User Documentation

Welcome to C-PAC’s user guide!

The C-PAC Mission

Once a distant goal, discovery science for the human connectome is now a reality. Researchers who previously struggled to obtain neuroimaging data from 20-30 participants are now exploring the functional connectome using data acquired from thousands of participants, made publicly available through the 1000 Functional Connectomes Project and the International Neuroimaging Data-sharing Initiative (INDI). However, in addition to access to data, scientists need access to tools that will facilitate data exploration. Such tools are particularly important for those who are inexperienced with the nuances of fMRI image analysis, or those who lack the programming support necessary for handling and analyzing large-scale datasets.

The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the Nipype platform to users in a plug and play fashion—without requiring the ability to program. Using an easy to read, text-editable configuration file or a graphical user interface, C-PAC users can rapidly orchestrate automated R-fMRI processing procedures, including:

Importantly, C-PAC makes it possible to use a single configuration file to launch a factorial number of pipelines differing with respect to specific processing steps (e.g., spatial/temporal filter settings, global correction strategies, motion correction strategies, group analysis models). Additional noteworthy features include the ability to easily:

  • customize C-PAC to handle any systematic directory organization

  • specify Nipype distributed processing settings

C-PAC maintains key Nipype strengths, including the ability to:

  • interface with different software packages (e.g., FSL, AFNI, ANTS)

  • protect against redundant computation and/or storage

  • automatically carry out input checking, bug tracking and reporting

Future updates will include more configurability, advanced analytic features (e.g., support vector machines, cluster analysis) and diffusion tensor imaging (DTI) capabilities.

For more information and additional tutorials, check out our YouTube channel, as well as slides from our previous presentations:

Latest Release: Version 1.8.3 Beta (Feb 11, 2022)

New features

  • Added XCP-style quality control file

  • Added RBC-options pipeline preconfiguration

  • Added engine.log (when verbose debugging is on)

  • Added ability to fix random seed for

    • antsAI

    • antsRegistration

    • Atropos (fixed but not specified)

    • fslmaths

    • mri_vol2vol

    • recon-all

  • Added ability to use lateral ventricles mask in place of cerebrospinal fluid mask when when segmentation is Off, specifically for the rodent pipeline, but works on any dataset when segmentation is off

Improvements

  • In a given pipeline configuration, segmentation probability maps and binary tissue masks are warped to template space, and those warped masks are included in the output directory

    • if registration_workflows['functional_registration']['EPI_registration']['run segmentation'] is On and segmentation['tissue_segmentation']['Template_Based']['template_for_segmentation'] includes EPI_Template

      and/or

    • if registration_workflows['anatomical_registration']['run'] is On and segmentation['tissue_segmentation']['Template_Based']['template_for_segmentation'] includes T1_Template

  • Renamed connectivity matrices from *_connectome.tsv to *_correlations.tsv

  • Moved some ephemeral logging statements into pypeline.log

Bug fixes

  • Fixed bug in which working connectivity matrix filepaths were generated incorrectly, preventing generating matrices depending on container bindings

  • Fixed broken links in README

  • Fixed bug in which anatomical-only configurations required functional data directories

  • Fixed bug in which nuisance regressors would crash when segmentation is off and no CSF mask is provided

The C-PAC Team

Primary Development Team:
Michael Milham (Founder, Co-Principal Investigator)
Cameron Craddock (Co-Principal Investigator)
Steven Giavasis (Lead Developer)
Jon Clucas (Developer)
Teresa George (Developer)
Amy Gutierrez (Developer)

Project Alumni:
Hecheng Jin
Xinhui Li
Anibal Solon Heinsfeld
Nanditha Rajamani
Alison Walensky
David O’Connor
Carol Froehlich
John Pellman
Amalia MacDonald
Daniel Clark
Rosalia Tungaraza
Daniel Lurie
Zarrar Shehzad
Krishna Somandepali
Aimi Watanabe
Qingyang Li
Ranjit Khanuja
Sharad Sikka
Brian Cheung

Other Contributors:
Ivan J. Roijals-Miras (Google Summer of Code)
Florian Gesser (Google Summer of Code)
Asier Erramuzpe (Google Summer of Code)
Chao-Gan Yan
Joshua Vogelstein
Adriana Di Martino
F. Xavier Castellanos
Sebastian Urchs
Bharat Biswal

Funding Acknowledgements

Primary support for the work by Michael P. Milham, Cameron Craddock and the INDI team was provided by gifts from Joseph P. Healey and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by NIMH awards to Dr. Milham (R03MH096321) and F.X. Castellanos (R01MH083246).

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