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 1.8.1 Beta (Sep 17, 2021)

New Features & Pipeline Harmonization

  • ABCD-BIDS Pipeline Harmonization.

  • CCS Pipeline Harmonization.

    • A new BOLD masking option, CCS_Anatomical_Refined, has been added. This has been adapted from the BOLD mask method from the CCS pipeline.

    • New T1 masking options using Freesurfer have been added to facilitate the harmonization with the CCS pipeline.

    • A preconfigured pipeline that reproduces the CCS pipeline is now available.

  • fmriprep Pipeline Harmonization Updated.

    • The fmriprep-options preconfigured pipeline, which reproduces the fmriprep pipeline, has been updated to reflect recent changes.

Improvements

  • Further improvements to memory management for less frequent hanging have been integrated, in an ongoing process.

  • Freesurfer’s Recon-All functionality in C-PAC has been opened to further configurability.

  • More options for selecting a reference for slice-timing correction have been added.

  • Description and other meta-data fields have been added to some output JSON sidecar files in an ongoing process to make output data descriptors clearer.

  • DVARS calculation is now performed using AFNI 3dTto1d, increasing computation speed.

  • Manually inserting input data to a C-PAC output directory for ingress is now easier. A sidecar JSON containing meta-data will be produced for the data automatically if one is not provided.

  • For developers: improvements to the codebase have streamlined the process of adding new output data types to C-PAC through a central table, in line with recent v1.8 changes and the move to BIDS-Derivatives compliance.

Bug Fixes

  • Choosing On/Off for nuisance regression once again correctly produces the non-cleaned BOLD outputs in addition to any nuisance regression strategies.

  • Forking T1 and EPI based registration while simultaneously forking ANTs and FSL registration options no longer crashes the pipeline.

  • An error where the DVARS calculation step would cause the pipeline to hang has been resolved.

  • All nuisance regression choices are now available for EPI-registration based pipelines.

  • The mem_gb input parameter for memory allocation no longer overrides other memory selections, when left unspecified.

  • The selected_volume option for motion correction reference is now working again.

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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