This is a BIDS-App to extract signal from a parcellation with nilearn, typically useful in a context of resting-state data processing.
Functional connectivity is a common approach in analysing resting state fMRI data. Python tool Nilearn provides utilities to extract, denoise time-series on a parcellation and compute functional connectivity. Currently there's no standalone one stop solution to generate connectomes from fMRIPrep outputs. This BIDS-app combines Nilearn, TemplateFlow to denoise the data and generate timeseries and functional connectomes directly from fMRIPrep outputs. The workflow comes with several built in denoising strategies and three choices of atlases (MIST, Schaefer 7 networks, DiFuMo). Users can customise their own strategies and atlases using the configuration json files.
Pull from Dockerhub (Recommended)
docker pull bids/giga_connectome:latest
docker run -ti --rm bids/giga_connectome --helpIf you want to get the bleeding-edge version of the app,
pull the unstable version.
docker pull bids/giga_connectome:unstablePlease use the GitHub issue to report errors. Check out the open issues first to see if we're already working on it. If not, open up a new issue!
You can review open issues that we are looking for help with. If you submit a new pull request please be as detailed as possible in your comments.
If you use nilearn, please cite the corresponding paper: Abraham 2014, Front. Neuroinform., Machine learning for neuroimaging with scikit-learn http://dx.doi.org/10.3389/fninf.2014.00014
We acknowledge all the nilearn developers (https://github.com/nilearn/nilearn/graphs/contributors) as well as the BIDS-Apps team https://github.com/orgs/BIDS-Apps/people
This is a Python project packaged according to Contemporary Python Packaging - 2023.