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