XCP-D: A robust pipeline for the post-processing of fMRI data

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they...

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Veröffentlicht in:Imaging neuroscience (Cambridge, Mass.) Mass.), 2024-08, Vol.2, p.1-26
Hauptverfasser: Mehta, Kahini, Salo, Taylor, Madison, Thomas J., Adebimpe, Azeez, Bassett, Danielle S., Bertolero, Max, Cieslak, Matthew, Covitz, Sydney, Houghton, Audrey, Keller, Arielle S., Lundquist, Jacob T., Luo, Audrey, Miranda-Dominguez, Oscar, Nelson, Steve M., Shafiei, Golia, Shanmugan, Sheila, Shinohara, Russell T., Smyser, Christopher D., Sydnor, Valerie J., Weldon, Kimberly B., Feczko, Eric, Fair, Damien A., Satterthwaite, Theodore D.
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Sprache:eng
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Zusammenfassung:Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they may not support output from different pre-processing pipelines, may have limited documentation, and may not follow generally accepted data organization standards (e.g., Brain Imaging Data Structure (BIDS)). In response, we present XCP-D: a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University of Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Apptainer image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NIfTI or CIFTI files following pre-processing with fMRIPrep, HCP, or ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >5,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.
ISSN:2837-6056
2837-6056
DOI:10.1162/imag_a_00257