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:

Neuroparc v1.0: Baseline

On July 20, 2020, Neuroparc released v1.0. In moving from v0 to v1, the paths to several of the Neuroparc atlases changed. The atlases are used by C-PAC in its default and preconfigured pipelines. C-PAC v1.7.0 includes the Neuroparc v1.0 paths, but if you are using a pipeline based on C-PAC 1.6.2a or older, you will need to update any Nueroparc v0 paths in your config

All paths begin with /ndmg_atlases/label/Human/

Neuroparc v0

Neuroparc v1

aal_space-MNI152NLin6_res-1x1x1.nii.gz

AAL_space-MNI152NLin6_res-1x1x1.nii.gz

aal_space-MNI152NLin6_res-2x2x2.nii.gz

AAL_space-MNI152NLin6_res-2x2x2.nii.gz

AAL2zourioMazoyer2002.nii.gz

AAL_space-MNI152NLin6_res-1x1x1.nii.gz

brodmann_space-MNI152NLin6_res-1x1x1.nii.gz

Brodmann_space-MNI152NLin6_res-1x1x1.nii.gz

brodmann_space-MNI152NLin6_res-2x2x2.nii.gz

Brodmann_space-MNI152NLin6_res-2x2x2.nii.gz

CorticalAreaParcellationfromRestingStateCorrelationsGordon2014.nii.gz

CAPRSC_space-MNI152NLin6_res-1x1x1.nii.gz

desikan_space-MNI152NLin6_res-1x1x1.nii.gz

Desikan_space-MNI152NLin6_res-1x1x1.nii.gz

desikan_space-MNI152NLin6_res-2x2x2.nii.gz

Desikan_space-MNI152NLin6_res-2x2x2.nii.gz

DesikanKlein2012.nii.gz

DesikanKlein_space-MNI152NLin6_res-1x1x1.nii.gz

glasser_space-MNI152NLin6_res-1x1x1.nii.gz

Glasser_space-MNI152NLin6_res-1x1x1.nii.gz

Juelichgmthr252mmEickhoff2005.nii.gz

Juelich_space-MNI152NLin6_res-1x1x1.nii.gz

MICCAI2012MultiAtlasLabelingWorkshopandChallengeNeuromorphometrics.nii.gz

MICCAI_space-MNI152NLin6_res-1x1x1.nii.gz

princetonvisual-top_space-MNI152NLin6_res-2x2x2.nii.gz

Princetonvisual-top_space-MNI152NLin6_res-2x2x2.nii.gz

Schaefer2018-200-node_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer200_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer2018-300-node_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer300_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer2018-400-node_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer400_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer2018-1000-node_space-MNI152NLin6_res-1x1x1.nii.gz

Schaefer1000_space-MNI152NLin6_res-1x1x1.nii.gz

slab907_space-MNI152NLin6_res-1x1x1.nii.gz

Slab907_space-MNI152NLin6_res-1x1x1.nii.gz

yeo-7_space-MNI152NLin6_res-1x1x1.nii.gz

Yeo-7_space-MNI152NLin6_res-1x1x1.nii.gz

yeo-7-liberal_space-MNI152NLin6_res-1x1x1.nii.gz

Yeo-7-liberal_space-MNI152NLin6_res-1x1x1.nii.gz

yeo-17_space-MNI152NLin6_res-1x1x1.nii.gz

Yeo-17_space-MNI152NLin6_res-1x1x1.nii.gz

yeo-17-liberal_space-MNI152NLin6_res-1x1x1.nii.gz

Yeo-17-liberal_space-MNI152NLin6_res-1x1x1.nii.gz

Latest Release: Version 1.8.0 Beta (Mar 13, 2021)

New Features

  • BIDS-Derivatives Compatibility. C-PAC can now both ingress data and write data out according to the BIDS-Derivatives standard, enhancing interoperability with other BIDS-compatible neuroimaging packages.

    • This allows users to inject already-processed data directly into key intermediate points of the C-PAC pipeline.

    • In addition, clearer information on provenance of pipeline steps for multiple C-PAC strategies are also provided in BIDS-compatible JSON files for all output data.

    • The implementation of BIDS-Derivatives compatibility and its use flow regarding interoperability with other neuroimaging packages will continue to grow and adapt throughout later releases; stay tuned for changes and upgrades.

    • [https://bids-specification.readthedocs.io/en/latest/05-derivatives/01-introduction.html]

  • Freesurfer Integration for Surface-Based Processing. The C-PAC pipeline can now run Freesurfer to generate surfaces, and to produce brain masks or tissue segmentation masks for use in the voxel-based pipeline, if the user desires.

    • Note: Due to the size of the Freesurfer installation, a separate, smaller C-PAC container without Freesurfer is also available if users wish to bypass surface-based analysis.

    • [https://surfer.nmr.mgh.harvard.edu/]

New Features - Pipeline Harmonization

Improvements

  • Memory Usage Optimization. C-PAC ’ s memory usage estimates have been improved - this should reduce the occurrence of pipeline crashes caused by exceeding the memory limits of shared computing resources.

  • Cleaner Pipeline Configuration. The pipeline configuration YAML format has been upgraded to a cleaner, easier-to-read nested format.

  • Pipeline Configuration Importing. If configuring your pipeline directly through a YAML file, C-PAC ’ s pre-configured pipelines can now be imported into a YAML file. This allows users to specify only small segments of the pipeline they wish to change, when basing off of a default or pre-configured pipeline.

Developer Improvements

  • Modularized Pipeline Blocks. C-PAC’s workflow segments have been modularized for easier development and methods integration, and for easier testing.

Bug Fixes

  • An error sometimes preventing users from supplying custom nuisance regressors to C-PAC has been resolved.

In addition, the C-PAC Docker and Singularity images, as well as the AWS AMI, have all been updated. These provide a quick way to get started.

And as always, you can contact us here for user support and discussion: https://groups.google.com/forum/#!forum/cpax_forum

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)
Hecheng Jin (Developer)
Xinhui Li (Developer)

Project Alumni:
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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