This is the implementation of the Graph WaveNet model used in our manuscript:

S. Wein , A. Schüller, A. M. Tome, W. M. Malloni, M. W. Greenlee, and E. W. Lang, Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures, Network Neuroscience 6 (3): 665–701 (2022).

The implementation is based on the Graph WaveNet proposed by:

Z. Wu, S. Pan, G. Long, J. Jiang, C. Zhang, Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019.

• pytroch>=1.00
• scipy>=0.19.0
• numpy>=1.12.1

Also a conda environment.yml file is provided. The environment can be installed with:

conda env create -f environment.yml

A short demo version is included in this repository, which can serve as a template to process your own MRI data. Artificial fMRI data is provided in the directory MRI_data/fMRI_sessions/ and the artificial timecourses have the shape (nodes,time). The adjacency matrix in form of the structural connectivity (SC) between brain regions can be stored in MRI_data/SC_matrix/. An artificial SC matrix with shape (nodes,nodes) is also provided in this demo version.

The training samples can be generated from the subject session data by running:

python generate_samples.py --input_dir=./MRI_data/fMRI_sessions/ --output_dir=./MRI_data/training_samples

The model can then be trained by running:

python gwn_for_brain_connectivity_train.py --data ./MRI_data/training_samples --save_predictions True

A Jupyter Notebook version is provided, which can be directly run in Google Colab with:

https://colab.research.google.com/github/simonvino/GraphWaveNet_brain_connectivity/blob/main/gwn_for_brain_connectivity_colab_demo.ipynb

Preprocessed fMRI and DTI data from Human Connectome Project data is publicly available under: https://db.humanconnectome.org.

A nice tutorial on white matter tracktography for creating a SC matrix is available under: https://osf.io/fkyht/.

If you considered this GWN architecture for brain connectivity analysis as useful, please cite our manuscript as:

@article{Wein2022,
    author = {Wein, S. and Schüller, A. and Tomé, A. M. and Malloni, W. M. and Greenlee, M. W. and Lang, E. W.},
    title = "{Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures}",
    journal = {Network Neuroscience},
    volume = {6},
    number = {3},
    pages = {665-701},
    year = {2022},
    month = {07},
    doi = {10.1162/netn_a_00252},
    url = {https://doi.org/10.1162/netn\_a\_00252},
}

And the model architecture was originally proposed by Wu et al.:

@inproceedings{Wu2019_GWN_traffic,
  title={Graph WaveNet for Deep Spatial-Temporal Graph Modeling},
  author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},
  booktitle={Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)},
  year={2019}
}