC1664 ses-M24 MCI 2.0 76.35 F 2.0 26.0 3.0 -sub-CLNC1664 ses-M36 MCI 2.0 76.35 F 2.0 26.0 3.0 -sub-CLNC1665 ses-M00 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M06 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M108 CN 1.0 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M12 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M18 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M24 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M30 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M36 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M42 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M48 CN 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M54 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M60 CN 1.0 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M66 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M72 CN 1.0 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M78 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M84 CN 1.0 67.37 M 7.0 21.0 1.0 -sub-CLNC1665 ses-M90 67.37 M 7.0 21.0 1.0 -sub-CLNC1666 ses-M00 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M06 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M12 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M18 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M24 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M30 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M36 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M42 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M48 CN 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M54 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M60 CN 1.0 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M66 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M72 CN 1.0 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M90 70.81 M 2.0 25.0 2.0 -sub-CLNC1666 ses-M96 CN 1.0 70.81 M 2.0 25.0 2.0 -sub-CLNC1667 ses-M00 CN 1.0 74.0 M 5.0 22.0 0.5 -sub-CLNC1667 ses-M03 74.0 M 5.0 22.0 0.5 -sub-CLNC1667 ses-M06 CN 1.0 74.0 M 5.0 22.0 0.5 -sub-CLNC1667 ses-M12 CN 1.0 74.0 M 5.0 22.0 0.5 -sub-CLNC1667 ses-M24 CN 1.0 74.0 M 5.0 22.0 0.5 -sub-CLNC1667 ses-M36 CN 1.0 74.0 M 5.0 22.0 0.5 -sub-CLNC1668 ses-M00 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M06 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M12 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M18 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M24 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M30 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M36 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M42 82.69 M 3.0 28.0 2.0 -sub-CLNC1668 ses-M48 AD 82.69 M 3.0 28.0 2.0 -sub-CLNC1669 ses-M00 AD 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M06 AD 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M12 AD 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M18 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M24 AD 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M30 70.22 M 4.0 21.0 0.0 -sub-CLNC1669 ses-M36 70.22 M 4.0 21.0 0.0 -sub-CLNC1670 ses-M00 AD 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M06 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M12 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M18 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M24 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M30 69.18 F 8.0 20.0 1.0 -sub-CLNC1670 ses-M36 69.18 F 8.0 20.0 1.0 -sub-CLNC1671 ses-M00 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M03 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M06 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M12 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M18 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M24 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M36 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1671 ses-M48 MCI 2.0 81.57 M 6.0 20.0 1.0 -sub-CLNC1672 ses-M00 MCI 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M06 MCI 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M108 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M12 AD 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M18 AD 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M24 AD 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M30 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M36 AD 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M42 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M48 AD 3.0 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M54 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M60 AD 3.0 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M66 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M72 AD 3.0 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M78 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M84 AD 3.0 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M90 61.22 M 5.0 21.0 0.0 -sub-CLNC1672 ses-M96 61.22 M 5.0 21.0 0.0 -sub-CLNC1673 ses-M00 MCI 2.0 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M03 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M06 MCI 2.0 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M12 MCI 2.0 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M24 MCI 2.0 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M30 80.89 M 8.0 29.0 3.0 -sub-CLNC1673 ses-M36 MCI 2.0 80.89 M 8.0 29.0 3.0 -sub-CLNC1674 ses-M00 CN 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M06 CN 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M12 CN 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M18 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M24 CN 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M30 85.17 M 8.0 22.0 0.5 -sub-CLNC1674 ses-M36 CN 85.17 M 8.0 22.0 0.5 -sub-CLNC1675 ses-M00 MCI 2.0 71.25 F 8.0 25.0 1.0 -sub-CLNC1676 ses-M00 CN 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M06 CN 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M108 CN 1.0 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M12 CN 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M18 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M24 CN 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M30 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M36 CN 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M42 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M54 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M60 CN 1.0 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M66 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M72 CN 1.0 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M78 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M84 CN 1.0 65.53 M 6.0 29.0 1.0 -sub-CLNC1676 ses-M90 65.53 M 6.0 29.0 1.0 -sub-CLNC1677 ses-M00 AD 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M06 AD 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M12 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M18 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M24 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M30 81.64 F 7.0 20.0 1.0 -sub-CLNC1677 ses-M36 81.64 F 7.0 20.0 1.0 -sub-CLNC1678 ses-M00 AD 3.0 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M03 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M06 AD 3.0 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M12 AD 3.0 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M18 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M24 AD 3.0 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M30 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M36 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M42 85.93 F 2.0 29.0 3.0 -sub-CLNC1678 ses-M48 85.93 F 2.0 29.0 3.0 -sub-CLNC1679 ses-M00 MCI 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M06 MCI 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M12 AD 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M18 AD 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M24 AD 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M30 72.47 M 6.0 27.0 2.0 -sub-CLNC1679 ses-M36 72.47 M 6.0 27.0 2.0 -sub-CLNC1680 ses-M00 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M03 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M06 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M12 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M18 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M24 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M30 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M36 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1680 ses-M48 MCI 2.0 84.7 F 1.0 23.0 0.0 -sub-CLNC1681 ses-M00 AD 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M06 AD 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M12 AD 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M18 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M24 AD 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M30 83.19 M 7.0 25.0 3.0 -sub-CLNC1681 ses-M36 83.19 M 7.0 25.0 3.0 -sub-CLNC1682 ses-M00 CN 1.0 84.25 M 2.0 24.0 0.5 -sub-CLNC1682 ses-M03 84.25 M 2.0 24.0 0.5 -sub-CLNC1682 ses-M06 CN 1.0 84.25 M 2.0 24.0 0.5 -sub-CLNC1682 ses-M24 CN 1.0 84.25 M 2.0 24.0 0.5 -sub-CLNC1683 ses-M00 CN 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M06 CN 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M12 CN 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M18 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M24 CN 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M30 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M36 CN 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M48 CN 1.0 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M54 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M60 CN 1.0 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M66 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M78 66.98 F 6.0 20.0 0.0 -sub-CLNC1683 ses-M84 CN 1.0 66.98 F 6.0 20.0 0.0 -sub-CLNC1684 ses-M00 AD 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M06 AD 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M12 AD 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M18 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M24 AD 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M30 90.26 F 3.0 27.0 1.0 -sub-CLNC1684 ses-M36 90.26 F 3.0 27.0 1.0 -sub-CLNC1685 ses-M00 AD 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M06 AD 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M12 AD 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M18 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M24 AD 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M30 78.51 M 1.0 28.0 3.0 -sub-CLNC1685 ses-M36 78.51 M 1.0 28.0 3.0 -sub-CLNC1686 ses-M00 CN 1.0 76.75 F 5.0 22.0 0.5 -sub-CLNC1686 ses-M06 CN 1.0 76.75 F 5.0 22.0 0.5 -sub-CLNC1686 ses-M24 CN 1.0 76.75 F 5.0 22.0 0.5 -sub-CLNC1687 ses-M00 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M06 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M12 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M18 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M24 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M30 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M36 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M48 CN 66.02 M 5.0 21.0 3.0 -sub-CLNC1687 ses-M60 CN 1.0 66.02 M 5.0 21.0 3.0 -sub-CLNC1688 ses-M00 MCI 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M06 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M12 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M18 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M24 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M30 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M36 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M42 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M48 AD 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M54 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M60 AD 3.0 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M66 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M72 AD 3.0 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M78 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M84 AD 3.0 87.34 F 3.0 29.0 1.0 -sub-CLNC1688 ses-M90 87.34 F 3.0 29.0 1.0 -sub-CLNC1689 ses-M00 MCI 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M06 MCI 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M12 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M18 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M24 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M30 59.35 M 8.0 21.0 1.0 -sub-CLNC1689 ses-M36 59.35 M 8.0 21.0 1.0 -sub-CLNC1690 ses-M00 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M03 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M06 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M12 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M18 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M24 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M30 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M36 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M48 MCI 2.0 78.03 M 3.0 22.0 0.0 -sub-CLNC1690 ses-M60 78.03 M 3.0 22.0 0.0 -sub-CLNC1691 ses-M00 CN 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M06 CN 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M12 CN 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M18 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M24 CN 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M30 78.51 F 9.0 27.0 2.0 -sub-CLNC1691 ses-M36 78.51 F 9.0 27.0 2.0 -sub-CLNC1692 ses-M00 CN 1.0 89.44 M 2.0 22.0 1.0 -sub-CLNC1692 ses-M06 CN 1.0 89.44 M 2.0 22.0 1.0 -sub-CLNC1693 ses-M00 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M03 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M06 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M12 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M18 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M24 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M36 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1693 ses-M48 MCI 2.0 68.69 M 4.0 20.0 0.5 -sub-CLNC1694 ses-M00 AD 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M06 AD 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M12 AD 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M18 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M24 AD 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M30 66.54 M 1.0 26.0 0.0 -sub-CLNC1694 ses-M36 66.54 M 1.0 26.0 0.0 -sub-CLNC1695 ses-M00 MCI 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M06 MCI 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M12 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M18 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M24 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M30 80.74 M 7.0 20.0 0.0 -sub-CLNC1695 ses-M36 80.74 M 7.0 20.0 0.0 -sub-CLNC1696 ses-M00 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M06 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M12 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M18 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M24 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M30 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M36 MCI 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M42 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M48 MCI 2.0 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M54 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M60 MCI 2.0 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M78 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M84 73.0 F 8.0 20.0 0.5 -sub-CLNC1696 ses-M96 73.0 F 8.0 20.0 0.5 -sub-CLNC1697 ses-M00 AD 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M06 AD 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M12 AD 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M18 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M24 AD 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M30 81.35 F 4.0 20.0 3.0 -sub-CLNC1697 ses-M36 81.35 F 4.0 20.0 3.0 -sub-CLNC1698 ses-M00 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M06 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M12 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M120 CN 1.0 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M18 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M24 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M30 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M36 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M48 CN 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M60 CN 1.0 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M72 CN 1.0 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M84 CN 1.0 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M90 77.16 F 8.0 23.0 0.0 -sub-CLNC1698 ses-M96 CN 1.0 77.16 F 8.0 23.0 0.0 -sub-CLNC1699 ses-M00 MCI 2.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M03 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M06 MCI 2.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M12 MCI 2.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M24 MCI 2.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M36 7.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1699 ses-M48 MCI 2.0 73.11 M 2.0 21.0 0.0 -sub-CLNC1700 ses-M00 AD 72.1 M 3.0 28.0 2.0 -sub-CLNC1700 ses-M06 AD 72.1 M 3.0 28.0 2.0 -sub-CLNC1700 ses-M12 AD 72.1 M 3.0 28.0 2.0 -sub-CLNC1700 ses-M18 72.1 M 3.0 28.0 2.0 -sub-CLNC1700 ses-M24 AD 72.1 M 3.0 28.0 2.0 -sub-CLNC1701 ses-M00 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M03 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M06 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M12 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M18 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M24 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M36 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1701 ses-M48 MCI 2.0 80.84 M 7.0 23.0 3.0 -sub-CLNC1702 ses-M00 AD 3.0 83.01 F 8.0 28.0 3.0 -sub-CLNC1702 ses-M03 83.01 F 8.0 28.0 3.0 -sub-CLNC1702 ses-M06 AD 3.0 83.01 F 8.0 28.0 3.0 -sub-CLNC1702 ses-M12 AD 3.0 83.01 F 8.0 28.0 3.0 -sub-CLNC1702 ses-M18 83.01 F 8.0 28.0 3.0 -sub-CLNC1703 ses-M00 CN 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M06 CN 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M12 CN 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M18 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M24 CN 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M30 78.07 F 7.0 26.0 0.5 -sub-CLNC1703 ses-M36 MCI 78.07 F 7.0 26.0 0.5 -sub-CLNC1704 ses-M00 MCI 2.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M03 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M06 MCI 2.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M12 MCI 2.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M18 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M24 5.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M36 AD 3.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1704 ses-M48 AD 3.0 73.46 M 9.0 27.0 0.0 -sub-CLNC1705 ses-M00 CN 1.0 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M03 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M06 CN 1.0 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M12 CN 1.0 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M18 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M24 CN 1.0 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M30 75.43 M 5.0 22.0 3.0 -sub-CLNC1705 ses-M48 CN 1.0 75.43 M 5.0 22.0 3.0 -sub-CLNC1706 ses-M00 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M03 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M06 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M12 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M18 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M24 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M30 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M36 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1706 ses-M48 MCI 2.0 55.11 F 1.0 27.0 0.0 -sub-CLNC1707 ses-M00 MCI 2.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M03 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M06 MCI 2.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M12 MCI 2.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M18 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M24 MCI 2.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M36 7.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1707 ses-M48 CN 1.0 65.13 M 1.0 22.0 2.0 -sub-CLNC1708 ses-M00 AD 3.0 76.86 M 9.0 22.0 3.0 -sub-CLNC1708 ses-M03 76.86 M 9.0 22.0 3.0 -sub-CLNC1708 ses-M06 AD 3.0 76.86 M 9.0 22.0 3.0 -sub-CLNC1708 ses-M12 AD 3.0 76.86 M 9.0 22.0 3.0 -sub-CLNC1709 ses-M00 MCI 2.0 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M03 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M06 MCI 2.0 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M12 MCI 2.0 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M18 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M24 MCI 2.0 84.3 F 6.0 25.0 2.0 -sub-CLNC1709 ses-M36 MCI 2.0 84.3 F 6.0 25.0 2.0 -sub-CLNC1710 ses-M00 MCI 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M06 AD 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M12 AD 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M18 AD 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M24 AD 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M30 66.77 F 7.0 22.0 3.0 -sub-CLNC1710 ses-M36 AD 66.77 F 7.0 22.0 3.0 -sub-CLNC1711 ses-M00 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M03 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M06 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M12 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M24 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M36 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1711 ses-M48 MCI 2.0 67.86 F 1.0 20.0 0.5 -sub-CLNC1712 ses-M00 MCI 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M06 MCI 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M12 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M18 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M24 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M30 71.24 F 2.0 20.0 0.0 -sub-CLNC1712 ses-M36 71.24 F 2.0 20.0 0.0 -sub-CLNC1713 ses-M00 CN 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M06 CN 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M12 CN 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M18 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M24 CN 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M30 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M36 67.9 M 5.0 21.0 0.5 -sub-CLNC1713 ses-M42 67.9 M 5.0 21.0 0.5 -sub-CLNC1714 ses-M00 CN 1.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M03 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M06 4.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M12 MCI 2.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M18 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M24 5.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M30 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M36 AD 3.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M48 AD 3.0 69.07 F 5.0 28.0 3.0 -sub-CLNC1714 ses-M60 69.07 F 5.0 28.0 3.0 -sub-CLNC1715 ses-M00 MCI 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M06 MCI 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M12 MCI 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M18 MCI 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M24 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M30 64.3 M 8.0 21.0 0.5 -sub-CLNC1715 ses-M36 64.3 M 8.0 21.0 0.5 -sub-CLNC1716 ses-M00 CN 1.0 78.32 M 3.0 25.0 2.0 -sub-CLNC1716 ses-M03 78.32 M 3.0 25.0 2.0 -sub-CLNC1716 ses-M06 CN 1.0 78.32 M 3.0 25.0 2.0 -sub-CLNC1716 ses-M24 CN 1.0 78.32 M 3.0 25.0 2.0 -sub-CLNC1717 ses-M00 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M03 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M06 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M12 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M18 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M24 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M30 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M36 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M48 MCI 2.0 66.6 M 8.0 27.0 2.0 -sub-CLNC1717 ses-M60 66.6 M 8.0 27.0 2.0 -sub-CLNC1718 ses-M00 MCI 2.0 72.32 M 7.0 26.0 0.5 -sub-CLNC1718 ses-M03 72.32 M 7.0 26.0 0.5 -sub-CLNC1718 ses-M06 MCI 2.0 72.32 M 7.0 26.0 0.5 -sub-CLNC1718 ses-M12 MCI 2.0 72.32 M 7.0 26.0 0.5 -sub-CLNC1718 ses-M18 72.32 M 7.0 26.0 0.5 -sub-CLNC1718 ses-M24 MCI 2.0 72.32 M 7.0 26.0 0.5 -sub-CLNC1719 ses-M00 CN 63.49 M 5.0 25.0 3.0 -sub-CLNC1719 ses-M06 CN 63.49 M 5.0 25.0 3.0 -sub-CLNC1719 ses-M18 63.49 M 5.0 25.0 3.0 -sub-CLNC1720 ses-M00 MCI 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M06 MCI 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M12 AD 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M18 AD 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M24 AD 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M30 63.19 F 1.0 28.0 1.0 -sub-CLNC1720 ses-M36 AD 63.19 F 1.0 28.0 1.0 -sub-CLNC1721 ses-M00 AD 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M06 AD 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M12 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M18 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M24 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M30 85.12 F 9.0 22.0 2.0 -sub-CLNC1721 ses-M36 85.12 F 9.0 22.0 2.0 -sub-CLNC1722 ses-M00 AD 71.19 F 3.0 20.0 1.0 -sub-CLNC1722 ses-M06 AD 71.19 F 3.0 20.0 1.0 -sub-CLNC1722 ses-M12 AD 71.19 F 3.0 20.0 1.0 -sub-CLNC1722 ses-M18 71.19 F 3.0 20.0 1.0 -sub-CLNC1722 ses-M24 AD 71.19 F 3.0 20.0 1.0 -sub-CLNC1723 ses-M00 MCI 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M06 MCI 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M12 MCI 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M18 MCI 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M24 MCI 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M30 73.85 F 7.0 26.0 1.0 -sub-CLNC1723 ses-M36 73.85 F 7.0 26.0 1.0 -sub-CLNC1724 ses-M00 CN 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M06 CN 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M12 CN 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M18 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M24 CN 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M30 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M36 AD 80.82 F 2.0 29.0 1.0 -sub-CLNC1724 ses-M42 80.82 F 2.0 29.0 1.0 -sub-CLNC1725 ses-M00 CN 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M06 CN 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M12 CN 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M18 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M24 CN 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M30 56.74 M 8.0 29.0 0.5 -sub-CLNC1725 ses-M36 MCI 56.74 M 8.0 29.0 0.5 -sub-CLNC1726 ses-M00 CN 1.0 68.12 F 4.0 25.0 2.0 -sub-CLNC1726 ses-M06 CN 1.0 68.12 F 4.0 25.0 2.0 -sub-CLNC1726 ses-M24 CN 1.0 68.12 F 4.0 25.0 2.0 -sub-CLNC1727 ses-M00 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M06 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M108 5.0 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M12 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M18 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M24 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M30 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M36 MCI 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M72 MCI 2.0 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M78 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M84 MCI 2.0 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M90 88.31 F 5.0 27.0 3.0 -sub-CLNC1727 ses-M96 5.0 88.31 F 5.0 27.0 3.0 -sub-CLNC1728 ses-M00 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M06 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M12 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M18 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M24 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M30 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M36 MCI 82.83 M 9.0 28.0 1.0 -sub-CLNC1728 ses-M42 82.83 M 9.0 28.0 1.0 -sub-CLNC1729 ses-M00 AD 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M06 AD 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M12 AD 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M18 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M24 AD 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M30 74.09 M 2.0 23.0 1.0 -sub-CLNC1729 ses-M36 74.09 M 2.0 23.0 1.0 -sub-CLNC1730 ses-M00 MCI 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M06 MCI 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M12 MCI 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M18 MCI 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M24 AD 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M30 82.34 M 3.0 21.0 2.0 -sub-CLNC1730 ses-M36 82.34 M 3.0 21.0 2.0 -sub-CLNC1731 ses-M00 MCI 2.0 75.5 F 3.0 21.0 3.0 -sub-CLNC1731 ses-M03 75.5 F 3.0 21.0 3.0 -sub-CLNC1731 ses-M06 MCI 2.0 75.5 F 3.0 21.0 3.0 -sub-CLNC1731 ses-M12 MCI 2.0 75.5 F 3.0 21.0 3.0 -sub-CLNC1731 ses-M18 75.5 F 3.0 21.0 3.0 -sub-CLNC1731 ses-M24 MCI 2.0 75.5 F 3.0 21.0 3.0 -sub-CLNC1732 ses-M00 CN 1.0 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M03 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M06 CN 1.0 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M12 CN 1.0 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M18 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M24 CN 1.0 64.33 M 6.0 21.0 0.0 -sub-CLNC1732 ses-M48 CN 1.0 64.33 M 6.0 21.0 0.0 -sub-CLNC1733 ses-M00 CN 1.0 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M03 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M06 CN 1.0 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M12 CN 1.0 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M18 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M24 CN 1.0 80.01 M 9.0 28.0 0.0 -sub-CLNC1733 ses-M48 CN 1.0 80.01 M 9.0 28.0 0.0 -sub-CLNC1734 ses-M00 CN 1.0 61.02 M 2.0 29.0 3.0 -sub-CLNC1735 ses-M00 AD 3.0 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M03 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M06 AD 3.0 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M12 AD 3.0 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M18 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M24 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M30 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M36 63.36 M 5.0 24.0 1.0 -sub-CLNC1735 ses-M42 63.36 M 5.0 24.0 1.0 -sub-CLNC1736 ses-M00 AD 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M06 AD 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M12 AD 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M18 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M24 AD 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M30 85.91 M 8.0 23.0 3.0 -sub-CLNC1736 ses-M36 85.91 M 8.0 23.0 3.0 diff --git a/tests/data/tsvtool/anonymous_analysis.tsv b/tests/data/tsvtool/anonymous_analysis.tsv deleted file mode 100644 index 2ced6a370..000000000 --- a/tests/data/tsvtool/anonymous_analysis.tsv +++ /dev/null @@ -1,6 +0,0 @@ -diagnosis n_subjects mean_age std_age min_age max_age sexF sexM mean_MMSE std_MMSE min_MMSE max_MMSE CDR_0 CDR_0.5 CDR_1 CDR_2 CDR_3 mean_scans std_scans n_scans -AD 337.0 73.90655786350148 7.333276783886249 51.34 99.69 167.0 170.0 24.661721068249257 2.796477331433921 20.0 29.0 70.0 55.0 64.0 74.0 74.0 3.311572700296736 1.1507768843668198 1116.0 -CN 426.0 74.26661971830987 7.232327843681067 51.09 94.2 211.0 215.0 24.485915492957748 2.8035923857007155 20.0 29.0 80.0 79.0 85.0 97.0 85.0 4.894366197183099 2.3963227300309535 2085.0 -MCI 788.0 74.14214467005075 7.045079273790639 53.37 94.74 406.0 382.0 24.401015228426395 2.947355174153725 20.0 29.0 140.0 171.0 169.0 150.0 158.0 4.407360406091371 2.1575735188856924 3473.0 -pMCI 297.0 73.6876430976431 7.288101772179384 53.37 91.91 144.0 153.0 24.47138047138047 2.997196632953589 20.0 29.0 60.0 59.0 72.0 58.0 48.0 2.9393939393939394 1.2724867497594767 873.0 -sMCI 298.0 74.49838926174496 6.7756021617155495 54.94 94.04 159.0 139.0 24.453020134228186 2.8800647142006857 20.0 29.0 47.0 74.0 53.0 61.0 63.0 3.5167785234899327 2.1491500640783605 1048.0 diff --git a/tests/data/tsvtool/anonymous_missing_mods/missing_mods_ses-M00.tsv b/tests/data/tsvtool/anonymous_missing_mods/missing_mods_ses-M00.tsv deleted file mode 100644 index 86bc72813..000000000 --- a/tests/data/tsvtool/anonymous_missing_mods/missing_mods_ses-M00.tsv +++ /dev/null @@ -1,1738 +0,0 @@ -participant_id t1w -sub-CLNC0000 1 -sub-CLNC0001 1 -sub-CLNC0002 1 -sub-CLNC0003 1 -sub-CLNC0004 1 -sub-CLNC0005 1 -sub-CLNC0006 1 -sub-CLNC0007 1 -sub-CLNC0008 1 -sub-CLNC0009 1 -sub-CLNC0010 1 -sub-CLNC0011 1 -sub-CLNC0012 1 -sub-CLNC0013 1 -sub-CLNC0014 1 -sub-CLNC0015 1 -sub-CLNC0016 1 -sub-CLNC0017 1 -sub-CLNC0018 1 -sub-CLNC0019 1 -sub-CLNC0020 1 -sub-CLNC0021 1 -sub-CLNC0022 1 -sub-CLNC0023 1 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M -sub-CLNC1087 ses-M12 MCI 75.45 M -sub-CLNC1087 ses-M18 MCI 75.45 M -sub-CLNC1087 ses-M24 MCI 75.45 M -sub-CLNC1087 ses-M36 MCI 75.45 M -sub-CLNC1087 ses-M60 MCI 75.45 M -sub-CLNC1087 ses-M72 MCI 75.45 M -sub-CLNC1087 ses-M84 MCI 75.45 M -sub-CLNC1088 ses-M00 MCI 59.54 M -sub-CLNC1090 ses-M00 MCI 78.46 F -sub-CLNC1090 ses-M03 MCI 78.46 F -sub-CLNC1090 ses-M06 MCI 78.46 F -sub-CLNC1090 ses-M12 MCI 78.46 F -sub-CLNC1090 ses-M24 MCI 78.46 F -sub-CLNC1095 ses-M00 MCI 72.52 M -sub-CLNC1095 ses-M03 MCI 72.52 M -sub-CLNC1095 ses-M06 MCI 72.52 M -sub-CLNC1095 ses-M12 MCI 72.52 M -sub-CLNC1095 ses-M24 MCI 72.52 M -sub-CLNC1097 ses-M00 MCI 73.45 M -sub-CLNC1097 ses-M03 MCI 73.45 M -sub-CLNC1097 ses-M06 MCI 73.45 M -sub-CLNC1097 ses-M12 MCI 73.45 M -sub-CLNC1097 ses-M24 MCI 73.45 M -sub-CLNC1097 ses-M48 MCI 73.45 M -sub-CLNC1099 ses-M00 MCI 86.84 M -sub-CLNC1103 ses-M00 MCI 75.36 M -sub-CLNC1103 ses-M06 MCI 75.36 M -sub-CLNC1104 ses-M00 MCI 71.81 M -sub-CLNC1104 ses-M03 MCI 71.81 M 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64.7 F -sub-CLNC1186 ses-M06 MCI 64.7 F -sub-CLNC1186 ses-M12 MCI 64.7 F -sub-CLNC1187 ses-M00 MCI 65.86 M -sub-CLNC1187 ses-M03 MCI 65.86 M -sub-CLNC1187 ses-M06 MCI 65.86 M -sub-CLNC1187 ses-M36 MCI 65.86 M -sub-CLNC1194 ses-M00 MCI 83.61 F -sub-CLNC1194 ses-M03 MCI 83.61 F -sub-CLNC1194 ses-M06 MCI 83.61 F -sub-CLNC1194 ses-M12 MCI 83.61 F -sub-CLNC1194 ses-M24 MCI 83.61 F -sub-CLNC1194 ses-M48 MCI 83.61 F -sub-CLNC1195 ses-M00 MCI 72.8 M -sub-CLNC1195 ses-M03 MCI 72.8 M -sub-CLNC1195 ses-M06 MCI 72.8 M -sub-CLNC1195 ses-M12 MCI 72.8 M -sub-CLNC1195 ses-M24 MCI 72.8 M -sub-CLNC1195 ses-M48 MCI 72.8 M -sub-CLNC1196 ses-M00 MCI 72.68 F -sub-CLNC1196 ses-M03 MCI 72.68 F -sub-CLNC1196 ses-M06 MCI 72.68 F -sub-CLNC1196 ses-M12 MCI 72.68 F -sub-CLNC1196 ses-M24 MCI 72.68 F -sub-CLNC1198 ses-M00 MCI 67.33 M -sub-CLNC1198 ses-M06 MCI 67.33 M -sub-CLNC1198 ses-M12 MCI 67.33 M -sub-CLNC1198 ses-M18 MCI 67.33 M -sub-CLNC1198 ses-M24 MCI 67.33 M -sub-CLNC1199 ses-M00 MCI 66.11 M -sub-CLNC1199 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76.1 F -sub-CLNC1222 ses-M03 MCI 76.1 F -sub-CLNC1222 ses-M06 MCI 76.1 F -sub-CLNC1222 ses-M12 MCI 76.1 F -sub-CLNC1226 ses-M00 MCI 81.64 M -sub-CLNC1226 ses-M12 MCI 81.64 M -sub-CLNC1227 ses-M00 MCI 76.83 M -sub-CLNC1227 ses-M06 MCI 76.83 M -sub-CLNC1227 ses-M12 MCI 76.83 M -sub-CLNC1228 ses-M00 MCI 80.52 M -sub-CLNC1228 ses-M06 MCI 80.52 M -sub-CLNC1229 ses-M00 MCI 74.59 F -sub-CLNC1229 ses-M03 MCI 74.59 F -sub-CLNC1229 ses-M12 MCI 74.59 F -sub-CLNC1229 ses-M24 MCI 74.59 F -sub-CLNC1230 ses-M00 MCI 70.77 M -sub-CLNC1230 ses-M06 MCI 70.77 M -sub-CLNC1230 ses-M12 MCI 70.77 M -sub-CLNC1230 ses-M18 MCI 70.77 M -sub-CLNC1233 ses-M00 MCI 68.14 F -sub-CLNC1233 ses-M06 MCI 68.14 F -sub-CLNC1233 ses-M12 MCI 68.14 F -sub-CLNC1233 ses-M18 MCI 68.14 F -sub-CLNC1233 ses-M24 MCI 68.14 F -sub-CLNC1233 ses-M48 MCI 68.14 F -sub-CLNC1233 ses-M60 MCI 68.14 F -sub-CLNC1233 ses-M72 MCI 68.14 F -sub-CLNC1233 ses-M84 MCI 68.14 F -sub-CLNC1235 ses-M00 MCI 76.56 M -sub-CLNC1235 ses-M06 MCI 76.56 M 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75.04 F -sub-CLNC1245 ses-M24 MCI 75.04 F -sub-CLNC1253 ses-M00 MCI 80.92 M -sub-CLNC1253 ses-M03 MCI 80.92 M -sub-CLNC1253 ses-M06 MCI 80.92 M -sub-CLNC1253 ses-M12 MCI 80.92 M -sub-CLNC1253 ses-M24 MCI 80.92 M -sub-CLNC1254 ses-M00 MCI 79.27 M -sub-CLNC1254 ses-M03 MCI 79.27 M -sub-CLNC1254 ses-M06 MCI 79.27 M -sub-CLNC1254 ses-M12 MCI 79.27 M -sub-CLNC1254 ses-M24 MCI 79.27 M -sub-CLNC1254 ses-M48 MCI 79.27 M -sub-CLNC1256 ses-M00 MCI 81.02 M -sub-CLNC1256 ses-M03 MCI 81.02 M -sub-CLNC1256 ses-M06 MCI 81.02 M -sub-CLNC1256 ses-M12 MCI 81.02 M -sub-CLNC1256 ses-M24 MCI 81.02 M -sub-CLNC1256 ses-M48 MCI 81.02 M -sub-CLNC1258 ses-M00 MCI 78.12 M -sub-CLNC1258 ses-M06 MCI 78.12 M -sub-CLNC1258 ses-M12 MCI 78.12 M -sub-CLNC1259 ses-M00 MCI 76.66 M -sub-CLNC1261 ses-M00 MCI 72.15 F -sub-CLNC1261 ses-M06 MCI 72.15 F -sub-CLNC1262 ses-M00 MCI 76.91 M -sub-CLNC1262 ses-M03 MCI 76.91 M -sub-CLNC1262 ses-M06 MCI 76.91 M -sub-CLNC1262 ses-M12 MCI 76.91 M -sub-CLNC1264 ses-M00 MCI 64.84 M 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F -sub-CLNC1406 ses-M00 MCI 83.84 F -sub-CLNC1406 ses-M03 MCI 83.84 F -sub-CLNC1406 ses-M06 MCI 83.84 F -sub-CLNC1406 ses-M12 MCI 83.84 F -sub-CLNC1409 ses-M00 MCI 75.3 M -sub-CLNC1409 ses-M03 MCI 75.3 M -sub-CLNC1409 ses-M06 MCI 75.3 M -sub-CLNC1409 ses-M12 MCI 75.3 M -sub-CLNC1409 ses-M24 MCI 75.3 M -sub-CLNC1409 ses-M36 MCI 75.3 M -sub-CLNC1412 ses-M00 MCI 79.01 F -sub-CLNC1412 ses-M03 MCI 79.01 F -sub-CLNC1412 ses-M06 MCI 79.01 F -sub-CLNC1412 ses-M12 MCI 79.01 F -sub-CLNC1412 ses-M24 MCI 79.01 F -sub-CLNC1412 ses-M36 MCI 79.01 F -sub-CLNC1412 ses-M60 MCI 79.01 F -sub-CLNC1415 ses-M00 MCI 72.55 F -sub-CLNC1415 ses-M03 MCI 72.55 F -sub-CLNC1415 ses-M06 MCI 72.55 F -sub-CLNC1415 ses-M12 MCI 72.55 F -sub-CLNC1415 ses-M24 MCI 72.55 F -sub-CLNC1416 ses-M00 MCI 80.6 M -sub-CLNC1416 ses-M06 MCI 80.6 M -sub-CLNC1417 ses-M00 MCI 65.76 M -sub-CLNC1417 ses-M03 MCI 65.76 M -sub-CLNC1417 ses-M06 MCI 65.76 M -sub-CLNC1417 ses-M12 MCI 65.76 M -sub-CLNC1418 ses-M00 MCI 78.25 M -sub-CLNC1418 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F -sub-CLNC1580 ses-M36 MCI 73.56 F -sub-CLNC1580 ses-M48 MCI 73.56 F -sub-CLNC1580 ses-M60 MCI 73.56 F -sub-CLNC1580 ses-M72 MCI 73.56 F -sub-CLNC1580 ses-M84 MCI 73.56 F -sub-CLNC1582 ses-M00 MCI 68.86 M -sub-CLNC1583 ses-M00 MCI 73.22 M -sub-CLNC1583 ses-M03 MCI 73.22 M -sub-CLNC1583 ses-M06 MCI 73.22 M -sub-CLNC1583 ses-M12 MCI 73.22 M -sub-CLNC1583 ses-M24 MCI 73.22 M -sub-CLNC1583 ses-M36 MCI 73.22 M -sub-CLNC1584 ses-M00 MCI 71.19 F -sub-CLNC1584 ses-M06 MCI 71.19 F -sub-CLNC1584 ses-M12 MCI 71.19 F -sub-CLNC1584 ses-M18 MCI 71.19 F -sub-CLNC1586 ses-M00 MCI 71.87 M -sub-CLNC1586 ses-M03 MCI 71.87 M -sub-CLNC1586 ses-M06 MCI 71.87 M -sub-CLNC1586 ses-M12 MCI 71.87 M -sub-CLNC1587 ses-M00 MCI 81.22 F -sub-CLNC1588 ses-M00 MCI 75.8 F -sub-CLNC1588 ses-M06 MCI 75.8 F -sub-CLNC1588 ses-M12 MCI 75.8 F -sub-CLNC1588 ses-M18 MCI 75.8 F -sub-CLNC1590 ses-M00 MCI 76.63 M -sub-CLNC1590 ses-M03 MCI 76.63 M -sub-CLNC1590 ses-M06 MCI 76.63 M -sub-CLNC1590 ses-M12 MCI 76.63 M -sub-CLNC1590 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-sub-CLNC1608 ses-M18 MCI 75.44 F -sub-CLNC1609 ses-M00 MCI 65.65 F -sub-CLNC1609 ses-M06 MCI 65.65 F -sub-CLNC1609 ses-M12 MCI 65.65 F -sub-CLNC1609 ses-M18 MCI 65.65 F -sub-CLNC1611 ses-M00 MCI 68.62 M -sub-CLNC1611 ses-M06 MCI 68.62 M -sub-CLNC1611 ses-M12 MCI 68.62 M -sub-CLNC1611 ses-M18 MCI 68.62 M -sub-CLNC1611 ses-M24 MCI 68.62 M -sub-CLNC1611 ses-M36 MCI 68.62 M -sub-CLNC1611 ses-M60 MCI 68.62 M -sub-CLNC1611 ses-M72 MCI 68.62 M -sub-CLNC1612 ses-M00 MCI 74.06 M -sub-CLNC1612 ses-M06 MCI 74.06 M -sub-CLNC1612 ses-M12 MCI 74.06 M -sub-CLNC1612 ses-M18 MCI 74.06 M -sub-CLNC1613 ses-M00 MCI 69.06 M -sub-CLNC1613 ses-M06 MCI 69.06 M -sub-CLNC1614 ses-M00 MCI 74.32 M -sub-CLNC1614 ses-M03 MCI 74.32 M -sub-CLNC1614 ses-M06 MCI 74.32 M -sub-CLNC1614 ses-M12 MCI 74.32 M -sub-CLNC1615 ses-M00 MCI 71.33 F -sub-CLNC1615 ses-M03 MCI 71.33 F -sub-CLNC1615 ses-M06 MCI 71.33 F -sub-CLNC1615 ses-M12 MCI 71.33 F -sub-CLNC1616 ses-M00 MCI 79.0 F -sub-CLNC1616 ses-M12 MCI 79.0 F -sub-CLNC1616 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M -sub-CLNC1626 ses-M03 MCI 64.35 M -sub-CLNC1626 ses-M06 MCI 64.35 M -sub-CLNC1626 ses-M12 MCI 64.35 M -sub-CLNC1626 ses-M24 MCI 64.35 M -sub-CLNC1626 ses-M48 MCI 64.35 M -sub-CLNC1628 ses-M00 MCI 85.59 F -sub-CLNC1628 ses-M06 MCI 85.59 F -sub-CLNC1628 ses-M108 MCI 85.59 F -sub-CLNC1628 ses-M12 MCI 85.59 F -sub-CLNC1628 ses-M18 MCI 85.59 F -sub-CLNC1628 ses-M24 MCI 85.59 F -sub-CLNC1628 ses-M36 MCI 85.59 F -sub-CLNC1628 ses-M48 MCI 85.59 F -sub-CLNC1628 ses-M60 MCI 85.59 F -sub-CLNC1628 ses-M72 MCI 85.59 F -sub-CLNC1628 ses-M84 MCI 85.59 F -sub-CLNC1630 ses-M00 MCI 64.65 F -sub-CLNC1630 ses-M03 MCI 64.65 F -sub-CLNC1630 ses-M06 MCI 64.65 F -sub-CLNC1630 ses-M12 MCI 64.65 F -sub-CLNC1631 ses-M00 MCI 74.45 M -sub-CLNC1631 ses-M03 MCI 74.45 M -sub-CLNC1631 ses-M06 MCI 74.45 M -sub-CLNC1631 ses-M12 MCI 74.45 M -sub-CLNC1631 ses-M24 MCI 74.45 M -sub-CLNC1631 ses-M48 MCI 74.45 M -sub-CLNC1635 ses-M00 MCI 68.67 F -sub-CLNC1635 ses-M03 MCI 68.67 F -sub-CLNC1635 ses-M12 MCI 68.67 F 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82.34 M -sub-CLNC1731 ses-M00 MCI 75.5 F -sub-CLNC1731 ses-M03 MCI 75.5 F -sub-CLNC1731 ses-M06 MCI 75.5 F -sub-CLNC1731 ses-M12 MCI 75.5 F -sub-CLNC1731 ses-M24 MCI 75.5 F diff --git a/tests/data/tsvtool/anonymous_reference/getlabels.json b/tests/data/tsvtool/anonymous_reference/getlabels.json deleted file mode 100644 index a949c1663..000000000 --- a/tests/data/tsvtool/anonymous_reference/getlabels.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "merged_tsv": "data/tsvtool/anonymous_BIDS.tsv", - "missing_mods": "data/tsvtool/anonymous_missing_mods", - "diagnoses": [ - "AD", - "CN", - "MCI", - "pMCI", - "sMCI" - ], - "modality": "t1w", - "restriction_tsv": null, - "time_horizon": 36, - "variables_of_interest": [ - "diagnosis", - "age", - "sex" - ], - "remove_smc": true -} \ No newline at end of file diff --git a/tests/data/tsvtool/anonymous_reference/pMCI.tsv b/tests/data/tsvtool/anonymous_reference/pMCI.tsv deleted file mode 100644 index 8f2c1d0b3..000000000 --- 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100.0, - "subset_name": "test", - "MCI_sub_categories": true, - "p_age_threshold": 0.8, - "p_sex_threshold": 0.8, - "categorical_split_variable": null, - "ignore_demographics": false -} \ No newline at end of file diff --git a/tests/test_cli.py b/tests/test_cli.py index 05e840b52..d2fb32692 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -5,19 +5,19 @@ from clinicadl.cmdline import cli -# Test to ensure that the help string at the command line is invoked without errors +# Test to ensure that the help string, at the command line, is invoked without errors # Test for the first level at the command line @pytest.fixture( params=[ - "extract", + "prepare-data", "generate", "interpret", "predict", "quality-check", "random-search", "train", - "tsvtool", + "tsvtools", ] ) def cli_args_first_lv(request): @@ -33,7 +33,7 @@ def test_first_lv(cli_args_first_lv): assert result.exit_code == 0 -# Test for extract cli, second level +# Test for prepare-data cli, second level @pytest.fixture( params=[ "image", @@ -42,7 +42,7 @@ def test_first_lv(cli_args_first_lv): "roi", ] ) -def extract_cli_arg1(request): +def prepare_data_cli_arg1(request): return request.param @@ -53,16 +53,16 @@ def extract_cli_arg1(request): "custom", ] ) -def extract_cli_arg2(request): +def prepare_data_cli_arg2(request): return request.param -def test_second_lv_extract(extract_cli_arg1, extract_cli_arg2): +def test_second_lv_prepare_data(prepare_data_cli_arg1, prepare_data_cli_arg2): runner = CliRunner() - arg1 = extract_cli_arg1 - arg2 = extract_cli_arg2 - print(f"Testing input extract cli {arg1} {arg2}") - result = runner.invoke(cli, f"extract {arg1} {arg2} -h") + arg1 = prepare_data_cli_arg1 + arg2 = prepare_data_cli_arg2 + print(f"Testing input prepare_data cli {arg1} {arg2}") + result = runner.invoke(cli, f"prepare-data {arg1} {arg2} -h") assert result.exit_code == 0 @@ -179,10 +179,12 @@ def test_second_lv_quality_check(qc_cli_arg1): @pytest.fixture( params=[ "analysis", - "getlabels", + "get-labels", "kfold", - "restrict", "split", + "prepare-experiment", + "get-progression", + "get-metadata", ] ) def tsvtool_cli_arg1(request): @@ -192,6 +194,6 @@ def tsvtool_cli_arg1(request): def test_second_lv_tsvtool(tsvtool_cli_arg1): runner = CliRunner() arg1 = tsvtool_cli_arg1 - print(f"Testing input tsvtool cli {arg1}") - result = runner.invoke(cli, f"tsvtool {arg1} -h") + print(f"Testing input tsvtools cli {arg1}") + result = runner.invoke(cli, f"tsvtools {arg1} -h") assert result.exit_code == 0 diff --git a/tests/test_extract.py b/tests/test_extract.py deleted file mode 100644 index e945c1a7c..000000000 --- a/tests/test_extract.py +++ /dev/null @@ -1,201 +0,0 @@ -# coding: utf8 - -import warnings -from typing import Any, Dict, List - -warnings.filterwarnings("ignore") - - -def clean_folder(path, recreate=True): - from os import makedirs - from os.path import abspath, exists - from shutil import rmtree - - abs_path = abspath(path) - if exists(abs_path): - rmtree(abs_path) - if recreate: - makedirs(abs_path) - - -def compare_folders(out, ref, shared_folder_name): - from difflib import unified_diff - from filecmp import cmp - from os import remove - from os.path import join - - out_txt = join(out, "out_folder.txt") - ref_txt = join(ref, "ref_folder.txt") - - list_files(join(out, shared_folder_name), filename=out_txt) - list_files(join(ref, shared_folder_name), filename=ref_txt) - - # Compare them - if not cmp(out_txt, ref_txt): - with open(out_txt, "r") as fin: - out_message = fin.read() - with open(ref_txt, "r") as fin: - ref_message = fin.read() - with open(out_txt, "r") as out: - with open(ref_txt, "r") as ref: - diff = unified_diff( - out.readlines(), - ref.readlines(), - fromfile="output", - tofile="reference", - ) - diff_text = "" - for line in diff: - diff_text = diff_text + line + "\n" - remove(out_txt) - remove(ref_txt) - raise AssertionError( - "Comparison of out and ref directories shows mismatch :\n " - "OUT :\n" - + out_message - + "\n REF :\n" - + ref_message - + "\nDiff :\n" - + diff_text - ) - - # Clean folders - remove(out_txt) - remove(ref_txt) - - -def list_files(startpath, filename=None): - """ - Args: - startpath: starting point for the tree listing. Does not list hidden - files (to avoid problems with .DS_store for example - filename: if None, display to stdout, otherwise write in the file - Returns: - void - """ - from os import remove, sep, walk - from os.path import abspath, basename, exists, expanduser, expandvars - - if exists(filename): - remove(filename) - - expanded_path = abspath(expanduser(expandvars(startpath))) - for root, dirs, files in walk(expanded_path): - level = root.replace(startpath, "").count(sep) - indent = " " * 4 * (level) - rootstring = "{}{}/".format(indent, basename(root)) - # Do not deal with hidden files - if not basename(root).startswith("."): - if filename is not None: - # 'a' stands for 'append' rather than 'w' for 'write'. We must - # manually jump line with \n otherwise everything is - # concatenated - with open(filename, "a") as fin: - fin.write(rootstring + "\n") - else: - print(rootstring) - subindent = " " * 4 * (level + 1) - for f in files: - filestring = "{}{}".format(subindent, f) - if not basename(f).startswith("."): - if filename is not None: - with open(filename, "a") as fin: - fin.write(filestring + "\n") - else: - print(filestring) - - -def test_extract(): - import shutil - from os.path import abspath, dirname, join - - root = dirname(abspath(join(abspath(__file__)))) - root = join(root, "data", "dataset", "DeepLearningPrepareData") - - # Remove potential residual of previous UT - clean_folder(join(root, "out", "caps"), recreate=False) - - # Copy necessary data from in to out - shutil.copytree(join(root, "in", "caps"), join(root, "out", "caps")) - - # Prepare test for different parameters - - modalities = ["t1-linear", "pet-linear", "custom"] - - uncropped_image = [True, False] - - image_params = {"mode": "image"} - patch_params = {"mode": "patch", "patch_size": 50, "stride_size": 50} - slice_params = { - "mode": "slice", - "slice_mode": "rgb", - "slice_direction": 0, - "discarded_slices": [0, 0], - } - roi_params = { - "mode": "roi", - "roi_list": ["rightHippocampusBox", "leftHippocampusBox"], - "uncropped_roi": True, - "roi_custom_template": "", - "roi_custom_mask_pattern": "", - } - - data: List[Dict[str, Any]] = [image_params, slice_params, patch_params, roi_params] - - for parameters in data: - - parameters["prepare_dl"] = True - - for modality in modalities: - - parameters["preprocessing"] = modality - - if modality == "pet-linear": - parameters["acq_label"] = "av45" - parameters["suvr_reference_region"] = "pons2" - parameters["use_uncropped_image"] = False - parameters["extract_json"] = f"{modality}_mode-{parameters['mode']}" - extract_generic(root, parameters) - - elif modality == "custom": - parameters["use_uncropped_image"] = True - parameters[ - "custom_suffix" - ] = "graymatter_space-Ixi549Space_modulated-off_probability.nii.gz" - parameters["roi_custom_template"] = "Ixi549Space" - parameters["extract_json"] = f"{modality}_mode-{parameters['mode']}" - extract_generic(root, parameters) - - elif modality == "t1-linear": - for flag in uncropped_image: - parameters["use_uncropped_image"] = flag - parameters[ - "extract_json" - ] = f"{modality}_crop-{not flag}_mode-{parameters['mode']}" - extract_generic(root, parameters) - else: - raise NotImplementedError( - f"Test for modality {modality} was not implemented." - ) - - # Check output vs ref - out_folder = join(root, "out") - ref_folder = join(root, "ref") - - compare_folders(out_folder, ref_folder, shared_folder_name="caps/subjects") - - clean_folder(join(root, "out", "caps"), recreate=False) - - -def extract_generic(root, parameters): - - from os.path import join - - from clinicadl.extract.extract import DeepLearningPrepareData - - DeepLearningPrepareData( - caps_directory=join(root, "out", "caps"), - tsv_file=join(root, "in", "subjects.tsv"), - n_proc=2, - parameters=parameters, - ) diff --git a/tests/test_generate.py b/tests/test_generate.py index ac6dd2121..f82732492 100644 --- a/tests/test_generate.py +++ b/tests/test_generate.py @@ -1,50 +1,76 @@ # coding: utf8 import os -from os.path import abspath -from typing import List +from pathlib import Path import pytest +from tests.testing_tools import clean_folder, compare_folders -@pytest.fixture(params=["generate_trivial", "generate_random", "generate_shepplogan"]) -def generate_commands(request): - if request.param == "generate_trivial": - data_caps_folder = "data/dataset/OasisCaps_example/" - output_folder = "data/dataset/trivial_example" + +@pytest.fixture( + params=[ + "random_example", + "trivial_example", + "shepplogan_example", + "hypometabolic_example", + "trivial_motion_example", + ] +) +def test_name(request): + return request.param + + +def test_generate(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "generate" / "in" + ref_dir = base_dir / "generate" / "ref" + tmp_out_dir = tmp_path / "generate" / "out" + tmp_out_dir.mkdir(parents=True) + + clean_folder(tmp_out_dir, recreate=True) + + data_caps_pet = str(input_dir / "caps_pet") + data_caps_folder = str(input_dir / "caps") + + if test_name == "trivial_example": + output_folder = tmp_out_dir / test_name test_input = [ "generate", "trivial", data_caps_folder, - output_folder, + str(output_folder), "--n_subjects", "4", "--preprocessing", "t1-linear", ] - output_reference = [ - "data.tsv", - "missing_mods_ses-M00.tsv", - "sub-TRIV0_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV1_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV2_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV3_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV4_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV5_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV6_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-TRIV7_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", + elif test_name == "hypometabolic_example": + output_folder = str(tmp_out_dir / test_name) + test_input = [ + "generate", + "hypometabolic", + data_caps_pet, + output_folder, + "--n_subjects", + "2", + "--pathology", + "ad", + "--anomaly_degree", + "50", + "--sigma", + "5", ] - elif request.param == "generate_random": - data_caps_folder = "data/dataset/OasisCaps_example/" - output_folder = "data/dataset/random_example" + elif test_name == "random_example": + output_folder = tmp_out_dir / test_name test_input = [ "generate", "random", data_caps_folder, - output_folder, + str(output_folder), "--n_subjects", - "10", + "4", "--mean", "4000", "--sigma", @@ -52,84 +78,39 @@ def generate_commands(request): "--preprocessing", "t1-linear", ] - output_reference = [ - "data.tsv", - "missing_mods_ses-M00.tsv", - "sub-RAND0_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND1_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND10_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND11_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND12_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND13_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND14_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND15_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND16_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND17_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND18_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND19_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND2_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND3_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND4_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND5_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND6_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND7_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND8_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - "sub-RAND9_ses-M00_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz", - ] - elif request.param == "generate_shepplogan": + elif test_name == "shepplogan_example": n_subjects = 10 - output_folder = "data/dataset/shepplogan_example" + output_folder = tmp_out_dir / test_name test_input = [ "generate", "shepplogan", - output_folder, + str(output_folder), "--n_subjects", f"{n_subjects}", ] - output_reference = ( - ["data.tsv", "missing_mods_ses-M00.tsv"] - + [ - f"sub-CLNC{i}{j:04d}_ses-M00_space-SheppLogan_axis-axi_channel-single_slice-0_phantom.pt" - for i in range(2) - for j in range(n_subjects) - ] - + [ - f"sub-CLNC{i}{j:04d}_ses-M00_space-SheppLogan_phantom.nii.gz" - for i in range(2) - for j in range(n_subjects) - ] - ) - else: - raise NotImplementedError(f"Test {request.param} is not implemented.") - - return test_input, output_folder, output_reference - - -def test_generate(generate_commands): + elif test_name == "trivial_motion_example": + output_folder = tmp_out_dir / test_name + test_input = [ + "generate", + "trivial_motion", + data_caps_folder, + str(output_folder), + "--preprocessing", + "t1-linear", + ] - test_input, output_folder, output_ref = generate_commands + else: + raise NotImplementedError(f"Test {test_name} is not implemented.") flag_error = not os.system("clinicadl " + " ".join(test_input)) assert flag_error - assert compare_folder_with_files(abspath(output_folder), output_ref) - - -def compare_folder_with_files(folder: str, file_list: List[str]) -> bool: - """Compare file existing in two folders - - Args: - folder: path to a folder - file_list: list of files which must be found in folder - - Returns: - True if files in file_list were all found in folder. - """ - folder_list = [] - for root, dirs, files in os.walk(folder): - folder_list.extend(files) + if test_name == "shepplogan_example": + file = list((output_folder / "tensor_extraction").iterdir()) + old_name = output_folder / "tensor_extraction" / file[0] + new_name = output_folder / "tensor_extraction" / "extract_test.json" + old_name.rename(new_name) - print(f"Missing files {set(file_list) - set(folder_list)}") - return set(file_list).issubset(set(folder_list)) + assert compare_folders(output_folder, ref_dir / test_name, tmp_out_dir) diff --git a/tests/test_interpret.py b/tests/test_interpret.py index c2747dd90..8030e4c98 100644 --- a/tests/test_interpret.py +++ b/tests/test_interpret.py @@ -1,24 +1,38 @@ # coding: utf8 +import json import os import shutil +from pathlib import Path import pytest from clinicadl import MapsManager +from tests.testing_tools import clean_folder, compare_folders @pytest.fixture(params=["classification", "regression"]) -def cli_commands(request): +def test_name(request): + return request.param - if request.param == "classification": + +def test_interpret(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "interpret" / "in" + ref_dir = base_dir / "interpret" / "ref" + tmp_out_dir = tmp_path / "interpret" / "out" + tmp_out_dir.mkdir(parents=True) + + labels_dir_str = str(input_dir / "labels_list" / "2_fold") + maps_tmp_out_dir = str(tmp_out_dir / "maps") + if test_name == "classification": cnn_input = [ "train", "classification", - "data/dataset/random_example", - "extract_image.json", - "data/labels_list", - "results", + str(input_dir / "caps_image"), + "t1-linear_mode-image.json", + labels_dir_str, + maps_tmp_out_dir, "--architecture Conv5_FC3", "--epochs", "1", @@ -28,14 +42,14 @@ def cli_commands(request): "0", ] - elif request.param == "regression": + elif test_name == "regression": cnn_input = [ "train", "regression", - "data/dataset/random_example", - "extract_patch.json", - "data/labels_list", - "results", + str(input_dir / "caps_patch"), + "t1-linear_mode-patch.json", + labels_dir_str, + maps_tmp_out_dir, "--architecture Conv5_FC3", "--epochs", "1", @@ -45,22 +59,21 @@ def cli_commands(request): "0", ] else: - raise NotImplementedError(f"Test {request.param} is not implemented.") + raise NotImplementedError(f"Test {test_name} is not implemented.") - return cnn_input + run_interpret(cnn_input, tmp_out_dir, ref_dir) -def test_interpret(cli_commands): +def run_interpret(cnn_input, tmp_out_dir, ref_dir): from clinicadl.interpret.gradients import method_dict - cnn_input = cli_commands - if os.path.exists("results"): - shutil.rmtree("results") + maps_path = tmp_out_dir / "maps" + if maps_path.is_dir(): + shutil.rmtree(maps_path) train_error = not os.system("clinicadl " + " ".join(cnn_input)) assert train_error - maps_manager = MapsManager("results", verbose="debug") + maps_manager = MapsManager(maps_path, verbose="debug") for method in method_dict.keys(): maps_manager.interpret("train", f"test-{method}", method) interpret_map = maps_manager.get_interpretation("train", f"test-{method}") - shutil.rmtree("results") diff --git a/tests/test_meta_maps.py b/tests/test_meta_maps.py deleted file mode 100644 index e07cca295..000000000 --- a/tests/test_meta_maps.py +++ /dev/null @@ -1,30 +0,0 @@ -# coding: utf8 - -import os - -import pytest - - -@pytest.fixture(params=["get_loss"]) -def cli_commands(request): - - if request.param == "get_loss": - analysis_input = ["maps-analysis", "data/maps_analysis", "-metric BA"] - else: - raise NotImplementedError(f"Test {request.param} is not implemented.") - - return analysis_input - - -def test_interpret(cli_commands): - analysis_input = cli_commands - - analysis_error = not os.system("clinicadl " + " ".join(analysis_input)) - output_path = os.path.join( - "data", "maps_analysis", "analysis_metric-BA_selection-loss.tsv" - ) - analysis_flag = os.path.exists(output_path) - assert analysis_error - assert analysis_flag - - os.remove(output_path) diff --git a/tests/test_predict.py b/tests/test_predict.py index b8b2dafc3..34427eeeb 100644 --- a/tests/test_predict.py +++ b/tests/test_predict.py @@ -2,11 +2,13 @@ import json import os import shutil -from os.path import exists, join +from os.path import exists +from pathlib import Path import pytest from clinicadl import MapsManager +from tests.testing_tools import clean_folder, compare_folders @pytest.fixture( @@ -15,51 +17,56 @@ "predict_roi_regression", "predict_slice_classification", "predict_patch_regression", - "predict_roi_multi_classification", + "predict_patch_multi_classification", "predict_roi_reconstruction", ] ) -def predict_commands(request): - if request.param == "predict_image_classification": - model_folder = "data/models/maps_image/" +def test_name(request): + return request.param + + +def test_predict(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "predict" / "in" + ref_dir = base_dir / "predict" / "ref" + tmp_out_dir = tmp_path / "predict" / "out" + tmp_out_dir.mkdir(parents=True) + + if test_name == "predict_image_classification": + model_folder = input_dir / "maps_image_cnn" modes = ["image"] use_labels = True - elif request.param == "predict_slice_classification": - model_folder = "data/models/maps_slice/" + elif test_name == "predict_slice_classification": + model_folder = input_dir / "maps_slice_cnn" modes = ["image", "slice"] use_labels = True - elif request.param == "predict_patch_regression": - model_folder = "data/models/maps_patch/" + elif test_name == "predict_patch_regression": + model_folder = input_dir / "maps_patch_cnn" modes = ["image", "patch"] use_labels = False - elif request.param == "predict_roi_regression": - model_folder = "data/models/maps_roi/" + elif test_name == "predict_roi_regression": + model_folder = input_dir / "maps_roi_cnn" modes = ["image", "roi"] use_labels = False - elif request.param == "predict_roi_multi_classification": - model_folder = "data/models/maps_roi_multi/" - modes = ["image", "roi"] + elif test_name == "predict_patch_multi_classification": + model_folder = input_dir / "maps_patch_multi_cnn" + modes = ["image", "patch"] use_labels = False - elif request.param == "predict_roi_reconstruction": - model_folder = "data/models/maps_roi_ae/" + elif test_name == "predict_roi_reconstruction": + model_folder = input_dir / "maps_roi_ae" modes = ["roi"] use_labels = False else: - raise NotImplementedError(f"Test {request.param} is not implemented.") + raise NotImplementedError(f"Test {test_name} is not implemented.") - return model_folder, use_labels, modes - - -def test_predict(predict_commands): - model_folder, use_labels, modes = predict_commands - out_dir = join(model_folder, "split-0/best-loss/test-RANDOM") + out_dir = str(model_folder / "split-0/best-loss/test-RANDOM") if exists(out_dir): shutil.rmtree(out_dir) # Correction of JSON file for ROI if "roi" in modes: - json_path = join(model_folder, "maps.json") + json_path = model_folder / "maps.json" with open(json_path, "r") as f: parameters = json.load(f) parameters["roi_list"] = ["leftHippocampusBox", "rightHippocampusBox"] @@ -70,14 +77,21 @@ def test_predict(predict_commands): maps_manager = MapsManager(model_folder, verbose="debug") maps_manager.predict( data_group="test-RANDOM", - caps_directory="data/dataset/OasisCaps_example", - tsv_path="data/dataset/OasisCaps_example/data.tsv", + caps_directory=input_dir / "caps_random", + tsv_path=input_dir / "caps_random/data.tsv", gpu=False, use_labels=use_labels, overwrite=True, + diagnoses=["CN"], ) for mode in modes: maps_manager.get_prediction(data_group="test-RANDOM", mode=mode) if use_labels: maps_manager.get_metrics(data_group="test-RANDOM", mode=mode) + + assert compare_folders( + tmp_out_dir / test_name, + ref_dir / test_name, + tmp_out_dir, + ) diff --git a/tests/test_prepare_data.py b/tests/test_prepare_data.py new file mode 100644 index 000000000..b161660dc --- /dev/null +++ b/tests/test_prepare_data.py @@ -0,0 +1,161 @@ +# coding: utf8 + +import os +import shutil +import warnings +from os import PathLike +from os.path import join +from pathlib import Path +from typing import Any, Dict, List + +import pytest + +from tests.testing_tools import clean_folder, compare_folders + +warnings.filterwarnings("ignore") + + +@pytest.fixture( + params=[ + "slice", + "patch", + "image", + "roi", + ] +) +def test_name(request): + return request.param + + +def test_prepare_data(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "prepare_data" / "in" + ref_dir = base_dir / "prepare_data" / "ref" + tmp_out_dir = tmp_path / "prepare_data" / "out" + tmp_out_dir.mkdir(parents=True) + + clean_folder(tmp_out_dir, recreate=True) + + input_caps_directory = input_dir / "caps" + input_caps_flair_directory = input_dir / "caps_flair" + if test_name == "image": + if (tmp_out_dir / "caps_image").is_dir(): + shutil.rmtree(tmp_out_dir / "caps_image") + shutil.rmtree(tmp_out_dir / "caps_image_flair") + shutil.copytree(input_caps_flair_directory, tmp_out_dir / "caps_image_flair") + shutil.copytree(input_caps_directory, tmp_out_dir / "caps_image") + parameters = {"mode": "image"} + + elif test_name == "patch": + if (tmp_out_dir / "caps_patch").is_dir(): + shutil.rmtree(tmp_out_dir / "caps_patch") + shutil.rmtree(tmp_out_dir / "caps_patch_flair") + shutil.copytree(input_caps_flair_directory, tmp_out_dir / "caps_patch_flair") + shutil.copytree(input_caps_directory, tmp_out_dir / "caps_patch") + parameters = {"mode": "patch", "patch_size": 50, "stride_size": 50} + + elif test_name == "slice": + if (tmp_out_dir / "caps_slice").is_dir(): + shutil.rmtree(tmp_out_dir / "caps_slice") + shutil.rmtree(tmp_out_dir / "caps_slice_flair") + shutil.copytree(input_caps_flair_directory, tmp_out_dir / "caps_slice_flair") + shutil.copytree(input_caps_directory, tmp_out_dir / "caps_slice") + parameters = { + "mode": "slice", + "slice_mode": "rgb", + "slice_direction": 0, + "discarded_slices": [0, 0], + } + + elif test_name == "roi": + if (tmp_out_dir / "caps_roi").is_dir(): + shutil.rmtree(tmp_out_dir / "caps_roi") + shutil.rmtree(tmp_out_dir / "caps_roi_flair") + shutil.copytree(input_caps_flair_directory, tmp_out_dir / "caps_roi_flair") + shutil.copytree(input_caps_directory, tmp_out_dir / "caps_roi") + parameters = { + "mode": "roi", + "roi_list": ["rightHippocampusBox", "leftHippocampusBox"], + "uncropped_roi": False, + "roi_custom_template": "", + "roi_custom_mask_pattern": "", + } + else: + print(f"Test {test_name} not available.") + assert 0 + + run_test_prepare_data(input_dir, ref_dir, tmp_out_dir, parameters) + + +def run_test_prepare_data(input_dir, ref_dir, out_dir, parameters): + modalities = ["t1-linear", "pet-linear", "flair-linear"] + uncropped_image = [True, False] + acquisition_label = ["18FAV45", "11CPIB"] + parameters["prepare_dl"] = True + + for modality in modalities: + parameters["preprocessing"] = modality + if modality == "pet-linear": + parameters["save_features"] = True + for acq in acquisition_label: + parameters["tracer"] = acq + parameters["suvr_reference_region"] = "pons2" + parameters["use_uncropped_image"] = False + parameters["extract_json"] = ( + f"{modality}-{acq}_mode-{parameters['mode']}.json" + ) + tsv_file = join(input_dir, f"pet_{acq}.tsv") + mode = parameters["mode"] + extract_generic(out_dir, mode, tsv_file, parameters) + + elif modality == "custom": + parameters["save_features"] = True + parameters["use_uncropped_image"] = True + parameters["custom_suffix"] = ( + "graymatter_space-Ixi549Space_modulated-off_probability.nii.gz" + ) + parameters["roi_custom_template"] = "Ixi549Space" + parameters["extract_json"] = f"{modality}_mode-{parameters['mode']}.json" + tsv_file = input_dir / "subjects.tsv" + mode = parameters["mode"] + extract_generic(out_dir, mode, tsv_file, parameters) + + elif modality == "t1-linear": + parameters["save_features"] = True + for flag in uncropped_image: + parameters["use_uncropped_image"] = flag + parameters["extract_json"] = ( + f"{modality}_crop-{not flag}_mode-{parameters['mode']}.json" + ) + + # tsv_file = input_dir / "subjects.tsv" + mode = parameters["mode"] + extract_generic(out_dir, mode, None, parameters) + + elif modality == "flair-linear": + parameters["save_features"] = False + parameters["prepare_dl"] = False + for flag in uncropped_image: + parameters["use_uncropped_image"] = flag + parameters["extract_json"] = ( + f"{modality}_crop-{not flag}_mode-{parameters['mode']}.json" + ) + + mode = f"{parameters['mode']}_flair" + extract_generic(out_dir, mode, None, parameters) + else: + raise NotImplementedError( + f"Test for modality {modality} was not implemented." + ) + assert compare_folders(out_dir / f"caps_{mode}", ref_dir / f"caps_{mode}", out_dir) + + +def extract_generic(out_dir, mode, tsv_file, parameters): + from clinicadl.prepare_data.prepare_data import DeepLearningPrepareData + + DeepLearningPrepareData( + caps_directory=out_dir / f"caps_{mode}", + tsv_file=tsv_file, + n_proc=1, + parameters=parameters, + ) diff --git a/tests/test_qc.py b/tests/test_qc.py index f7ca97583..910c357d4 100644 --- a/tests/test_qc.py +++ b/tests/test_qc.py @@ -1,34 +1,92 @@ import shutil from os import system +from os.path import join +from pathlib import Path +import pandas as pd import pytest +from tests.testing_tools import compare_folders -@pytest.fixture(params=["t1-linear", "t1-volume"]) -def cli_commands(request): - if request.param == "t1-linear": + +@pytest.fixture(params=["t1-linear", "t1-volume", "pet-linear"]) +def test_name(request): + return request.param + + +def test_qc(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "qualityCheck" / "in" + ref_dir = base_dir / "qualityCheck" / "ref" + tmp_out_dir = tmp_path / "qualityCheck" / "out" + tmp_out_dir.mkdir(parents=True) + + if test_name == "t1-linear": + out_tsv = str(tmp_out_dir / "QC.tsv") test_input = [ "t1-linear", - "data/dataset/OasisCaps_example", - "results/quality_check.tsv", + str(input_dir / "caps"), + out_tsv, "--no-gpu", ] - elif request.param == "t1-volume": + + elif test_name == "t1-volume": + out_dir = str(tmp_out_dir / "QC_T1V") test_input = [ "t1-volume", - "data/dataset/OasisCaps_t1-volume", - "results", - "adni2021", + str(input_dir / "caps_T1V"), + out_dir, + "Ixi549Space", + ] + + elif test_name == "pet-linear": + out_tsv = str(tmp_out_dir / "QC_pet.tsv") + test_input = [ + "pet-linear", + str(input_dir / "caps_pet"), + out_tsv, + "18FFDG", + "cerebellumPons2", + "--threshold", + "0.5", ] else: raise NotImplementedError( - f"Quality check test on {request.param} is not implemented." + f"Quality check test on {test_name} is not implemented ." ) - return test_input + flag_error = not system(f"clinicadl quality-check " + " ".join(test_input)) + assert flag_error + if test_name == "t1-linear": + ref_tsv = join(ref_dir, "QC.tsv") + ref_df = pd.read_csv(ref_tsv, sep="\t") + ref_df.reset_index(inplace=True) -def test_qc(cli_commands): - flag_error = not system(f"clinicadl quality-check " + " ".join(cli_commands)) - assert flag_error - shutil.rmtree("results") + out_df = pd.read_csv(out_tsv, sep="\t") + out_df.reset_index(inplace=True) + + out_df["pass_probability"] = round(out_df["pass_probability"], 2) + ref_df["pass_probability"] = round(ref_df["pass_probability"], 2) + + system(f"diff {out_tsv} {ref_tsv} ") + assert out_df.equals(ref_df) + + elif test_name == "t1-volume": + assert compare_folders(out_dir, str(ref_dir / "QC_T1V"), tmp_out_dir) + + elif test_name == "pet-linear": + out_df = pd.read_csv(out_tsv, sep="\t") + ref_tsv = join(ref_dir, "QC_pet.tsv") + ref_df = pd.read_csv(ref_tsv, sep="\t") + out_df.reset_index(inplace=True) + ref_df.reset_index(inplace=True) + + out_df = pd.read_csv(out_tsv, sep="\t") + out_df.reset_index(inplace=True) + + out_df["pass_probability"] = round(out_df["pass_probability"], 2) + ref_df["pass_probability"] = round(ref_df["pass_probability"], 2) + + system(f"diff {out_tsv} {ref_tsv} ") + assert out_df.equals(ref_df) diff --git a/tests/test_random_search.py b/tests/test_random_search.py index 081984f71..e1c530513 100644 --- a/tests/test_random_search.py +++ b/tests/test_random_search.py @@ -3,43 +3,62 @@ import json import os import shutil +from os.path import join +from pathlib import Path import pytest -launch_dir = "results" -name_dir = "job-1" +from tests.testing_tools import compare_folders +# random searxh for ROI with CNN @pytest.fixture( params=[ - "rs_image_cnn", + "rs_roi_cnn", ] ) -def cli_commands(request): +def test_name(request): + return request.param - if request.param == "rs_image_cnn": - toml_path = "data/random_search.toml" - generate_input = ["random-search", launch_dir, name_dir] - else: - raise NotImplementedError(f"Test {request.param} is not implemented.") - return toml_path, generate_input +def test_random_search(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "randomSearch" / "in" + ref_dir = base_dir / "randomSearch" / "ref" + tmp_out_dir = tmp_path / "randomSearch" / "out" + tmp_out_dir.mkdir(parents=True) + + if test_name == "rs_roi_cnn": + toml_path = join(input_dir / "random_search.toml") + generate_input = ["random-search", str(tmp_out_dir), "job-1"] + else: + raise NotImplementedError(f"Test {test_name} is not implemented.") + run_test_random_search(toml_path, generate_input, tmp_out_dir, ref_dir) -def test_random_search(cli_commands): - toml_path, generate_input = cli_commands - if os.path.exists(launch_dir): - shutil.rmtree(launch_dir) +def run_test_random_search(toml_path, generate_input, tmp_out_dir, ref_dir): + if os.path.exists(tmp_out_dir): + shutil.rmtree(tmp_out_dir) # Write random_search.toml file - os.makedirs(launch_dir, exist_ok=True) - shutil.copy(toml_path, launch_dir) + os.makedirs(tmp_out_dir, exist_ok=True) + shutil.copy(toml_path, tmp_out_dir) flag_error_generate = not os.system("clinicadl " + " ".join(generate_input)) performances_flag = os.path.exists( - os.path.join(launch_dir, name_dir, "split-0", "best-loss", "train") + tmp_out_dir / "job-1" / "split-0" / "best-loss" / "train" ) assert flag_error_generate assert performances_flag - shutil.rmtree(launch_dir) + + assert compare_folders( + tmp_out_dir / "job-1" / "groups", + ref_dir / "job-1" / "groups", + tmp_out_dir, + ) + assert compare_folders( + tmp_out_dir / "job-1" / "split-0" / "best-loss", + ref_dir / "job-1" / "split-0" / "best-loss", + tmp_out_dir, + ) diff --git a/tests/test_resume.py b/tests/test_resume.py index 5d2be8d27..3cf883c32 100644 --- a/tests/test_resume.py +++ b/tests/test_resume.py @@ -1,35 +1,51 @@ # coding: utf8 +import json import os -import pathlib import shutil from os import system +from os.path import join +from pathlib import Path import pytest from clinicadl import MapsManager +from tests.testing_tools import compare_folders @pytest.fixture( params=[ - "data/stopped_jobs/stopped_1", - "data/stopped_jobs/stopped_2", - "data/stopped_jobs/stopped_3", - "data/stopped_jobs/stopped_4", + "stopped_1", + "stopped_2", + "stopped_3", ] ) -def input_directory(request): +def test_name(request): return request.param -def test_resume(input_directory): - flag_error = not system(f"clinicadl -vv train resume {input_directory}") +def test_resume(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "resume" / "in" + ref_dir = base_dir / "resume" / "ref" + tmp_out_dir = tmp_path / "resume" / "out" + tmp_out_dir.mkdir(parents=True) + + shutil.copytree(input_dir / test_name, tmp_out_dir / test_name) + maps_stopped = tmp_out_dir / test_name + + flag_error = not system(f"clinicadl -vv train resume {maps_stopped}") assert flag_error - maps_manager = MapsManager(input_directory) + maps_manager = MapsManager(maps_stopped) split_manager = maps_manager._init_split_manager() for split in split_manager.split_iterator(): - performances_flag = pathlib.Path( - input_directory, f"split-{split}", "best-loss", "train" + performances_flag = ( + maps_stopped / f"split-{split}" / "best-loss" / "train" ).exists() assert performances_flag - shutil.rmtree(input_directory) + + with open(maps_stopped / "maps.json", "r") as out: + json_data_out = json.load(out) + with open(ref_dir / "maps_image_cnn" / "maps.json", "r") as ref: + json_data_ref = json.load(ref) + assert json_data_ref == json_data_out diff --git a/tests/test_train_ae.py b/tests/test_train_ae.py index 15358c681..ab9c057ff 100644 --- a/tests/test_train_ae.py +++ b/tests/test_train_ae.py @@ -3,84 +3,113 @@ import json import os import shutil +from os.path import join +from pathlib import Path import pytest +from tests.testing_tools import clean_folder, compare_folders + @pytest.fixture( params=[ - "train_image_ae", - "train_patch_ae", - "train_roi_ae", - "train_slice_ae", + "image_ae", + "patch_multi_ae", + "roi_ae", + "slice_ae", ] ) -def cli_commands(request): - if request.param == "train_image_ae": - mode = "image" +def test_name(request): + return request.param + + +def test_train_ae(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "train" / "in" + ref_dir = base_dir / "train" / "ref" + tmp_out_dir = base_dir / "train" / "out" + # tmp_out_dir.mkdir(parents=True) + + clean_folder(tmp_out_dir, recreate=True) + + labels_path = str(input_dir / "labels_list" / "2_fold") + config_path = str(input_dir / "train_config.toml") + if test_name == "image_ae": + split = [0, 0] test_input = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_image.json", - "data/labels_list", - "results", + str(input_dir / "caps_image"), + "t1-linear_crop-True_mode-image.json", + labels_path, + str(tmp_out_dir), "-c", - "data/train_config.toml", + config_path, + "--split", + "1", ] - elif request.param == "train_patch_ae": - mode = "patch" + elif test_name == "patch_multi_ae": + split = [0, 0] test_input = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_patch.json", - "data/labels_list", - "results", + str(input_dir / "caps_patch"), + "t1-linear_crop-True_mode-patch.json", + labels_path, + str(tmp_out_dir), "-c", - "data/train_config.toml", + config_path, + "--multi_network", ] - elif request.param == "train_roi_ae": - mode = "roi" + elif test_name == "roi_ae": + split = [0, 0] test_input = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results", + str(input_dir / "caps_roi"), + "t1-linear_crop-True_mode-roi.json", + labels_path, + str(tmp_out_dir), "-c", - "data/train_config.toml", + config_path, ] - elif request.param == "train_slice_ae": - mode = "slice" + elif test_name == "slice_ae": + split = [0, 0] test_input = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_slice.json", - "data/labels_list", - "results", + str(input_dir / "caps_slice"), + "t1-linear_crop-True_mode-slice.json", + labels_path, + str(tmp_out_dir), "-c", - "data/train_config.toml", + config_path, ] else: - raise NotImplementedError(f"Test {request.param} is not implemented.") + raise NotImplementedError(f"Test {test_name} is not implemented.") - return test_input, mode + if tmp_out_dir.is_dir(): + shutil.rmtree(tmp_out_dir) - -def test_train(cli_commands): - if os.path.exists("results"): - shutil.rmtree("results") - - test_input, mode = cli_commands - if os.path.exists("results"): - shutil.rmtree("results") flag_error = not os.system("clinicadl " + " ".join(test_input)) assert flag_error - with open(os.path.join("results", "maps.json"), "r") as f: - json_data = json.load(f) - assert json_data["mode"] == mode - shutil.rmtree("results") + with open(tmp_out_dir / "maps.json", "r") as out: + json_data_out = json.load(out) + with open(ref_dir / ("maps_" + test_name) / "maps.json", "r") as ref: + json_data_ref = json.load(ref) + + if test_name == "patch_multi_ae": + json_data_out["multi_network"] = True + assert json_data_out == json_data_ref # ["mode"] == mode + + assert compare_folders( + tmp_out_dir / "groups", + ref_dir / ("maps_" + test_name) / "groups", + tmp_path, + ) + assert compare_folders( + tmp_out_dir / f"split-{split[0]}" / "best-loss", + ref_dir / ("maps_" + test_name) / f"split-{split[1]}" / "best-loss", + tmp_path, + ) diff --git a/tests/test_train_cnn.py b/tests/test_train_cnn.py index e0a66d3aa..da5b3a3f1 100644 --- a/tests/test_train_cnn.py +++ b/tests/test_train_cnn.py @@ -3,121 +3,131 @@ import json import os import shutil +from os.path import join +from pathlib import Path import pytest -output_dir = "results" +from tests.testing_tools import compare_folders @pytest.fixture( params=[ - "train_slice_cnn", - "train_image_cnn", - "train_patch_cnn", - "train_patch_multicnn", - "train_roi_cnn", - "train_roi_multicnn", + "slice_cnn", + "image_cnn", + "patch_cnn", + "patch_multi_cnn", + "roi_cnn", ] ) -def cli_commands(request): +def test_name(request): + return request.param + + +def test_train_cnn(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "train" / "in" + ref_dir = base_dir / "train" / "ref" + tmp_out_dir = tmp_path / "train" / "out" + tmp_out_dir.mkdir(parents=True) + + labels_path = input_dir / "labels_list" / "2_fold" + config_path = input_dir / "train_config.toml" split = "0" - if request.param == "train_slice_cnn": - mode = "slice" + + if test_name == "slice_cnn": + split_ref = 0 test_input = [ "train", "classification", - "data/dataset/random_example", - "extract_slice.json", - "data/labels_list", - output_dir, + str(input_dir / "caps_slice"), + "t1-linear_crop-True_mode-slice.json", + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), ] - elif request.param == "train_image_cnn": - mode = "image" - split = "1" + elif test_name == "image_cnn": + split_ref = 1 test_input = [ "train", "regression", - "data/dataset/random_example", - "extract_image.json", - "data/labels_list", - output_dir, + str(input_dir / "caps_image"), + "t1-linear_crop-True_mode-image.json", + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", - "--split", - split, + str(config_path), ] - elif request.param == "train_patch_cnn": - mode = "patch" - split = "1" + elif test_name == "patch_cnn": + split_ref = 0 test_input = [ "train", "classification", - "data/dataset/random_example", - "extract_patch.json", - "data/labels_list", - output_dir, + str(input_dir / "caps_patch"), + "t1-linear_crop-True_mode-patch.json", + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), "--split", split, ] - elif request.param == "train_patch_multicnn": - mode = "patch" + elif test_name == "patch_multi_cnn": + split_ref = 0 test_input = [ "train", "classification", - "data/dataset/random_example", - "extract_patch.json", - "data/labels_list", - output_dir, + str(input_dir / "caps_patch"), + "t1-linear_crop-True_mode-patch.json", + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), "--multi_network", ] - elif request.param == "train_roi_cnn": - mode = "roi" + elif test_name == "roi_cnn": + split_ref = 0 test_input = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - output_dir, + str(input_dir / "caps_roi"), + "t1-linear_crop-True_mode-roi.json", + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", - ] - elif request.param == "train_roi_multicnn": - mode = "roi" - test_input = [ - "train", - "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - output_dir, - "-c", - "data/train_config.toml", + str(config_path), ] else: - raise NotImplementedError(f"Test {request.param} is not implemented.") - - return test_input, split, mode + raise NotImplementedError(f"Test {test_name} is not implemented.") + if tmp_out_dir.is_dir(): + shutil.rmtree(tmp_out_dir) -def test_train(cli_commands): - test_input, split, mode = cli_commands - if os.path.exists(output_dir): - shutil.rmtree(output_dir) flag_error = not os.system("clinicadl " + " ".join(test_input)) assert flag_error - performances_flag = os.path.exists( - os.path.join("results", f"split-{split}", "best-loss", "train") - ) + + performances_flag = ( + tmp_out_dir / f"split-{split}" / "best-loss" / "train" + ).exists() assert performances_flag - with open(os.path.join("results", "maps.json"), "r") as f: - json_data = json.load(f) - assert json_data["mode"] == mode - shutil.rmtree(output_dir) + with open(tmp_out_dir / "maps.json", "r") as out: + json_data_out = json.load(out) + with open(ref_dir / ("maps_" + test_name) / "maps.json", "r") as ref: + json_data_ref = json.load(ref) + + assert json_data_out == json_data_ref # ["mode"] == mode + + assert compare_folders( + tmp_out_dir / "groups", + ref_dir / ("maps_" + test_name) / "groups", + tmp_path, + ) + assert compare_folders( + tmp_out_dir / "split-0" / "best-loss", + ref_dir / ("maps_" + test_name) / f"split-{split_ref}" / "best-loss", + tmp_path, + ) + + shutil.rmtree(tmp_out_dir) diff --git a/tests/test_train_from_json.py b/tests/test_train_from_json.py index 8e7fc2a51..f48791d31 100644 --- a/tests/test_train_from_json.py +++ b/tests/test_train_from_json.py @@ -1,56 +1,73 @@ +import os import pathlib import shutil -from os import system +from os import path, system +from os.path import join +from pathlib import Path from .testing_tools import compare_folders_with_hashes, create_hashes_dict, models_equal -def test_json_compatibility(): +def test_json_compatibility(cmdopt, tmp_path): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "train_from_json" / "in" + ref_dir = base_dir / "train_from_json" / "ref" + tmp_out_dir = tmp_path / "train_from_json" / "out" + tmp_out_dir.mkdir(parents=True) + split = "0" - config_json = pathlib.Path("data/reproducibility/maps.json") - output_dir = pathlib.Path("results") - if output_dir.exists(): - shutil.rmtree(output_dir) + config_json = input_dir / "maps_roi_cnn/maps.json" + reproduced_maps_dir = tmp_out_dir / "maps_reproduced" + + if reproduced_maps_dir.exists(): + shutil.rmtree(reproduced_maps_dir) flag_error = not system( - f"clinicadl train from_json {config_json} {output_dir} -s {split}" + f"clinicadl train from_json {str(config_json)} {str(reproduced_maps_dir)} -s {split}" ) assert flag_error - shutil.rmtree(output_dir) +def test_determinism(cmdopt, tmp_path): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "train_from_json" / "in" + ref_dir = base_dir / "train_from_json" / "ref" + tmp_out_dir = tmp_path / "train_from_json" / "out" + tmp_out_dir.mkdir(parents=True) -def test_determinism(): - input_dir = pathlib.Path("results/input_MAPS") - output_dir = pathlib.Path("results/reproduced_MAPS") + maps_dir = tmp_out_dir / "maps_roi_cnn" + reproduced_maps_dir = tmp_out_dir / "reproduced_MAPS" + if maps_dir.exists(): + shutil.rmtree(maps_dir) + if reproduced_maps_dir.exists(): + shutil.rmtree(reproduced_maps_dir) test_input = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - str(input_dir), + str(input_dir / "caps_roi"), + "t1-linear_mode-roi.json", + str(input_dir / "labels_list" / "2_fold"), + str(maps_dir), "-c", - "data/reproducibility_config.toml", + str(input_dir / "reproducibility_config.toml"), + "--no-gpu", ] # Run first experiment flag_error = not system("clinicadl " + " ".join(test_input)) assert flag_error input_hashes = create_hashes_dict( - input_dir, + maps_dir, ignore_pattern_list=["tensorboard", ".log", "training.tsv", "maps.json"], ) - # Reproduce experiment - config_json = input_dir.joinpath("maps.json") + # Reproduce experiment (train from json) + config_json = tmp_out_dir / "maps_roi_cnn/maps.json" flag_error = not system( - f"clinicadl train from_json {config_json} {output_dir} -s 0" + f"clinicadl train from_json {str(config_json)} {str(reproduced_maps_dir)} -s 0" ) assert flag_error compare_folders_with_hashes( - output_dir, + reproduced_maps_dir, input_hashes, ignore_pattern_list=["tensorboard", ".log", "training.tsv", "maps.json"], ) - shutil.rmtree(input_dir) - shutil.rmtree(output_dir) diff --git a/tests/test_transfer_learning.py b/tests/test_transfer_learning.py index f5acd0c54..95713d7ad 100644 --- a/tests/test_transfer_learning.py +++ b/tests/test_transfer_learning.py @@ -1,8 +1,13 @@ +import json import os import shutil +from os.path import join +from pathlib import Path import pytest +from tests.testing_tools import compare_folders + # Everything is tested on roi except for cnn --> multicnn (patch) as multicnn is not implemented for roi. @pytest.fixture( @@ -13,117 +18,148 @@ "transfer_cnn_multicnn", ] ) -def cli_commands(request): +def test_name(request): + return request.param + - if request.param == "transfer_ae_ae": +def test_transfer_learning(cmdopt, tmp_path, test_name): + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "transferLearning" / "in" + ref_dir = base_dir / "transferLearning" / "ref" + tmp_out_dir = tmp_path / "transferLearning" / "out" + tmp_target_dir = tmp_path / "transferLearning" / "target" + tmp_out_dir.mkdir(parents=True) + + caps_roi_path = input_dir / "caps_roi" + extract_roi_str = "t1-linear_mode-roi.json" + labels_path = input_dir / "labels_list" / "2_fold" + config_path = input_dir / "train_config.toml" + if test_name == "transfer_ae_ae": source_task = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_source", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), ] target_task = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_target", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_target_dir), "-c", - "data/train_config.toml", + str(config_path), "--transfer_path", - "results_source", + str(tmp_out_dir), ] - elif request.param == "transfer_ae_cnn": + name = "aeTOae" + elif test_name == "transfer_ae_cnn": source_task = [ "train", "reconstruction", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_source", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), ] target_task = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_target", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_target_dir), "-c", - "data/train_config.toml", + str(config_path), "--transfer_path", - "results_source", + str(tmp_out_dir), ] - elif request.param == "transfer_cnn_cnn": + name = "aeTOcnn" + elif test_name == "transfer_cnn_cnn": source_task = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_source", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), ] target_task = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_target", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_target_dir), "-c", - "data/train_config.toml", + str(config_path), "--transfer_path", - "results_source", + str(tmp_out_dir), ] - elif request.param == "transfer_cnn_multicnn": + name = "cnnTOcnn" + elif test_name == "transfer_cnn_multicnn": source_task = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_source", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_out_dir), "-c", - "data/train_config.toml", + str(config_path), ] target_task = [ "train", "classification", - "data/dataset/random_example", - "extract_roi.json", - "data/labels_list", - "results_target", + str(caps_roi_path), + extract_roi_str, + str(labels_path), + str(tmp_target_dir), "-c", - "data/train_config.toml", + str(config_path), "--transfer_path", - "results_source", - "--multi_network", + str(tmp_out_dir), ] + name = "cnnTOcnn" else: - raise NotImplementedError(f"Test {request.param} is not implemented.") - - return source_task, target_task + raise NotImplementedError(f"Test {test_name} is not implemented.") + if tmp_out_dir.exists(): + shutil.rmtree(tmp_out_dir) + if tmp_target_dir.exists(): + shutil.rmtree(tmp_target_dir) -def test_transfer(cli_commands): - if os.path.exists("results_source"): - shutil.rmtree("results_source") - if os.path.exists("results_target"): - shutil.rmtree("results_target") - - source_task, target_task = cli_commands - flag_source = not os.system("clinicadl " + " ".join(source_task)) - flag_target = not os.system("clinicadl " + " ".join(target_task)) + flag_source = not os.system("clinicadl -vvv " + " ".join(source_task)) + flag_target = not os.system("clinicadl -vvv " + " ".join(target_task)) assert flag_source assert flag_target - shutil.rmtree("results_source") - shutil.rmtree("results_target") + + with open(tmp_target_dir / "maps.json", "r") as out: + json_data_out = json.load(out) + with open(ref_dir / ("maps_roi_" + name) / "maps.json", "r") as ref: + json_data_ref = json.load(ref) + + json_data_ref["transfer_path"] = json_data_out["transfer_path"] + json_data_ref["gpu"] = json_data_out["gpu"] + json_data_ref["caps_directory"] = json_data_out["caps_directory"] + assert json_data_out == json_data_ref # ["mode"] == mode + + assert compare_folders( + tmp_target_dir / "groups", + ref_dir / ("maps_roi_" + name) / "groups", + tmp_path, + ) + assert compare_folders( + tmp_target_dir / "split-0" / "best-loss", + ref_dir / ("maps_roi_" + name) / "split-0" / "best-loss", + tmp_path, + ) diff --git a/tests/test_tsvtool.py b/tests/test_tsvtool.py deleted file mode 100644 index abdb34373..000000000 --- a/tests/test_tsvtool.py +++ /dev/null @@ -1,206 +0,0 @@ -import os -import shutil -from os import path - -import pandas as pd - -from clinicadl.utils.caps_dataset.data import load_data_test -from clinicadl.utils.split_manager import KFoldSplit - -merged_tsv = "data/tsvtool/anonymous_BIDS.tsv" -missing_mods = "data/tsvtool/anonymous_missing_mods" -reference_path = "data/tsvtool/anonymous_reference" -diagnoses = "AD CN MCI pMCI sMCI" - -""" -Check the absence of data leakage - 1) Baseline datasets contain only one scan per subject - 2) No intersection between train and test sets - 3) Absence of MCI train subjects in test sets of subcategories of MCI -""" - - -def check_subject_unicity(diagnosis_path): - print("Check unicity", diagnosis_path) - diagnosis_df_paths = os.listdir(diagnosis_path) - diagnosis_df_paths = [x for x in diagnosis_df_paths if x.endswith("_baseline.tsv")] - - for diagnosis_df_path in diagnosis_df_paths: - flag_unique = True - check_df = pd.read_csv(path.join(diagnosis_path, diagnosis_df_path), sep="\t") - check_df.set_index(["participant_id", "session_id"], inplace=True) - for subject, subject_df in check_df.groupby(level=0): - if len(subject_df) > 1: - flag_unique = False - - assert flag_unique - - -def check_independance(train_path, test_path): - print("Check independence") - diagnosis_df_paths = os.listdir(train_path) - diagnosis_df_paths = [x for x in diagnosis_df_paths if x.endswith("_baseline.tsv")] - - for diagnosis_df_path in diagnosis_df_paths: - flag_independant = True - train_df = pd.read_csv(path.join(train_path, diagnosis_df_path), sep="\t") - train_df.set_index(["participant_id", "session_id"], inplace=True) - test_df = pd.read_csv(path.join(test_path, diagnosis_df_path), sep="\t") - test_df.set_index(["participant_id", "session_id"], inplace=True) - - for subject, session in train_df.index: - if subject in test_df.index: - flag_independant = False - - assert flag_independant - - -def check_subgroup_independence(train_path, test_path): - print("Check subgroup independence") - diagnosis_df_paths = os.listdir(test_path) - diagnosis_df_paths = [x for x in diagnosis_df_paths if x.endswith("_baseline.tsv")] - sub_diagnosis_list = [ - x for x in diagnosis_df_paths if "MCI" in x and x != "MCI_baseline.tsv" - ] - - MCI_train_df = pd.read_csv(path.join(train_path, "MCI_baseline.tsv"), sep="\t") - MCI_train_df.set_index(["participant_id", "session_id"], inplace=True) - for sub_diagnosis in sub_diagnosis_list: - flag_independant = True - sub_test_df = pd.read_csv(path.join(test_path, sub_diagnosis), sep="\t") - sub_test_df.set_index(["participant_id", "session_id"], inplace=True) - - for subject, session in MCI_train_df.index: - if subject in sub_test_df.index: - flag_independant = False - - assert flag_independant - - MCI_test_df = pd.read_csv(path.join(test_path, "MCI_baseline.tsv"), sep="\t") - MCI_test_df.set_index(["participant_id", "session_id"], inplace=True) - for sub_diagnosis in sub_diagnosis_list: - flag_independant = True - sub_test_df = pd.read_csv(path.join(train_path, sub_diagnosis), sep="\t") - sub_test_df.set_index(["participant_id", "session_id"], inplace=True) - - for subject, session in MCI_test_df.index: - if subject in sub_test_df.index: - flag_independant = False - - assert flag_independant - - -def run_test_suite(formatted_data_path, n_splits, subset_name): - check_train = True - - if n_splits == 0: - train_path = path.join(formatted_data_path, "train") - test_path = path.join(formatted_data_path, subset_name) - if not path.exists(train_path): - check_train = False - - check_subject_unicity(test_path) - if check_train: - check_subject_unicity(train_path) - check_independance(train_path, test_path) - MCI_path = path.join(train_path, "MCI_baseline.tsv") - if path.exists(MCI_path): - check_subgroup_independence(train_path, test_path) - - else: - for split in range(n_splits): - train_path = path.join( - formatted_data_path, - "train_splits-" + str(n_splits), - "split-" + str(split), - ) - test_path = path.join( - formatted_data_path, - subset_name + "_splits-" + str(n_splits), - "split-" + str(split), - ) - - if not path.exists(train_path): - check_train = False - - check_subject_unicity(test_path) - if check_train: - check_subject_unicity(train_path) - check_independance(train_path, test_path) - MCI_path = path.join(train_path, "MCI_baseline.tsv") - if path.exists(MCI_path): - check_subgroup_independence(train_path, test_path) - - -def test_getlabels(): - """Checks that getlabels is working and that it is coherent with previous version in reference_path""" - output_path = "data/tsvtool_test" - flag_getlabels = not os.system( - f"clinicadl -vvv tsvtool getlabels {merged_tsv} {missing_mods} {output_path} " - f"--diagnoses AD --diagnoses CN --diagnoses MCI --diagnoses pMCI --diagnoses sMCI" - ) - assert flag_getlabels - tsv_list = [ - tsv_file for tsv_file in os.listdir(output_path) if tsv_file.endswith(".tsv") - ] - for file in tsv_list: - out_df = pd.read_csv(path.join(output_path, file), sep="\t") - ref_df = pd.read_csv(path.join(reference_path, file), sep="\t") - out_df_sorted = out_df.reindex(sorted(out_df.columns), axis=1) - ref_df_sorted = ref_df.reindex(sorted(ref_df.columns), axis=1) - assert out_df_sorted.equals(ref_df_sorted) - - shutil.rmtree(output_path) - - -def test_split(): - """Checks that: - - split and kfold are working - - the loading functions can find the output - - no data leakage is introduced in split and kfold. - """ - n_splits = 5 - train_path = path.join(reference_path, "train") - flag_split = not os.system(f"clinicadl -vvv tsvtool split {reference_path}") - flag_kfold = not os.system( - f"clinicadl -vvv tsvtool kfold {reference_path} --n_splits {n_splits}" - ) - assert flag_split - assert flag_kfold - flag_load = True - try: - _ = load_data_test( - path.join(reference_path, "validation"), diagnoses.split(" ") - ) - split_manager = KFoldSplit(".", reference_path, diagnoses.split(" "), n_splits) - for split in split_manager.split_iterator(): - _ = split_manager[split] - except FileNotFoundError: - flag_load = False - assert flag_load - - run_test_suite(reference_path, 0, "validation") - run_test_suite(reference_path, n_splits, "validation") - - shutil.rmtree(path.join(reference_path, "train")) - shutil.rmtree(path.join(reference_path, "validation")) - shutil.rmtree(path.join(reference_path, "train_splits-5")) - shutil.rmtree(path.join(reference_path, "validation_splits-5")) - - -def test_analysis(): - """Checks that analysis can be performed""" - results_path = path.join("data", "tsvtool", "analysis.tsv") - ref_analysis_path = path.join("data", "tsvtool", "anonymous_analysis.tsv") - flag_analysis = not os.system( - f"clinicadl tsvtool analysis {merged_tsv} {reference_path} {results_path} " - f"--diagnoses AD --diagnoses CN --diagnoses MCI --diagnoses pMCI --diagnoses sMCI" - ) - - assert flag_analysis - ref_df = pd.read_csv(ref_analysis_path, sep="\t") - out_df = pd.read_csv(results_path, sep="\t") - out_df_sorted = out_df.reindex(sorted(out_df.columns), axis=1) - ref_df_sorted = ref_df.reindex(sorted(ref_df.columns), axis=1) - assert out_df_sorted.equals(ref_df_sorted) - os.remove(results_path) diff --git a/tests/test_tsvtools.py b/tests/test_tsvtools.py new file mode 100644 index 000000000..954a4329b --- /dev/null +++ b/tests/test_tsvtools.py @@ -0,0 +1,256 @@ +import os +import shutil +from os import path +from os.path import join +from pathlib import Path + +import pandas as pd + +from clinicadl.utils.tsvtools_utils import extract_baseline +from tests.testing_tools import compare_folders + +""" +Check the absence of data leakage + 1) Baseline datasets contain only one scan per subject + 2) No intersection between train and test sets + 3) Absence of MCI train subjects in test sets of subcategories of MCI +""" + + +def check_is_subject_unique(labels_path_baseline: Path): + print("Check subject uniqueness", labels_path_baseline) + + flag_is_unique = True + check_df = pd.read_csv(labels_path_baseline, sep="\t") + check_df.set_index(["participant_id", "session_id"], inplace=True) + if labels_path_baseline.name[-12:] != "baseline.tsv": + check_df = extract_baseline(check_df, set_index=False) + for _, subject_df in check_df.groupby(level=0): + if len(subject_df) > 1: + flag_is_unique = False + assert flag_is_unique + + +def check_is_independant( + train_path_baseline: Path, test_path_baseline: Path, subject_flag=True +): + print("Check independence") + + flag_is_independant = True + train_df = pd.read_csv(train_path_baseline, sep="\t") + train_df.set_index(["participant_id", "session_id"], inplace=True) + test_df = pd.read_csv(test_path_baseline, sep="\t") + test_df.set_index(["participant_id", "session_id"], inplace=True) + + for subject, session in train_df.index: + if (subject, session) in test_df.index: + flag_is_independant = False + + assert flag_is_independant + + +def run_test_suite(data_tsv, n_splits): + check_train = True + if n_splits == 0: + train_baseline_tsv = data_tsv / "train_baseline.tsv" + test_baseline_tsv = data_tsv / "test_baseline.tsv" + if not train_baseline_tsv.exists(): + check_train = False + + check_is_subject_unique(test_baseline_tsv) + if check_train: + check_is_subject_unique(train_baseline_tsv) + check_is_independant(train_baseline_tsv, test_baseline_tsv) + + else: + for split_number in range(n_splits): + for folder, _, files in os.walk(data_tsv / "split"): + folder = Path(folder) + + for file in files: + if file[-3:] == "tsv": + check_is_subject_unique(folder / file) + train_baseline_tsv = folder / "train_baseline.tsv" + test_baseline_tsv = folder / "test_baseline.tsv" + if train_baseline_tsv.exists(): + if test_baseline_tsv.exists(): + check_is_independant(train_baseline_tsv, test_baseline_tsv) + + +def test_getlabels(cmdopt, tmp_path): + """Checks that getlabels is working and that it is coherent with + previous version in reference_path.""" + + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + import shutil + + bids_output = tmp_out_dir / "bids" + bids_directory = input_dir / "bids" + restrict_tsv = input_dir / "restrict.tsv" + output_tsv = tmp_out_dir + if tmp_out_dir.exists(): + shutil.rmtree(tmp_out_dir) + tmp_out_dir.mkdir(parents=True) + shutil.copytree(bids_directory, bids_output) + merged_tsv = input_dir / "merge-tsv.tsv" + missing_mods_directory = input_dir / "missing_mods" + + flag_getlabels = not os.system( + f"clinicadl -vvv tsvtools get-labels {str(bids_output)} {str(output_tsv)} " + f"-d AD -d MCI -d CN -d Dementia " + f"--merged_tsv {str(merged_tsv)} --missing_mods {str(missing_mods_directory)} " + f"--restriction_tsv {str(restrict_tsv)}" + ) + assert flag_getlabels + + out_df = pd.read_csv(tmp_out_dir / "labels.tsv", sep="\t") + ref_df = pd.read_csv(ref_dir / "labels.tsv", sep="\t") + assert out_df.equals(ref_df) + + +def test_split(cmdopt, tmp_path): + """Checks that: + - split and kfold are working + - the loading functions can find the output + - no data leakage is introduced in split and kfold. + """ + + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + n_test = 10 + n_splits = 2 + train_tsv = tmp_out_dir / "split/train.tsv" + labels_tsv = tmp_out_dir / "labels.tsv" + shutil.copyfile(input_dir / "labels.tsv", labels_tsv) + + flag_split = not os.system( + f"clinicadl -vvv tsvtools split {str(labels_tsv)} --subset_name test --n_test {n_test}" + ) + flag_getmetadata = not os.system( + f"clinicadl -vvv tsvtools get-metadata {str(train_tsv)} {str(labels_tsv)} -voi age -voi sex -voi diagnosis" + ) + flag_kfold = not os.system( + f"clinicadl -vvv tsvtools kfold {str(train_tsv)} --n_splits {n_splits} --subset_name validation" + ) + assert flag_split + assert flag_getmetadata + assert flag_kfold + + assert compare_folders(tmp_out_dir / "split", ref_dir / "split", tmp_out_dir) + + run_test_suite(tmp_out_dir, n_splits) + + +def test_analysis(cmdopt, tmp_path): + """Checks that analysis can be performed.""" + + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + merged_tsv = input_dir / "merge-tsv.tsv" + labels_tsv = input_dir / "labels.tsv" + output_tsv = tmp_out_dir / "analysis.tsv" + ref_analysis_tsv = ref_dir / "analysis.tsv" + + flag_analysis = not os.system( + f"clinicadl tsvtools analysis {str(merged_tsv)} {str(labels_tsv)} {str(output_tsv)} " + f"--diagnoses CN --diagnoses MCI --diagnoses Dementia" + ) + + assert flag_analysis + ref_df = pd.read_csv(ref_analysis_tsv, sep="\t") + out_df = pd.read_csv(output_tsv, sep="\t") + assert out_df.equals(ref_df) + + +def test_get_progression(cmdopt, tmp_path): + """Checks that get-progression can be performed""" + + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + input_progression_tsv = input_dir / "labels.tsv" + progression_tsv = tmp_out_dir / "progression.tsv" + ref_progression_tsv = ref_dir / "progression.tsv" + shutil.copyfile(input_progression_tsv, progression_tsv) + + flag_get_progression = not os.system( + f"clinicadl tsvtools get-progression {str(progression_tsv)} " + ) + assert flag_get_progression + + ref_df = pd.read_csv(ref_progression_tsv, sep="\t") + out_df = pd.read_csv(progression_tsv, sep="\t") + assert out_df.equals(ref_df) + + +def test_prepare_experiment(cmdopt, tmp_path): + """Checks that: + - split and kfold are working + - the loading functions can find the output + - no data leakage is introduced in split and kfold. + """ + + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + labels_tsv = tmp_out_dir / "labels.tsv" + shutil.copyfile(input_dir / "labels.tsv", labels_tsv) + + validation_type = "kfold" + n_valid = 2 + n_test = 10 + flag_prepare_experiment = not os.system( + f"clinicadl -vvv tsvtools prepare-experiment {str(labels_tsv)} --n_test {n_test} --validation_type {validation_type} --n_validation {n_valid}" + ) + + assert flag_prepare_experiment + + assert compare_folders(tmp_out_dir / "split", ref_dir / "split", tmp_out_dir) + + run_test_suite(tmp_out_dir, n_valid) + + +def test_get_metadata(cmdopt, tmp_path): + """Checks that get-metadata can be performed""" + base_dir = Path(cmdopt["input"]) + input_dir = base_dir / "tsvtools" / "in" + ref_dir = base_dir / "tsvtools" / "ref" + tmp_out_dir = tmp_path / "tsvtools" / "out" + tmp_out_dir.mkdir(parents=True) + + input_metadata_tsv = input_dir / "restrict.tsv" + metadata_tsv = tmp_out_dir / "metadata.tsv" + input_labels_tsv = input_dir / "labels.tsv" + labels_tsv = tmp_out_dir / "labels.tsv" + ref_metadata_tsv = ref_dir / "metadata.tsv" + + shutil.copyfile(input_metadata_tsv, metadata_tsv) + shutil.copyfile(input_labels_tsv, labels_tsv) + + flag_get_metadata = not os.system( + f"clinicadl tsvtools get-metadata {str(metadata_tsv)} {str(labels_tsv)} -voi diagnosis -voi sex -voi age" + ) + assert flag_get_metadata + + ref_df = pd.read_csv(ref_metadata_tsv, sep="\t") + out_df = pd.read_csv(metadata_tsv, sep="\t") + assert out_df.equals(ref_df) diff --git a/tests/testing_tools.py b/tests/testing_tools.py index 96e36a355..4985fb48b 100644 --- a/tests/testing_tools.py +++ b/tests/testing_tools.py @@ -1,4 +1,6 @@ import pathlib +from os import PathLike +from pathlib import Path from typing import Dict, List @@ -81,3 +83,86 @@ def models_equal(state_dict_1, state_dict_2, epsilon=0): print(f"Not equivalent: {key_item_1[0]} != {key_item_2[0]}") return False return True + + +def tree(dir_: PathLike, file_out: PathLike): + """Creates a file (file_out) with a visual tree representing the file + hierarchy at a given directory + + .. note:: + Does not display empty directories. + + """ + from pathlib import Path + + file_content = "" + + for path in sorted(Path(dir_).rglob("*")): + if path.is_dir() and not any(path.iterdir()): + continue + depth = len(path.relative_to(dir_).parts) + spacer = " " * depth + file_content = file_content + f"{spacer}+ {path.name}\n" + + print(file_content) + + Path(file_out).write_text(file_content) + + +def compare_folders(outdir: PathLike, refdir: PathLike, tmp_path: PathLike) -> bool: + """ + Compares the file hierarchy of two folders. + + Args: + outdir: path to the fisrt fodler. + refdir: path to the second folder. + tmp_path: path to a temporary folder. + """ + + from filecmp import cmp + from pathlib import PurePath + + file_out = PurePath(tmp_path) / "file_out.txt" + file_ref = PurePath(tmp_path) / "file_ref.txt" + tree(outdir, file_out) + tree(refdir, file_ref) + if not cmp(file_out, file_ref): + with open(file_out, "r") as fin: + out_message = fin.read() + with open(file_ref, "r") as fin: + ref_message = fin.read() + raise ValueError( + "Comparison of out and ref directories shows mismatch :\n " + "OUT :\n" + out_message + "\n REF :\n" + ref_message + ) + return True + + +def compare_folder_with_files(folder: str, file_list: List[str]) -> bool: + """Compare file existing in two folders + Args: + folder: path to a folder + file_list: list of files which must be found in folder + Returns: + True if files in file_list were all found in folder. + """ + import os + + folder_list = [] + for root, dirs, files in os.walk(folder): + folder_list.extend(files) + + print(f"Missing files {set(file_list) - set(folder_list)}") + return set(file_list).issubset(set(folder_list)) + + +def clean_folder(path, recreate=True): + from os import makedirs + from os.path import abspath, exists + from shutil import rmtree + + abs_path = abspath(path) + if exists(abs_path): + rmtree(abs_path) + if recreate: + makedirs(abs_path)