Code accompanying: Zarkali et al. Dementia risk in Parkinson’s disease is associated with interhemispheric connectivity loss and determined by regional gene expression. NeuroImage Clinical (in press) https://www.sciencedirect.com/science/article/pii/S2213158220303077#f0020
Usage:
- Data folder: Contains demographics, connectome level data for participants and module allocations derived from community Louvain algorithm (gamma=1).
- Demographics: Participant.csv
- Normalised connectome (glasser atlas): Data/Connectomes/<Participant_ID>/connectome_norm.csv
- Streamline length-transformed connectome (glasser atlas): Data/Connectomes/<Participant_ID>/new_connectome.csv
- Module allocations: These are found in Data/Modules/ - each text file contains a list of indexes per category/module
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ConnectomeAnalysis.ipynb: Jupyter notebook containing relevant code to perform community louvain on connectome-level data, devide connections to categories (subcortical-cortical, interhemispheric, intrahemispheric and intramodular) and calculate connection strength and streamline lenght per connection type
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Functions.py: Contains useful functions for connectome analysis.
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ConnectomeQC&Preprocessing.ipynb: Notebook to quickly visualise and QC connectomes.
Feel free to use any of the data and/or code; please consider citing our paper if you do so. If you have any questions regarding this repo not covered in our paper, please email [email protected].