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XCP-D: A Robust Pipeline for the post-processing of fMRI data.
Mehta, Kahini; Salo, Taylor; Madison, Thomas; Adebimpe, Azeez; Bassett, Danielle S; Bertolero, Max; Cieslak, Matthew; Covitz, Sydney; Houghton, Audrey; Keller, Arielle S; Luo, Audrey; Miranda-Dominguez, Oscar; Nelson, Steve M; Shafiei, Golia; Shanmugan, Sheila; Shinohara, Russell T; Sydnor, Valerie J; Feczko, Eric; Fair, Damien A; Satterthwaite, Theodore D.
Afiliación
  • Mehta K; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Salo T; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.
  • Madison T; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Adebimpe A; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Bassett DS; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.
  • Bertolero M; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Cieslak M; Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA.
  • Covitz S; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Houghton A; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.
  • Keller AS; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Luo A; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA.
  • Miranda-Dominguez O; Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA.
  • Nelson SM; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Shafiei G; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Shanmugan S; Santa Fe Institute, Santa Fe, NM, 87051, USA.
  • Shinohara RT; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Sydnor VJ; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.
  • Feczko E; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Fair DA; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Satterthwaite TD; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.
bioRxiv ; 2023 Nov 21.
Article en En | MEDLINE | ID: mdl-38045258
ABSTRACT
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 tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity 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, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos