This fMRI post-processing and noise regression pipeline is developed by the Satterthwaite lab at the University of Pennslyvania ( XCP ; eXtensible Connectivity Pipeline) and Developmental Cognition and Neuroimaging lab at the University of Minnesota ( -D CAN) for open-source software distribution.
XCP-D paves the final section of the reproducible and scalable route from the MRI scanner to functional connectivity data in the hands of neuroscientists. We developed XCP-D to extend the BIDS and NiPrep apparatus to the point where data is most commonly consumed and analyzed by neuroscientists studying functional connectivity. Thus, with the development of XCP-D, data can be automatically preprocessed and analyzed in BIDS format, using NiPrep-style containerized code, all the way from the scanner to functi
XCP-D picks up right where fMRIprep ends, directly consuming the outputs of fMRIPrep. XCP-D leverages the BIDS and NiPrep frameworks to automatically generate denoised BOLD images, parcellated time series, functional connectivity matrices, and quality assessment reports. XCP-D can also process outputs from: NiBabies and Minimally preprocessed HCP data .