This work is made available by a community of people, amongst which the INRIA Parietal Project Team and the scikit-learn folks, in particular:
- Alexandre Abraham
- Alexandre Gramfort
- Vincent Michel
- Bertrand Thirion
- Fabian Pedregosa
- Gael Varoquaux
- Philippe Gervais
- Michael Eickenberg
- Danilo Bzdok
- Loïc Estève
- Kamalakar Reddy Daddy
- Elvis Dohmatob
- Alexandre Abadie
- Andres Hoyos Idrobo
- Salma Bougacha
- Mehdi Rahim
- Sylvain Lanuzel
- Kshitij Chawla
Many of also contributed outside of Parietal, notably:
- Chris Filo Gorgolewski
- Ben Cipollini
- Julia Huntenburg
- Martin Perez-Guevara
Thanks to M. Hanke and Y. Halchenko for data and packaging.
Alexandre Abraham, Gael Varoquaux, Kamalakar Reddy Daddy, Loïc Estève, Mehdi Rahim, Philippe Gervais where payed by the NiConnect project, funded by the French Investissement d'Avenir.
NiLearn is also supported by DigiCosme 
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 in the scikit-learn. Researchers that invest their time in developing and maintaining the package deserve recognition with citations. In addition, the Parietal team needs the 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.