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Related Experiment Video

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XCP-D: A Robust Pipeline for the post-processing of fMRI data.

Kahini Mehta1,2,3, Taylor Salo1,2,3, Thomas Madison4

  • 1Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Biorxiv : the Preprint Server for Biology
|December 4, 2023
PubMed
Summary

XCP-D offers a standardized solution for post-processing functional neuroimaging data. This tool ensures reproducible analysis across various preprocessing pipelines, enhancing functional MRI research.

Keywords:
denoisingfMRIfunctional connectivityimage processingpost-processingreproducibilityresting-statesoftware

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Area of Science:

  • Neuroscience
  • Medical Imaging

Background:

  • Functional neuroimaging is crucial for neuroscience research.
  • Standardized pre-processing pipelines exist, but post-processing lacks standardization.
  • Existing post-processing tools have limitations in supporting diverse pipelines and BIDS best practices.

Approach:

  • XCP-D is a novel, collaborative post-processing tool developed by PennLINC and the DCAN lab.
  • It utilizes an open development model on GitHub with continuous integration testing.
  • XCP-D is distributed as a Docker container or Singularity image for accessibility.

Key Points:

  • Generates denoised BOLD images and functional derivatives from resting-state fMRI data.
  • Supports NifTI and CIFTI file formats.
  • Compatible with outputs from fMRIPrep, HCP, and ABCD-BIDS pre-processing pipelines.

Conclusions:

  • XCP-D addresses the lack of standardization in fMRI data post-processing.
  • Facilitates robust, scalable, and reproducible neuroimaging analysis.
  • Already adopted by researchers, indicating its utility and demand.