Standardized denoising strategies with fMRIprep

[lunebellec]

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:

• load_confounds github page.
• nii-masker github page.
• fMRIprep documentation on confounds.

Goals for Brainhack Global 2020:

• complete this pull request to nii-masker: Add load confounds danjgale/nii-masker#20
• prepare a pull request for pydra support in nii-masker. See this issue: Use Pydra to manage dataflow danjgale/nii-masker#21
• see the relevant issues on load-confounds: https://github.com/SIMEXP/load_confounds/labels/brainhackMTL2020

good first issue

• working on a demo of load_confounds could be a good first issue. make a proper demo SIMEXP/load_confounds#74
• for users familiar with fmriprep, working on a resource dataset would also be a good first issue: identify an example dataset on openneuro SIMEXP/load_confounds#82
• going through the documentation of load_confounds or nii-masker and 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:

• load_confouds
• nii-masker
• the development fMRI dataset of nilearn

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

Project labels

• 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

Project Submission

Submission checklist

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.

• Provide an image of your project for the Brainhack Global 2020 website.
  

We would like to think about how you will credit and onboard new members to your project. If you’d like to share your thoughts with future project participants, you can include information about:

• Contributors will be added on an all contributors page

[complexbrains]

Hi @pbellec welcome to Brainhack Montreal 🤗 🎉 It feels like your project is ready but just missing an image to create its card as others here.

So if you can drop an image anywhere in the issue, we will publish it and have its card ready! 💯

Wish you all the bests with your participation in this amazing event 🤗 😉

[PeerHerholz]

Hi @pbellec (et al.), great project! Not sure if I missed it, but fmridenoise could be interesting here.

Cheers, Peer