Disrupted sleep is heavily implicated in a wide variety of psychiatric and neurological illnesses. Insufficient sleep is well known to lead to neuronal hyperexcitabilty, and may lead mechanistically to brain pathologies via metabolic and inflammatory processes.
In this project we will use whole-brain modelling of fMRI data from the Stockholm Sleepy Brain Study to investigate physiological and network-level signatures and mechanisms of sleep deprivation.
We will be using a newly-developed approach for whole-brain modelling in Python using PyTorch, a widely-used machine learning library, as well as various standard Python neuroimaging tools (nilearn, nibabel, hcp_utils). All of the code needed is contained in this repo, and instructions for downloaded the requisite data are given below.
(Better: fork first, then clone your fork and add this repo as upstream)
git clone [email protected]:mygithubusername/kcnischool2022-projectweek
cd kcnischool2022-projectweek
git remote add upstream https://github.com/griffithslab/kcnischool2022-projectweek
git fetch upstream
git merge upstream/mainconda activate myenvcd data
python download_ds000201_data.py
cd ..( in the notebooks folder )
When you're ready, pull request (PRs) back to main:
First add your new stuff on a feature branch in your fork:
git checkout -b "mynewfeature"
git add mynewfile.ipynb
git commit mynewfile.ipynb -m"comment on mynewfile"
git push -u origin mynewfeature...then submit a PR from your github fork, where this push will appear!
PyTorch whole-brain modelling methodology:
- https://www.biorxiv.org/content/10.1101/2022.05.19.492664v1
- https://www.biorxiv.org/content/10.1101/2022.06.09.494069v1
Original model by Deco et al.
- https://www.jneurosci.org/content/33/27/11239 ([PDF])(https://drive.google.com/file/d/1ImucIqk5Cl-8fXVKzal8jY5IaiP5pLgw/view?usp=sharing)
Sleep fMRI data from:
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