This is the official implementation for the accepted MICCAI 2022 (Oral) paper Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. This framework consists of two modules: a brain network-oriented backbone prediction model and a globally shared explanation generator that can highlight disorder-specific biomarkers including salient Regions of Interest (ROIs) and important connections. The whole implementation is built upon PyTorch and PyTorch Geometric.

The framework needs the following dependencies:

torch~=1.10.2
numpy~=1.22.2
nni~=2.4
PyYAML~=5.4.1
scikit-learn~=1.0.2
networkx~=2.6.2
scipy~=1.7.3
tensorly~=0.6.0
pandas~=1.4.1
libsvm~=3.23.0.4
matplotlib~=3.4.3
tqdm~=4.62.3
torch-geometric~=2.0.3
h5py~=3.6.0

To install the dependencies, run:

pip install -r requirements.txt

The cuda version we used is 10.1. We installed torch through Conda (v4.11.0) with cuda support.

This repository is organized into the following folders:

- `./`: The main functions for backbone and explanation generator training.
- `./models`: Models.
- `./utils`: Utility functions.
- `./analysis`: Visualizations and testing scripts.
- `./baselines`: The baseline models and testing scripts.

To train our model on any of the datasets in our paper, simply run:

python main_explainer.py --dataset_name=<dataset_name> [--explain]

The --dataset_name is the name of the dataset you want to use. We tested it against the following datasets:

• HIV
• BP
• PPMI (Can be downloaded here)

Please place the dataset files in the ./datasets/ folder under the root folder.

The --explain argument is optional. By passing --explain, the framework will trigger the explanation enhanced model IBGNN+. Otherwise, the framework will tests the backbone model IBGNN.

The four shallow baselines we included in our paper are in the baselines/shallow folder.

To test the deep baseline models, run:

python main_explainer.py --dataset_name=<dataset_name> --model_name=<model_name>

Similarly, the --dataset_name is the name of the dataset to train the model on. The --model_name can be one of the following deep baselines:

• GCN
• GAT
• PNA

For the other two state-of-the-art deep baselines on brain networks, BrainNetCNN and BrainGNN, we use the publicly available implementation from their corresponding links.

Please cite our paper if you find this code useful for your work:

@inproceedings{cui2022interpretable,
  title={Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis},
  author={Cui, Hejie and Dai, Wei and Zhu, Yanqiao and Li, Xiaoxiao and He, Lifang and Yang, Carl},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={375--385},
  year={2022},
  organization={Springer}
}