######################################################### XCP-D : A Robust Postprocessing Pipeline of fMRI data #########################################################

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This fMRI post-processing and noise regression pipeline is developed by the Satterthwaite lab at the University of Pennslyvania <https://www.satterthwaitelab.com/> (XCP\; eXtensible Connectivity Pipeline) and Developmental Cognition and Neuroimaging lab at the University of Minnesota <https://innovation.umn.edu/developmental-cognition-and-neuroimaging-lab/> (-D\CAN) for open-source software distribution.

About

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 functional connectivity matrices.

XCP-D picks up right where fMRIprep <https://fmriprep.org> ends, directly consuming the outputs of fMRIPrep. XCP-D leverages the BIDS and NiPreps 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 <https://nibabies.readthedocs.io>, ABCD-BIDS <https://github.com/DCAN-Labs/abcd-hcp-pipeline>, Minimally preprocessed HCP <https://www.humanconnectome.org/study/hcp-lifespan-development/\ data-releases>, and UK Biobank <https://doi.org/10.1016/j.neuroimage.2017.10.034>_ data.

Please note that XCP is only compatible with HCP-YA versions downloaded c.a. Feb 2023 at the moment.

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See the documentation <https://xcp-d.readthedocs.io/en/latest/>_ for more details.

Why you should use XCP-D

XCP-D produces the following commonly-used outputs: matrices, parcellated time series,
dense time series, and additional QC measures.

XCP-D is designed for resting-state or pseudo-resting-state functional connectivity analyses.
XCP-D derivatives may be useful for seed-to-voxel and ROI-to-ROI functional connectivity analyses,
as well as decomposition-based methods, such as ICA or NMF.

When you should not use XCP-D

XCP-D is not designed as a general-purpose postprocessing pipeline. It is really only appropriate for certain analyses, and other postprocessing/analysis tools are better suited for many types of data/analysis.

XCP-D derivatives are not particularly useful for task-dependent functional connectivity analyses, such as psychophysiological interactions (PPIs) or beta series analyses. It is also not suitable for general task-based analyses, such as standard task GLMs, as we recommend included nuisance regressors in the GLM step, rather than denoising data prior to the GLM.

Citing XCP-D

If you use XCP-D in your research, please use the boilerplate generated by the workflow. If you need an immediate citations, please cite the following preprint:

Mehta, K., Salo, T., Madison, T., Adebimpe, A., Bassett, D. S., Bertolero, M., ... & Satterthwaite, T. D. (2023). XCP-D: A Robust Pipeline for the post-processing of fMRI data. bioRxiv. doi:10.1101/2023.11.20.567926.

Please also cite the Zenodo DOI for the version you're referencing.