Brain Network Transformer is the open-source implementation of the NeurIPS 2022 paper Brain Network Transformer.

Download the ABIDE dataset from here.

1. Change the path attribute in file source/conf/dataset/ABIDE.yaml to the path of your dataset.

2. Run the following command to train the model.

python -m source --multirun datasz=100p model=bnt,fbnetgen,brainnetcnn,transformer dataset=ABIDE,ABCD repeat_time=5 preprocess=mixup

• datasz, default=(10p, 20p, 30p, 40p, 50p, 60p, 70p, 80p, 90p, 100p). How much data to use for training. The value is a percentage of the total number of samples in the dataset. For example, 10p means 10% of the total number of samples in the training set.

• model, default=(bnt,fbnetgen,brainnetcnn,transformer). Which model to use. The value is a list of model names. For example, bnt means Brain Network Transformer, fbnetgen means FBNetGen, brainnetcnn means BrainNetCNN, transformer means VanillaTF.

• dataset, default=(ABIDE,ABCD). Which dataset to use. The value is a list of dataset names. For example, ABIDE means ABIDE, ABCD means ABCD.

• repeat_time, default=5. How many times to repeat the experiment. The value is an integer. For example, 5 means repeat 5 times.

• preprocess, default=(mixup, non_mixup). Which preprocess to applied. The value is a list of preprocess names. For example, mixup means mixup, non_mixup means the dataset is feeded into models without preprocess.

conda create --name bnt python=3.9
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
conda install -c conda-forge wandb
pip install hydra-core --upgrade
conda install -c conda-forge scikit-learn
conda install -c conda-forge pandas

• python=3.9
• cudatoolkit=11.3
• torchvision=0.13.1
• pytorch=1.12.1
• torchaudio=0.12.1
• wandb=0.13.1
• scikit-learn=1.1.1
• pandas=1.4.3
• hydra-core=1.2.0

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

@inproceedings{
  kan2022bnt,
  title={BRAIN NETWORK TRANSFORMER},
  author={Xuan Kan and Wei Dai and Hejie Cui and Zilong Zhang and Ying Guo and Carl Yang},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022},
}