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BrainEigenmodes

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Code and data for the manuscript "Geometric constraints on human brain function"

Uses an eigenmode-based analysis to study how the geometric structure of the brain constrains function captured by fMRI

The code also serves as a toolbox for people to calculate surface and/or volume geometric eigenmodes using their own data (See demo_eigenmode_calculation.sh)

File descriptions

  1. data/: folder containing example data, parcellations, empirical data, results from the paper, figure source data, template eigenmodes, and template surfaces and volumes
  2. functions_matlab/: folder containing various utility analysis and visualization MATLAB functions
  3. surface_eigenmodes.py: Python script to calculate geometric eigenmodes of a cortical surface
  4. volume_eigenmodes.py: Python script to calculate geometric eigenmodes of a 3D volume
  5. demo_eigenmode_calculation.sh: Bash shell script to demonstrate how to calculate geometric eigenmodes
  6. demo_eigenmode_analysis.m: MATLAB script to demonstrate how to use eigenmodes to analyze fMRI data
  7. demo_eigenmode_visualization.m: MATLAB script to demonstrate how to visualize surface and volume eigenmodes
  8. demo_connectome_eigenmode_calculation.m: MATLAB script to demonstrate how to calculate connectome and EDR connectome eigenmodes
  9. demo_wave_model_simulation.m: MATLAB script to demonstrate how to simulate waves on a cortical surface using eigenmodes
  10. generate_paper_figures_main_bioRxiv.m: MATLAB script to generate the Main figures of the bioRxiv preprint
  11. generate_paper_figures_supp_bioRxiv.m: MATLAB script to generate the Supplementary figures of the bioRxiv preprint
  12. generate_paper_figures_main_Nature.m: MATLAB script to generate the Main figures of the Nature paper
  13. generate_paper_figures_supp_Nature.m: MATLAB script to generate the Supplementary figures of the Nature paper
  14. generate_paper_figures_extended_Nature.m: MATLAB script to generate the Extended Data figures of the Nature paper

Installation

Download the repository and you're good to go. Read the comments and documentation within each code for usage guidance.

Downloading data

Due to their file sizes exceeding the limit allowed by GitHub, you will need to fill the data/empirical/, data/results/, and data/template_eigenmodes/ directories with data that you can download from this OSF repository. The total file size is 11.3GB.

VERY IMPORTANT NOTE: Some portions of generate_paper_figures.m and generate_paper_suppfigures.m require the abovementioned data hosted in OSF. Hence, you need them to be saved in the appropriate folders to be able to run these two scripts smoothly.

Original data

Original empirical data are from the Human Connectome Project. Please consult the link for detailed information about access, licensing, and terms and conditions of usage.

Dependencies

Some important aspects you need to do before running demo_eigenmode_calculation.sh, surface_eigenmodes.py, or volume_eigenmodes.py

  1. Install FreeSurfer and load module if using an HPC system.
  2. Install Connectome Workbench and load module if using an HPC system.
  3. Install Gmsh and load module if using an HPC system.
  4. Install the following Python libraries (e.g., via pip): lapy(v1.0.0), nibabel, and brainspace
  5. Make sure you also have the following common Python libraries: numpy, scipy, os, argparse, subprocess

Some of the MATLAB-based scripts depend on packages developed by others. Copies of these packages have been stored in the functions_matlab/ folder to ensure version compatibility. However, please visit their respective links below to get more details and don't forget to give them some love.

  1. gifti
  2. cifti
  3. cbrewer (NOTE: The link is for the new version cbrewer2, but the repo uses the older version.)
  4. Stuart Oldham's repository for drawing ROI boundaries on a surface
  5. Frantisek Vasa's repository for creating rotations of a parcellated map

Compatibility

The codes have been tested on versions of Python from 3.7 to 3.8 and versions of MATLAB from R2019b to R2020b. The codes are also only compatible with Lapy up to v1.0.0.

Citation

If you use our code in your research, please cite us as follows:

[PREPRINT] J.C. Pang, K.M. Aquino, M. Oldehinkel, P.A. Robinson, B.D. Fulcher, M. Breakspear, A. Fornito, Geometric constraints on human brain function, bioRxiv (2022) (DOI: 10.1101/2022.10.04.510897)

[ARTICLE] J.C. Pang, K.M. Aquino, M. Oldehinkel, P.A. Robinson, B.D. Fulcher, M. Breakspear, A. Fornito, Geometric constraints on human brain function, Nature, 618, 566–574 (2023) (DOI: 10.1038/s41586-023-06098-1)

Further details

Please contact james.pang1@monash.edu if you need any further details.