Improving surface functionality in Nilearn

[ymzayek]

Title

Improving surface functionality in Nilearn

Short description and the goals for the OHBM BrainHack

INTRODUCTION

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. In recent years, we have developed functionality to support working with surfaces and we now have concrete plans for extending surface support in Nilearn in order to extend this functionality. To this end, we aim to get contributors to get familiar with the existing surface functionality and to work on issues to improve the surface module and surface plotting functions.

GOALS FOR THE BRAIN HACK

Contributors are encouraged to work on issues in the project board by opening pull requests. These include good first issues that will help first time contributors get started and also issues relating to surface functionality. Contributors are also welcome to open issues for relevant bugs or suggestions for enhancement of the current surface functionality.

Link to the Project

https://github.com/nilearn/nilearn

Image for the OHBM brainhack website

https://nilearn.github.io/stable/_static/nilearn-transparent.png

Project lead

Hao-Ting Wang, Github: @htwangtw, Discord: haodareyou
Remi Gau, Github: @Remi-Gau, Discord: remigau
Yasmin Mzayek, Github: @ymzayek, Discord: ymzayek
Elizabeth DuPre, Github: @emdupre, Discord: emdupre#8727

Main Hub

Montreal

Other Hub covered by the leaders

• Montreal
• Asia / Pacific
• 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
• Python: Writing a script
• Python: Data analysis with Python
• Python: Visualisation
• Machine learning basics
• BIDS
• Machine learning for neuroimaging
• VCS: Using Git and Github
• VCS: Data management with Datalad
• Virtualization
• High Performance Computing: Oracle cloud resource

Good first issues

Surface project issues

Centralized on this kanban board: https://github.com/orgs/nilearn/projects/6

Other small issues to open your first pull request

• update contributors affiliation
• stop checking versions of some old dependencies
• refactor some tests to use context managers
• nilearn fun facts

Twitter summary

Get familiar with nilearn's surface data functionality and help us improve it! https://nilearn.github.io/stable/index.html

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

nilearn_surface

Please read and follow the OHBM Code of Conduct

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

[smeisler]

👍
Would especially be nice if multi-run GLMs can be introduced for surface files, as requested in https://neurostars.org/t/glm-on-surface-data-in-nilearn/22632.