Comments (1)
Hi! Thanks for your interest in our work!
You can see the code I created to load data from the uk biobank and human connectome project (HCP) here: https://github.com/tjiagoM/spatio-temporal-brain/blob/master/datasets.py (including how we preprocessed the data)
You will probably need to know how data loading works with pytorch geometric (https://pytorch-geometric.readthedocs.io/en/latest/notes/create_dataset.html), before looking to this module.
Unfortunately, I cannot give you the preprocessed data from the UK Biobank, but for the HCP you should have free access. Maybe if you email me it would be easier to discuss data sharing, but I'll try to doublecheck what are the rules here.
Hope this helps!
P.S.: I'm currently quite busy in answering to a rebuttal round of a journal at which we submitted this work. As a consequence, I've been changing the code in a branch named dev
. As the paper is not yet accepted and includes some changes which are not in the paper I'm not putting this in the main branch for now. However, if you are curious, feel free to check: https://github.com/tjiagoM/spatio-temporal-brain/tree/dev
from spatio-temporal-brain.
Related Issues (16)
- Check statistics per person/dataset
- Stratification according to both sex and session
- Try cosine loss
- Change the default weight initialisation HOT 1
- Correct stratification for no people across folds HOT 1
- help HOT 3
- Remove disconnected nodes after threshold
- Plot the graph representation HOT 1
- data
- Specialised temporal convolutions for the LR/RL HOT 1
- Use GAT
- Diffpool and concat pooling
- Option for normalise timeseries per subject
- Option for no timeseries normalisation/scaling
- Option for functional connectivity instead of structural
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from spatio-temporal-brain.