BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
BrainGB is a unified, modular, scalable, and reproducible framework established for brain network analysis with GNNs. It is designed to enable fair evaluation with accessible datasets, standard settings, and baselines to foster a collaborative environment within computational neuroscience and other related communities. This library is built upon Pytorch and PyTorch Geometric.
Visit our BrainGB GitHub repository at https://github.com/HennyJie/BrainGB.
Our Recent Papers on GNN-based Brain Connectome Analysis using BrainGB
Here's a list of publications from our research group related to Brain Network Analysis:
| Year | Title | Venue | Code | Paper |
|---|---|---|---|---|
| 2024 | FedBrain: Federated Training of Graph Neural Networks for Connectome-based Brain Imaging Analysis | PSB 2024 | Link | Link |
| 2024 | BrainSTEAM: A Practical Pipeline for Connectome-based fMRI Analysis towards Subject Classification | PSB 2024 | Link | Link |
| 2023 | R-Mixup: Riemannian Mixup for Biological Networks | KDD 2023 | Link | Link |
| 2023 | Dynamic Brain Transformer with Multi-level Attention for Brain Network Analysis | BHI 2023 | Link | Link |
| 2023 | Transformer-based Hierarchical Clustering for Brain Network Analysis | ISBI 2023 | Link | Link |
| 2023 | Deep DAG Learning of Effective Brain Connectivity for fMRI Analysis | ISBI 2023 | Link | Link |
| 2023 | PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis | CHIL 2023 | Link | Link |
| 2022 | Comparing Shallow and Deep Graph Models for Brain Network Analysis | BrainNN 2022 | Link | Link |
| 2022 | BrainMixup: Data Augmentation for GNN-based Functional Brain Network Analysis | BrainNN 2022 | Link | Link |
| 2022 | Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning | KDD 2022 | Link | Link |
| 2022 | Brain Network Transformer | NeurIPS 2022 | Link | Link |
| 2022 | Multi-View Brain Network Analysis with Cross-View Missing Network Generation | BIBM 2022 | Link | Link |
| 2022 | Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis | EMBC 2022 | Link | Link |
| 2022 | Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis | MICCAI 2022 | Link | Link |
| 2022 | FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation | MIDL 2022 | Link | Link |
| 2022 | BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks | TMI 2022 | Link | Link |
These publications offer a range of approaches and tools for those interested in Brain Network Analysis.
Contribution
We welcome any form of contribution from the community. Please feel free to email us.
Citation
Please cite our paper if you find our platform useful for your work:
@article{cui2022braingb,
author = {Cui, Hejie and Dai, Wei and Zhu, Yanqiao and Kan, Xuan and Chen Gu, Antonio Aodong and Lukemire, Joshua and Zhan, Liang and He, Lifang and Guo, Ying and Yang, Carl},
title = {{BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks}},
journal={IEEE Transactions on Medical Imaging (TMI)},
year = {2022}
}