MEG

This folder contains all the code created by Oscar Ferrante and Ling Liu in the frame of the COGITATE project.

Setup:

Create a new conda environment by running the following:

conda env create --file=requirements_cogitate_meg.yaml

For the linear mixed midel (LMM) analysis used in the activation analysis, create a specific LMM conda environment by running the following:

conda env create --file=requirements_cogitate_meg_lmm.yaml

The environments are tailored for Linux and the HPC, so some things might break a little if you use windows or Mac (not tested very thoroughly).

In order to recreate the exact environment (reproducibility purposes) in which the code was developed, requirements files with build are also provided.

• requirements_cogitate_meg_exact.yml
• requirements_cogitate_meg_lmm_exact.yml

Installation time ~= 90min

Change root path:

To run the analysis described below on the sample data, make sure to change the bids root path in /meeg/config/config.py: $ROOT/sample_data/bids

Sample data and demo

Sample data, used to run a demo of the analysis pipeline, can be found here

MEG data from four subjects (two per data collection site) are provided. We provide bids converted data as well as preprocessed data (in ./derivatives/preprocessing/ and ./derivatives/fs/).

In order to run the demo, edit the scripts so that the bids paths point to the downloaded data.

Running preprocessing:

In the command line, enter:

python REPO_ROOT/cogitate-msp1/scripts/meeg/preprocessing/99_run_preproc.py --sub SA124 --visit V1 --record run --step 1

When the first preprocessing step is finished, enter:

python REPO_ROOT/cogitate-msp1/scripts/meeg/preprocessing/P99_run_preproc.py --sub SA124 --visit V1 --record run --step 2

Expected output: the script should generate a directory under: $ROOT/sample_data/bids/derivatives/preprocessing/sub-SA124 containing several subfolders, one for each preprocessing steps. The epoching files contain the final state of the data ready for the next analysis steps.

Run time ~= 90min

Running analyses:

For each analysis, run the scripts in the corresponding analysis folder (e.g., /meeg/activation) following the order reported in the file name (e.g., first run "S01_source_loc.py", then "S02_source_loc_ga.py" and so on). To run any of the individual-level analysis, enter:

python REPO_ROOT/cogitate-msp1/scripts/meeg/ANALYSIS_FOLDER/ANALYSIS_CODE.py --sub SA124 --visit V1

Replace ANALYSIS_FOLDER with the name of the folder corresponding to the analysis you want to run (e.g., activation) and ANALYSIS_CODE with the name of the script you want to execute (e.g., S01_source_loc.py). To run any of the group-level analysis (i.e., these analyses are marked in the script file name with the suffix "ga"), enter:

python REPO_ROOT/cogitate-msp1/scripts/meeg/ANALYSIS_FOLDER/ANALYSIS_CODE.py

List of analysis and corresponding run time

• activation: Individual-level analysis run time ~= 60min per participant Group-level analysis run time ~= 240min
• connectivity Individual-level analysis run time ~= 90min per participant Group-level analysis run time ~= 30min
• ged (to be run before the connectivtiy analysis) Individual-level analysis run time ~= 15min per participant Group-level analysis run time ~= 10min
• roi_mvpa Individual-level analysis run time ~= XXmin per participant Group-level analysis run time ~= XXmin
• source_modelling Individual-level analysis run time ~= 210min per participant Group-level analysis run time ~= 60min