Matthieu Gilson (mattermost: @matgilson)
Julien Sein (mattermost: @julien.sein)
Jean-Luc Anton (mattermost: @jl-anton)
Andrea Bagante (mattermost: @andreabag)
Martin Szinte
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
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
Good first issues
issue one: tutorial of nilearn on surface analysis (https://nilearn.github.io/stable/auto_examples/00_tutorials/index.html)
issue two: find a good issue...
Communication channels
https://mattermost.brainhack.org/brainhack/channels/bhg23-marseille-surfacing_fmri_data
Skills
Onboarding documentation
No response
What will participants learn?
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.
Hi @brainhackorg/project-monitors my project is ready!