- Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention arxiv:2205.09576
A simple way to download part of ADHD-200 datasets is to use nilearn. These data can be used as training data.
from nilearn import datasets
adhd_dataset = datasets.fetch_adhd(n_subjects=40, "./data/")
You can make the dataset by:
from dataset import LoadADHD200
data = LoadADHD200(img_path="./data/adhd/data/",
mask_path="./data/ADHD200_mask_152_4mm.nii.gz",
save_fmri=True,
save_path="./data/adhd200.npy")
More preprocessed ADHD-200 data can be accessed here ADHD-200 Preprocessed.
- Training model
chmod +x train.sh
./train.sh
- Tensorboard
tensorboard --logdir=./logdir/
The result in ADHD-200 dataset shown in constructFBN.ipynb
The result in task-based fMRI dataset shown in task-gambling-avg-group-wise-40.ipynb.
We use the Baseline developed by our SNNUBIAI Lab for evaluation.
@article{liu2022discovering,
title={Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention},
author={Liu, Yiheng and Ge, Enjie and He, Mengshen and Liu, Zhengliang and Zhao, Shijie and Hu, Xintao and Zhu, Dajiang and Liu, Tianming and Ge, Bao},
journal={arXiv preprint arXiv:2205.09576},
year={2022}
}
@inproceedings{liu2023spatial,
title={Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks},
author={Liu, Yiheng and Ge, Enjie and Qiang, Ning and Liu, Tianming and Ge, Bao},
booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
pages={1--4},
year={2023},
organization={IEEE}
}
@article{liu2024spatial,
title={Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI},
author={Liu, Yiheng and Ge, Enjie and Kang, Zili and Qiang, Ning and Liu, Tianming and Ge, Bao},
journal={NeuroImage},
pages={120519},
year={2024},
publisher={Elsevier}
}