Benchmark denoising strategies on fMRIPrep processed outputs #176
Labels
bhg:mtl_can_1
BHG 2021 Montreal event
git_skills:2_branches_PRs
modality:fMRI
modality:MRI
programming:documentation
Markdown, Sphinx
programming:Python
project_development_status:1_basic structure
project_type:coding_methods
project_type:documentation
project_type:pipeline_development
project_type:visualisation
project
status:published
status:web_ready
tools:BIDS
tools:fMRIPrep
tools:Jupyter
topic:connectome
topic:data_visualisation
Title
Benchmark denoising strategies on fMRIPrep processed outputs
Leaders
Hao-Ting Wang
Collaborators
Pierre Bellec
Brainhack Global 2021 Event
Brainhack Montreal
Project Description
The project is a continuation of load_confounds. The aim is to evaluate the impact of denoising strategy on functional connectivity data, using output processed by fMRIPrep LTS.
The work-in-progress repository is here: https://github.com/SIMEXP/fmriprep-denoise-benchmark
Link to project repository/sources
https://github.com/SIMEXP/fmriprep-denoise-benchmark
Goals for Brainhack Global
Make a jupyterbook based on one set of outputs
Good first issues
Communication channels
https://mattermost.brainhack.org/brainhack/channels/fmriprep_denoising
Skills
Onboarding documentation
No response
What will participants learn?
fMRI connectome processing, nilearn, and jupyter book.
Data to use
No response
Number of collaborators
4
Credit to collaborators
Contribution will be highlighted with contributor bot.
Image
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Type
coding_methods, pipeline_development
Development status
1_basic structure
Topic
connectome, data_visualisation
Tools
BIDS, fMRIPrep, Jupyter
Programming language
Python
Modalities
fMRI
Git skills
2_branches_PRs
Anything else?
No response
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!
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