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https://github.com/bids-apps/giga_connectome
generate connectome from fMRIPrep outputs
https://github.com/bids-apps/giga_connectome
Last synced: 2 days ago
JSON representation
generate connectome from fMRIPrep outputs
- Host: GitHub
- URL: https://github.com/bids-apps/giga_connectome
- Owner: bids-apps
- License: mit
- Created: 2022-12-07T14:42:00.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-12T19:55:03.000Z (7 months ago)
- Last Synced: 2024-04-13T10:44:53.499Z (7 months ago)
- Language: Python
- Homepage: https://giga-connectome.readthedocs.io/en/stable/
- Size: 821 KB
- Stars: 2
- Watchers: 4
- Forks: 4
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-bids - giga_connectome - App to extract signal from a parcellation with nilearn, typically useful in a context of resting-state data processing. (BIDS Apps / others)
README
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/bids-apps/giga_connectome)
[](https://github.com/bids-apps/giga_connectome/actions/workflows/test.yml)
[](https://github.com/bids-apps/giga_connectome/actions/workflows/run_precommit.yml)
[](https://giga-connectome.readthedocs.io/en/latest/?badge=stable)
# giga_connectome
This is a BIDS-App to extract signal from a parcellation with nilearn,
typically useful in a context of resting-state data processing.
## Description
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.
## Quick start
Pull from `Dockerhub` (Recommended)
```bash
docker pull bids/giga_connectome:latest
docker run -ti --rm bids/giga_connectome --help
```
If you want to get the bleeding-edge version of the app,
pull the `unstable` version.
```bash
docker pull bids/giga_connectome:unstable
```
## How to report errors
Please use the [GitHub issue](https://github.com/bids-apps/giga_connectome/issues) 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](https://github.com/bids-apps/giga_connectome/issues/new)!
## How to contribute
You can review open [issues]((https://github.com/bids-apps/giga_connectome/issues)) that we are looking for help with.
If you submit a new pull request please be as detailed as possible in your comments.
## Acknowledgements
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][].
[Contemporary Python Packaging - 2023]: https://effigies.gitlab.io/posts/python-packaging-2023/