Standardized denoising strategies with fMRIprep

Project info

Standardized denoising strategies with fMRIprep

Project lead: Pierre Bellec, twitter @pierre_bellec mattermost @pierre.bellec he/him

Project collaborators: François paugam, mattermost @francois_p Annabelle Harvey, twitter @harvey_aa, mattermost @harveya

Registered Brainhack Global 2020 Event: Brainhack MTL 2020ish

**Project Description:**abels There are many strategies that have been proposed in the literature to denoise fMRI time series, and fMRIprep implements many of them. However, the data generated by fMRIprep is minimally preprocess and the user is left combining some confound variables of their choice to finalize fully preprocessed time series. There is detailed documentation in fMRIprep about what these confounds are, but users are left to (1) select a denoising strategy; (2) select the relevant confounds and regress them out. This project aims at contributing to two software libraries aimed at easy denoising either using the nilearn library (with load_confounds), or from the command line (with nii-masker). Contributions include improving documentation, tests and adding features.

Data to use:

Selecting an appropriate dataset for demo is one of the objective of the hackathon. See this issue.

Link to project repository/sources:

Goals for Brainhack Global 2020:

good first issue

Skills: A basic understanding of python, fMRI denoising and nilearn is required. Knowledge of pydra and BIDS is also a necessary for some of the issues we will be working on.

Tools/Software/Methods to Use:

Communication channels: ~fmriprep_denoising on We will use the jitsi integration on mattermost for meetings.

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Project Submission

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Develop tools to easily implement standardized fMRI denoising strategies using fMRIprep outputs.

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Jan 1, 0001 12:00 AM