flexible_cpm

Modified connectome-based predictive model. See preprint of book chapter outlining this method: https://osf.io/ay95f/

1. Enables inclusion of covariates at feature selection or model-building stage or both stages
2. Provides ability to threshold edges based on p-values (e.g. only select edges with p-values for correlation between edge and target variable < .01) or sparsity (e.g. select top 7.5% of edges based on correlation bewteen edge and target variable)
3. Permits different choices of k-fold cross-validation schemes
4. Returns weights (i.e. regression coefficients) from trained model for external validation on other datasets
5. Creates masks for visualisation in bioimagesuite and enables creation of masks when restricted connectivity matrices are used (i.e. when nodes are removed from the original 268 x 268 connectivity matrix)
6. Enables leave-site-out cross-validation
7. Optimises CPM for larger datasets via the use of parallel computing

Functions with prefix "CPM__" are adapted/generalised functions of code written by Xilin Shen and Emily Finn and is uploaded here with their permission (codeshare_behavioral_prediction.m as obtained here: https://www.nitrc.org/frs/download.php/8071/nn_code.zip). This code is therefore copyrighted to Xilin Shen and Emily Finn as detailed below:

Copyright 2015 Xilin Shen and Emily Finn This code is released under the terms of the GNU GPL v2. This code is not FDA approved for clinical use; it is provided freely for research purposes. If using this in a publication please reference this properly as: Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang, Chun MM,Papademetris X & Constable RT. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience 18, 1664-1671.

All other functions and scripts (e.g. functions with suffix "_CPM" - including run_flexible_CPM_leaveSiteOut) are original and this code is also released under the terms of the GNU GPL v2

Code is written and tested on MATLAB R2020a