https://github.com/bids-apps/giga_connectome
generate connectome from fMRIPrep outputs
https://github.com/bids-apps/giga_connectome
Last synced: 21 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 (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-21T06:37:02.000Z (about 1 year ago)
- Last Synced: 2025-04-21T07:42:56.721Z (about 1 year ago)
- Language: Python
- Homepage: https://giga-connectome.readthedocs.io/en/stable/
- Size: 904 KB
- Stars: 7
- Watchers: 3
- Forks: 6
- Open Issues: 26
-
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://doi.org/10.21105/joss.07061)
[](#contributors)
[](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://results.pre-commit.ci/latest/github/bids-apps/giga_connectome/main)
[](https://giga-connectome.readthedocs.io/en/latest/?badge=stable)
[](https://hub.docker.com/r/bids/giga_connectome/tags)
# 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.
You can read our [JOSS paper](https://doi.org/10.21105/joss.07061) for the background of the project and the details of implementations.
## Description
Functional connectivity is a common approach in analysing resting state fMRI data.
The Python tool `Nilearn` provides utilities to extract and denoise time-series on a parcellation.
`Nilearn` also has methods to compute functional connectivity.
While `Nilearn` provides useful methods to generate connectomes,
there is no standalone one stop solution to generate connectomes from `fMRIPrep` outputs.
`giga-connectome` (a BIDS-app!) combines `Nilearn` and `TemplateFlow` to denoise the data, generate timeseries,
and most critically `giga-connectome` generates functional connectomes directly from `fMRIPrep` outputs.
The workflow comes with several built-in denoising strategies and
there are several choices of atlases (MIST, Schaefer 7 networks, DiFuMo, Harvard-Oxford).
Users can customise their own strategies and atlases using the configuration json files.
## Supported `fMRIPrep` versions
`giga-connectome` fully supports outputs of fMRIPrep LTS (long-term support) 20.2.x.
For `fMRIPrep` 23.1.0 and later, `giga-connectome` does not support ICA-AROMA denoising,
as the strategy is removed from the `fMRIPrep` workflow.
## 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.
If you have any question related how to create a pull request, you can check our [documentation for contributors](https://giga-connectome.readthedocs.io/en/latest/contributing.html).
## Contributors
Hao-Ting Wang
🤔 🔬 💻 ⚠️
Quentin Dessain
📓 📦
Natasha Clarke
📓 💡 🐛
Remi Gau
🚇 🚧
Lune Bellec
🤔 💵
Jon Cluce
🐛
Emeline Mullier
🐛
James Kent
🐛 📖
Marcel Stimberg
📓 📖 🐛
## Acknowledgements
Please cite the following paper if you are using `giga-connectome` in your work:
```bibtex
@article{Wang2025,
doi = {10.21105/joss.07061},
url = {https://doi.org/10.21105/joss.07061},
year = {2025}, publisher = {The Open Journal},
volume = {10},
number = {110},
pages = {7061},
author = {Hao-Ting Wang and Rémi Gau and Natasha Clarke and Quentin Dessain and Lune Bellec},
title = {Giga Connectome: a BIDS-app for time series and functional connectome extraction},
journal = {Journal of Open Source Software}
}
```
`giga-connectome` uses `nilearn` under the hood,
hence please consider cite `nilearn` using the Zenodo DOI:
```bibtex
@software{Nilearn,
author = {Nilearn contributors},
license = {BSD-4-Clause},
title = {{nilearn}},
url = {https://github.com/nilearn/nilearn},
doi = {https://doi.org/10.5281/zenodo.8397156}
}
```
Nilearn’s Research Resource Identifier (RRID) is: [RRID:SCR_001362][]
We acknowledge all the [nilearn developers][]
as well as the [BIDS-Apps team][]
This is a Python project packaged according to [Contemporary Python Packaging - 2023][].
[Contemporary Python Packaging - 2023]: https://effigies.gitlab.io/posts/python-packaging-2023/
[RRID:SCR_001362]: https://rrid.site/data/record/nlx_144509-1/SCR_001362/resolver?q=nilearn&l=nilearn&i=rrid:scr_001362
[nilearn developers]: https://github.com/nilearn/nilearn/graphs/contributors
[BIDS-Apps team]:https://github.com/orgs/BIDS-Apps/people