Benchmark denoising strategies on fMRIPrep processed outputs

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

1. Check if the preprocessing parameters are sensible: https://github.com/SIMEXP/fmriprep-denoise-benchmark/issues/10
2. Select atlases and possibly save with consistent naming convention: https://github.com/SIMEXP/fmriprep-denoise-benchmark/issues/2

Communication channels

https://mattermost.brainhack.org/brainhack/channels/fmriprep_denoising

Skills

• Python
• Jupyter book
• fMRI preprocessing

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.

• 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.