xcp_d

Post-processing of fMRIPrep, NiBabies, and HCP outputs

https://github.com/pennlinc/xcp_d

Scientific Fields

Repository

Post-processing of fMRIPrep, NiBabies, and HCP outputs

README.rst

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

.. image:: https://img.shields.io/badge/Source%20Code-pennlinc%2Fxcp__d-purple
   :target: https://github.com/PennLINC/xcp_d
   :alt: GitHub Repository

.. image:: https://readthedocs.org/projects/xcp-d/badge/?version=latest
   :target: http://xcp-d.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation

.. image:: https://img.shields.io/badge/docker-pennlinc/xcp_d-brightgreen.svg?logo=docker&style=flat
   :target: https://hub.docker.com/r/pennlinc/xcp_d/tags/
   :alt: Docker

.. image:: https://circleci.com/gh/PennLINC/xcp_d.svg?style=svg
   :target: https://circleci.com/gh/PennLINC/xcp_d
   :alt: Test Status

.. image:: https://codecov.io/gh/PennLINC/xcp_d/branch/main/graph/badge.svg
   :target: https://codecov.io/gh/PennLINC/xcp_d
   :alt: Codecov

.. image:: https://zenodo.org/badge/309485627.svg
   :target: https://zenodo.org/badge/latestdoi/309485627
   :alt: DOI

.. image:: https://img.shields.io/github/license/pennlinc/xcp_d
   :target: https://opensource.org/licenses/BSD-3-Clause
   :alt: License

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.


*****
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 `_ 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 `_,
`ABCD-BIDS `_,
`Minimally preprocessed HCP `_,
and `UK Biobank `_ data.

.. important::

   Please note that *XCP-D* is only compatible with HCP-YA data released around February 2023.
   This is because the HCP-YA data was not versioned at the time of release,
   and we have to pin to a specific release date.
   We cannot guarantee that *XCP-D* will work with other versions of the HCP-YA data,
   or with data from other HCP projects.

.. image:: https://raw.githubusercontent.com/pennlinc/xcp_d/main/docs/_static/xcp_figure_1.png

See the `documentation `_ 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 including 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 citation, please cite the following paper:

   Mehta, K., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M., ... & Satterthwaite, T. D.
   (2024).
   XCP-D: A Robust Pipeline for the post-processing of fMRI data.
   *Imaging Neuroscience*, 2, 1-26.
   doi:10.1162/imag_a_00257.

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

Owner

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  XCP-D : A Robust Postprocessing Pipeline of fMRI
  data
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Taylor
    family-names: Salo
    email: salot@pennmedicine.upenn.edu
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0001-9813-3167'
  - given-names: Kahini
    family-names: Mehta
    email: kahini.mehta@pennmedicine.upenn.edu
    affiliation: Columbia University
    orcid: 'https://orcid.org/0000-0001-9466-100X'
  - given-names: Azeez
    family-names: Adebimpe
    orcid: 'https://orcid.org/0000-0001-9049-0135'
  - given-names: Maxwell
    family-names: Bertolero
    orcid: 'https://orcid.org/0000-0002-2691-3698'
    affiliation: Turing Medical
  - given-names: Kristin
    family-names: Murtha
    affiliation: University of Pennsylvania
  - given-names: Matthew
    family-names: Cieslak
    email: matthew.cieslak@pennmedicine.upenn.edu
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0002-1931-4734'
  - given-names: Steven
    family-names: Meisler
    orcid: 'https://orcid.org/0000-0002-8888-1572'
    affiliation: University of Pennsylvania
  - given-names: Thomas
    family-names: Madison
    affiliation: University of Minnesota
    orcid: 'https://orcid.org/0000-0003-3030-6580'
  - given-names: Valerie
    family-names: Sydnor
    affiliation: University of Pittsburgh Medical Center
    orcid: 'https://orcid.org/0000-0002-8640-668X'
  - given-names: Sydney
    family-names: Covitz
    affiliation: Stanford University
    orcid: 'https://orcid.org/0000-0002-7430-4125'
  - given-names: Roey
    family-names: Schurr
    affiliation: Hebrew University of Jerusalem
    orcid: 'https://orcid.org/0000-0002-5325-2071'
  - given-names: Damien
    family-names: Fair
    affiliation: University of Minnesota
    orcid: 'https://orcid.org/0000-0001-8602-393X'
  - given-names: Theodore
    family-names: Satterthwaite
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0001-7072-9399'
identifiers:
  - type: doi
    value: 10.5281/zenodo.5139942
    description: The Zenodo DOI
repository-code: 'https://github.com/PennLINC/xcp_d'
url: 'https://xcp-d.readthedocs.io'
abstract: >-
  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 from the scanner to functional
  connectivity matrices.
keywords:
  - fMRI
  - BIDS-App
  - Neuroimaging
license: BSD-3-Clause
version: 0.11.1
date-released: '2025-07-25'

Issues and Pull Requests

Last synced: 6 months ago