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:
nii-masker. See this issue: https://github.com/danjgale/nii-masker/issues/21
good first issue
nii-maskerand suggesting fix or improvements is also an excellent way to start discovering the projects and make contributions.
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 mattermost.brainhack.org. We will use the jitsi integration on mattermost for meetings.
Type of project: #coding_methods, #documentation
Project development status: 2_releases_existing
Topic of the projet: #reproducible_scientific_methods, #fMRI, #denoising
Tools used in the project: #fMRIPrep, #nilearn
Tools skill level required to enter the project (more than one possible): #familiar
Programming language used in the project: #Python, #shell_scripting, #workflows
Modalities involved in the project (if any): #fMRI
Git skills reuired to enter the project (more than one possible): 2_branches_PRs
I added all of the labels I want an associate to my project
Once the issue is submitted, please check items in this list as you add under ‘Additional project info’
Link to your project: could be a code repository, a shared document, etc.
Goals for Brainhack Global 2020: describe what you want to achieve during this brainhack.
Flesh out at least 2 “good first issues”: those are tasks that do not require any prior knowledge about your project, could be defined as issues in a GitHub repository, or in a shared document.
Skills: list skills that would be particularly suitable for your project. We ask you to include at least one non-coding skill. Use the issue labels for this purpose.
Chat channel: A link to a chat channel that will be used during the Brainhack Global 2020 event. This can be an existing channel or a new one. We recommend using the Brainhack space on Mattermost.
Twitter-sized summary of your project pitch.
Develop tools to easily implement standardized fMRI denoising strategies using fMRIprep outputs.
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