Group-Specific Discriminant Analysis for Sex Differences in Human Brain Lateralization

Introduction

This repository contains the implementation of Group-Specific Discriminant Analysis (GSDA) with application for investigating sex differences in human brain lateralization.

Framework

Datasets

The resting-state fMRI data from HCP [1] and GSP [2] is used in this study. Code for data preprocessing is available at /preprocess. Processed data is available at Zenodo: [HCP], [GSP].

System Requirements

numpy>=1.24.3
pandas>=1.5.3
scipy>=1.10.1
scikit-learn>=1.2.2
pytorch>=2.0.0
yacs

Installation Guide

pip install -r requirements.txt

Instructions for Use

Basic usage:

python main.py --cfg configs/demo-hcp.yaml

Please create more .yaml files for different random seeds and datasets.

Demo

We provide GSDA running demo through a cloud Jupyter notebook on . Note the number of repetition is limited for faster demonstrations. This demo takes 10-20 minutes to complete the training and testing process.

References

[1] Smith, S. M. et al. Resting-state fMRI in the human connectome project. NeuroImage 80, 144–168 (2013)

[2] Holmes, A. J. et al. Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 1–16 (2015)