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XCP-D:一种用于功能磁共振成像(fMRI)数据后处理的强大流程。

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

作者信息

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

机构信息

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

Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, United States.

出版信息

Imaging Neurosci (Camb). 2024 Aug 13;2. doi: 10.1162/imag_a_00257. eCollection 2024.

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 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.

摘要

功能神经影像学是神经科学研究的重要工具。预处理管道生成标准化的、经过最少预处理的数据,以支持一系列潜在分析。然而,后处理并没有类似的标准化。虽然存在几种后处理选项,但它们可能不支持来自不同预处理管道的输出,可能文档有限,并且可能不遵循普遍接受的数据组织标准(例如,脑成像数据结构(BIDS))。作为回应,我们推出了XCP-D:宾夕法尼亚大学的PennLINC和明尼苏达大学的DCAN实验室的合作成果。XCP-D在GitHub上采用开放开发模型,并纳入持续集成测试;它以Docker容器或Apptainer镜像的形式分发。XCP-D在使用fMRIPrep、HCP或ABCD-BIDS管道进行预处理后,从NIfTI或CIFTI文件中的静息态数据生成去噪后的BOLD图像和功能导数。甚至在正式发布之前,XCP-D已经从DockerHub下载了超过5000次。总之,XCP-D促进了功能磁共振成像数据的强大、可扩展和可重复的后处理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/613d/12288603/ec0371919633/imag_a_00257_fig1.jpg

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