fMRIPrep-Split into separate fit & apply workflows

[esavary]

Title

fMRIPrep-Split into separate fit & apply workflows

Short description and the goals for the OHBM BrainHack

fMRIPrep is a large, computationally intensive workflow that can potentially produce an order of magnitude more data than is input. However, many of these derivatives can be deterministically generated by a fairly small subset of "first order" derivatives, such as registration transforms.

This project aims to create a workflow that generates the minimal set of derivatives, as well as workflows for generating reports and other derivatives, such as resampled BOLD series and confound time series. The desired end result is a collection of modular workflows that can be composed either to produce the minimal set of derivatives or the full set that are the current outputs of fMRIPrep.

Link to the Project

https://github.com/nipreps/fmriprep/

Image for the OHBM brainhack website

No response

Project lead

Chris Markiewicz, GitHub: @effigies, discord: Chris Markiewicz (gh: effigies)

Main Hub

Montreal

Other Hub covered by the leaders

• Montreal
• Asia / Pacific
• Europe / Middle East / Africa
• Americas

Skills

While we encourage participants of all skill levels to join, a good understanding of Python is recommended to contribute to this project.

Recommended tutorials for new contributors

• Set up a brainhack friendly computing environment
• A introduction to Bash
• Python: Writing a script
• Python: Data analysis with Python
• Python: Visualisation
• Machine learning basics
• BIDS
• Machine learning for neuroimaging
• VCS: Using Git and Github
• VCS: Data management with Datalad
• Virtualization
• High Performance Computing: Oracle cloud resource

Good first issues

No response

Twitter summary

No response

Short name for the Discord chat channel (~15 chars)

fmriprep_fit_apply

Please read and follow the OHBM Code of Conduct

• I agree to follow the OHBM Code of Conduct during the hackathon