Nilearn: Statistics and Machine Learning for Neuroimaging in Python

[man-shu]

Title

Nilearn: Statistics and Machine Learning 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 MRI 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.

Recent work in Nilearn has been centered on developing a new API to allow users to seamlessly work with surface data in a manner similar to volumetric data, enhancing support for the General Linear Model (GLM), enhancing the BIDS interface, and improving and updating the infrastructure and codebase.

We want to dedicate these Brainhack days towards direct interaction with our user-base and resolving any specific issues they might have. To this end, we encourage users to simply pop-in-and-say-hi. They are also welcome to present their queries via neurostars.org or open new issues/PRs on our GitHub repo. In addition, any interested contributors are also encouraged to work on pre-existing issues on our GitHub. To get started, new contributors should look for the "Good First Issue" or "Hackathon" labels.

Link to the Project

https://github.com/nilearn/nilearn

Image/Logo for the OHBM brainhack website

https://drive.google.com/file/d/1c2AcPvCCRWy80Se2m_lfVIlJc_1laon2/view?usp=sharing

Project lead

Himanshu Aggarwal, Github: @man-shu, Discord: man_shooo

Main Hub

Seoul

Link to the Project pitch

No response

Other hubs covered by the leaders

• [ ] Seoul
• [ ] Hybrid (Asia / Pacific)
• [ ] Hybrid (Europe / Middle East / Africa)
• [ ] Hybrid (Americas)

Skills

We welcome all users and contributions from various skill sets and levels. This can include opening discussions around improvements to the documentation 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

We recommend starting with Nilearn's basic tutorials and the introduction to Nilearn. This would help new users and contributors familiarize themselves with the package and its functionalities. They can even provide feedback on the tutorials and suggest improvements.

Good first issues

Here are a few issues:

• Use nilearn's logger instead of print statement
• Add scripts to collect stats for the nilearn repo.
• Update missing default value in docstrings
• Write tests for existing functions
• Add jupyterlite links to example gallery notebooks

You can also find an updated list of issues for the Brainhack here

Depending upon your comfort level with the package, issues can also be filtered as follows:

• Good first issues
• Effort: Low
• Effort: Medium
• Effort: High

Twitter summary

With @nilearn, we aim to simplify statistical analysis and machine learning on brain images in Python by fostering an open user base and active contributing community.

Find us @OHBM @brainhackorg in Seoul, Korea, from June 20th to 22nd 2024.

https://nilearn.github.io/stable/index.htmluser base

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

[sina-mansour]

@man-shu Thank you for your hacktrack project submission! The submission looks good to go (will be listed on our website soon). We look forward to seeing you in Seoul!