brainhackorg / global2020

Brainhack Global 2020
https://brainhack.org/global2020/
MIT License
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Standardized denoising strategies with fMRIprep #126

Open pbellec opened 3 years ago

pbellec commented 3 years ago

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 mattermost.brainhack.org. We will use the jitsi integration on mattermost for meetings.

Project labels

Project Submission

Submission checklist

Once the issue is submitted, please check items in this list as you add under ‘Additional project info’

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

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:

complexbrains commented 3 years ago

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 commented 3 years ago

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

Cheers, Peer

pbellec commented 3 years ago

Hi @pbellec welcome to Brainhack Montreal hugs tada 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! 100

Wish you all the bests with your participation in this amazing event hugs wink

Thanks @complexbrains I added an image in the issue.

@PeerHerholz Awesome, thanks for pointing out. I somehow had missed this one. Will reach out.