new project: Surf(ac)ing fMRI data

[MatthieuGilson]

Title

Surf(ac)ing fMRI data

Leaders

Matthieu Gilson (mattermost: @matgilson)
Julien Sein (mattermost: @julien.sein)
Jean-Luc Anton (mattermost: @jl-anton)
Andrea Bagante (mattermost: @andreabag)
Martin Szinte

Collaborators

No response

Brainhack Global 2023 Event

Brainhack Marseille

Project Description

The goal of this project is to combine tools in a pipeline for surface-based analysis of fMRI data. Surface-based analysis is a powerful way to align data from different subjects and datasets (https://www.nature.com/articles/s41598-020-62832-z, https://www.sciencedirect.com/science/article/abs/pii/S1361841512000357). Join us to test tools that will help you to analyze your own fMRI data at the whole-brain level!

The pipeline will combine open-science tools like fMRIprep, Workbench (from HCP), nilearn (Python library). We will provide a couple of subject data to benchmark the tools; they will be formatted in the BIDS format (https://bids.neuroimaging.io/), which is a standard to share data. Experience in Python is recommended. You should install a Python distribution like Anaconda beforehand (https://anaconda.org/), we may also use MRI viewer like mango (https://mangoviewer.com/) and tools from Workbench (https://humanconnectome.org/software/connectome-workbench).

Link to project repository/sources

TBA

Goals for Brainhack Global

• contribute to benchmarking of open-source tools in fMRI analysis
• contribute to promoting sharable open-source tools in local neuroscientific community, beyond the computational community

Good first issues

1. issue one: tutorial of nilearn on surface analysis (https://nilearn.github.io/stable/auto_examples/00_tutorials/index.html)

2. issue two: find a good issue...

Communication channels

https://mattermost.brainhack.org/brainhack/channels/bhg23-marseille-surfacing_fmri_data

Skills

• python coding: 60%
• data manipulation: 40%

Onboarding documentation

No response

What will participants learn?

• MRI data manipulation (including BIDS format)
• fMRI preprocessing (fmriprep, workbench)
• decoding (nilearn)

Data to use

we will provide a few subjects as a testbed

Number of collaborators

3

Credit to collaborators

Collaborators will be added in the README file of the github repo

Image

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Type

pipeline_development

Development status

0_concept_no_content

Topic

MR_methodologies, neural_decoding

Tools

BIDS, fMRIPrep, Nipype

Programming language

Python, unix_command_line

Modalities

fMRI, MRI

Git skills

1_commit_push

Anything else?

No response

Things to do after the project is submitted and ready to review.

• Add a comment below the main post of your issue saying: Hi @brainhackorg/project-monitors my project is ready!
• Twitter-sized summary of your project pitch.

[arnaudletroter]

Thank you for submitting your project to BrainHack Marseille 2023.
Your project is now visible online !

Arnaud for The BHM organization team.