This repository contains PyTorch code for spatio-temporal deep learning on functional MRI data for phenotyping prediction. The original work was published at MLCN 2021:
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
HCP data can be directly downloaded from Human Connectome Project
For PyTorch and dependencies installation, please follow instructions in install.md
In the folder /data/:
python preprocessing_nodetimeseries.py subjects.txt 25 /data/HCP/rfMRI ../outputs/
For sex classification
python ./tools/train_node_timeseries.py --nodes 25 --bs 64 --epochs 100 --gpu 0 --windows 100 --data_path path_to_data
For fluid intelligence regression
python ./tools/train_node_timeseries.py --nodes 25 --bs 64 --epochs 100 --gpu 0 --windows 100 --fluid --data_path path_to_data
Starting tensorboard visualisation
tensorboard --logdir ./logs/MS-G3D/
Coming soon
This repository is based on the following repositories:
-
repository: MS-G3D - paper: Z.Liu et al 2020
-
repository: ST-GCN-HCP - paper: S.Gadgil et al 2020
and
- repository: ST-GCN - paper: S.Yan et al 2018
Please cite this work if you found it useful:
@misc{dahan2021improving,
title={Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity},
author={Simon Dahan and Logan Z. J. Williams and Daniel Rueckert and Emma C. Robinson},
year={2021},
eprint={2109.03115},
archivePrefix={arXiv},
primaryClass={q-bio.NC}
}