miccai

MDRL

This is the implementation for the paper Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI. This framework consists of three parts: (1) dynamic graph construction, (2) modularity-constrained spatiotemporal graph neural network (MSGNN) for dynamic feature learning, and (3) prediction and biomarker detection.The whole implementation is built upon PyTorch.

Folder Structure

This repository is organized into the following folders:

- `./main.py`: The main functions for training and testing.
- `./data_pre.py`: Data preparation.
- `./net`: Models.

We used the following datasets:

• HAND
• ABIDE (Can be downloaded here)
• MDD (Can be downloaded here)

Please place the preprocessed dataset files under the root folder.

Dependencies

The framework needs the following dependencies:

torch~=1.13.0
numpy~=1.21.5
torch_scatter~=2.1.0+pt113cu117
scipy~=1.9.3
einops~=0.5.0

Many thanks to Dr Byung-Hoon Kim for sharing their project STAGIN.