This work is made available by a community of people, which originated from the INRIA Parietal Project Team and the scikit-learn but grew much further.
An up-to-date list of contributors can be seen in on GitHub
Additional credit goes to M. Hanke and Y. Halchenko for data and packaging.
The nilearn core developers are:
- Alexandre Gramfort https://github.com/agramfort
- Ben Cipollini https://github.com/bcipolli
- Bertrand Thirion https://github.com/bthirion
- Chris Gorgolewski https://github.com/chrisgorgo
- Danilo Bzdok https://github.com/banilo
- Elizabeth DuPre https://github.com/emdupre
- Gael Varoquaux https://github.com/GaelVaroquaux
- Jerome Dockes https://github.com/jeromedockes
- Julia Huntenburg https://github.com/juhuntenburg
- Kamalaker Dadi https://github.com/KamalakerDadi
- Kshitij Chawla https://github.com/kchawla-pi
- Mehdi Rahim https://github.com/mrahim
- Salma Bougacha https://github.com/salma1601
Alexandre Abraham, Gael Varoquaux, Kamalakar Reddy Daddy, Loïc Estève, Mehdi Rahim, Philippe Gervais were paid by the NiConnect project, funded by the French Investissement d'Avenir.
NiLearn is also supported by DigiCosme
and DataIA 
.
There is no paper published yet about nilearn. We are waiting for the package to mature a bit. However, the patterns underlying the package have been described in: Machine learning for neuroimaging with scikit-learn.
We suggest that you read and cite the paper. Thank you.
A huge amount of work goes into scikit-learn, upon which nilearn relies heavily. Researchers who invest their time in developing and maintaining the package deserve recognition with citations. In addition, the Parietal team needs citations to the paper in order to justify paying a software engineer on the project. To guarantee the future of the toolkit, if you use it, please cite it.
See the scikit-learn documentation on how to cite.