Nilearn: Statistics for Neuroimaging in Python

[ymzayek]

Title

Nilearn: Statistics for Neuroimaging in Python

Short description and the goals for the OHBM BrainHack

Nilearn is an open-source Python package for fast and easy analysis and visualization of brain images. It provides statistical and machine-learning tools, with instructive documentation and a friendly community. It includes applications such as multi-voxel pattern analysis (MVPA), decoding, predictive modelling, functional connectivity, and brain parcellations. Moreover, in recent years, Nilearn has expanded to include Generalized Linear Models (GLMs) to analyse functional MRI data. For the Brainhack, we aim to get feedback from the community about some of the highlights of the latest release. These include:

• A new module nilearn.interfaces to implement loading and saving utilities with various interfaces
• Ability to provide custom hemodynamic response function (HRF) for dealing with non human primate data in GLM analysis
• Interactive surface plotting using Plotly engine
• Improved contributing documentation

Finally, we are always looking to get feedback on our documentation and to onboard new contributors.

Link to the Project

https://github.com/nilearn/nilearn

Image for the OHBM brainhack website

https://raw.githubusercontent.com/nilearn/nilearn/main/doc/logos/nilearn-logo.png

Project lead

Yasmin Mzayek, Github: @ymzayek, Discord: ymzayek#1714 (EMEA - virtual) Alexis Thual, Github: @alexisthual, Discord: Alexis贺麒#7076 (Glasgow) Elizabeth DuPre, Github: @emdupre, Discord: emdupre#8727 (Glasgow) Hao-Ting Wang, Github: @htwangtw, Discord: hao#7367 (Glasgow) Bertrand Thirion, Github: @bthirion, Discord: bthirion#8863 (EMEA - virtual)

Main Hub

Europe / Middle East / Africa

Other Hub covered by the leaders

• [X] Glasgow
• [ ] Asia / Pacific
• [X] Europe / Middle East / Africa
• [ ] Americas

Skills

We welcome all contributions from various skill sets and levels. This can include opening discussions around improvements to the documenation and/or code base, answering or commenting on questions or issues raised on github and neurostars, reviewing pull requests, and contributing code.

Recommended tutorials for new contributors

• [ ] Set up a brainhack friendly computing environment
• [ ] A introduction to Bash
• [X] Python: Writing a script
• [ ] Python: Data analysis with Python
• [ ] Python: Visualisation
• [X] Machine learning basics
• [ ] BIDS
• [ ] Machine learning for neuroimaging
• [X] VCS: Using Git and Github
• [ ] VCS: Data management with Datalad
• [ ] Virtualization
• [ ] High Performance Computing: Oracle cloud resource

Good first issues

To help you get started with contributing to Nilearn, we have listed some good first issues on our project board for the Brainhack.

Twitter summary

With Nilearn, we aim to simplify statistical analysis and machine learning on brain images in Python by fostering an open and active contributing community. We hope you will join us! https://nilearn.github.io/stable/index.html

Short name for the Discord chat channel (~15 chars)

nilearn

Please read and follow the OHBM Code of Conduct

• [X] I agree to follow the OHBM Code of Conduct during the hackathon

[djarecka]

Thank you for submitting the project! We have 35 projects right now, woohoo! But that means the projects pitches will have to be short. We will give you tomorrow 2 minutes to pitch your project, you can have one slide or no slides! If you decide to use a slide, please include the link to the slide here.

And don't worry, you will still have more time to talk about your project during the BrainHack :-)

[ymzayek]

@djarecka here is the slide: https://github.com/ymzayek/nilearn-materials/blob/main/OHBMBrainhack2022/nilearnpitch.pdf