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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 J Madison4

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

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary

XCP-D offers standardized post-processing for functional neuroimaging data. This tool ensures reproducible analysis by generating denoised BOLD images and derivatives compatible with various preprocessing pipelines.

Keywords:
fMRIfunctional connectivitypost-processingresting-statesoftware

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Data Science

Background:

  • Functional neuroimaging relies on pre-processing pipelines for standardized data.
  • Post-processing of neuroimaging data lacks standardization, hindering reproducibility.
  • Existing tools may not support diverse pre-processing outputs or adhere to data organization standards like BIDS.

Purpose of the Study:

  • Introduce XCP-D, a novel post-processing pipeline for functional neuroimaging.
  • Address the lack of standardization in fMRI data post-processing.
  • Facilitate robust, scalable, and reproducible fMRI data analysis.

Main Methods:

  • Developed XCP-D through a collaborative effort using an open development model on GitHub.
  • Distributed XCP-D as a Docker container or Apptainer image for accessibility.
  • Implemented continuous integration testing for quality assurance.

Main Results:

  • XCP-D generates denoised BOLD images and functional derivatives from resting-state fMRI data.
  • The pipeline supports NIfTI or CIFTI file formats.
  • XCP-D is compatible with data pre-processed using fMRIPrep, HCP, or ABCD-BIDS pipelines.
  • Achieved over 5,000 downloads prior to official release, indicating high demand.

Conclusions:

  • XCP-D provides a standardized, reproducible solution for fMRI data post-processing.
  • Enhances the utility of functional neuroimaging data for diverse research applications.
  • Promotes interoperability and data sharing within the neuroscience community.