netneurolab/shafiei_megfmrimapping

Code supporting Shafiei et al., 2022 "Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex".

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

This repository contains processing scripts and data in support of the preprint:

Shafiei, G., Baillet, S., & Misic, B. (2022). Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex. bioRxiv. https://doi.org/10.1101/2021.09.07.458941

code

The code folder contains all the code used to run the analyses and generate the figures. All code in preprocessing folder was written in Matlab and was used to preprocess MEG HCP data using Brainstorm. All code in analysis folder was written in Python and was used to analyze the preprocessed data. I regularly use netneurotools, a handy Python package developed in-house.

The preprocessing folder contains the following files:

• fcn_hcp_meg_process_connectivity.m is the main function used to preprocess MEG HCP data. It relies on Brainstorm and is a modified version of Brainstorm tutorial to preprocess resting-state MEG data from HCP.
• scpt_runBrainstorm.m is the script that runs fcn_hcp_meg_process_connectivity.m.
• resolution_metric.py is the script that is used to estimate MEG source localization error.

The analysis folder contains the following files:

• fcn_megfmri.py contains all the functions used in main analysis (scpt_megfmri.py)
• scpt_megfmri.py contains the script to run the main analyses and generate the figures of the manuscript.
• scpt_Fig1.py, scpt_Fig2.py, and so on contain the scripts to regenerate the main and supplementary figures of the manuscript.
• scpt_prepare_plotting_main.py and scpt_prepare_plotting_suppl.py contain the scripts that were used to prepare summary data to plot the main and supplementary figures (available in figures_data).

data

The data folder contains the data used to run the analyses. Specifically, it containes the preprocessed, parcellated group-average MEG and fMRI functional connectivity matrices from 33 unrelated subjects in HCP. Note that HCP data redistribution must follow their data terms. If you use any of the HCP data, please note that you must register with ConnectomeDB, agree to their terms and sign up for Open Access Data here. Please also cite relevant publications as mentioned here.

The figures_data folder contains the summary data that can be directly used to regenerate the figures.

The data folder also contains required files to use and plot brain maps with Schaefer atlas.