Welcome to C-PAC’s Documentation!

Latest Release: C-PAC v1.4.1 (March 13, 2019)


  • 36-Parameter Confound Regression Model. A new nuisance regression option has been introduced into C-PAC for confound regression using whole-brain motion parameters.

Satterthwaite TD, Elliott MA, Gerraty RT, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage. 2012;64:240-56. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3811142/)

  • tCompCor: Temporal Standard Deviation Noise ROI Component-Based Noise Correction. tCompCor has also been introduced into C-PAC as a nuisance regression option, for the removal of physiological noise from the functional time series.

Yashar Behzadi, Khaled Restom, Joy Liau, Thomas T. Liu. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37(1):90-101. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2214855/)

  • Linear anatomical registration. You can now run linear-only registration-to-template using FSL FLIRT. This allows a much faster processing time for when very high-quality nonlinear anatomical registration is not as important for your analysis.


  • ndmg Mode. With ndmg-mode enabled, C-PAC runs a leaner preprocessing pipeline and produces connectome graphs using the pipeline configuration originally selected by the ndmg team and Neurodata’s pre-selected collection of atlases.



  • Nuisance Regression Expansion. Along with the new addition of the 36-parameter motion model and tCompCor, the already-existing nuisance regression options have been expanded to include greater degrees of configurability. Refer to our updated User Guide for more details.


  • Fixed an error where C-PAC would not write outputs to an AWS S3 bucket when configured to do so.
  • Fixed the “thresh_and_sum” error in the Singularity container that would cause the workflow run to fail.

COMING SOON (v1.4.2 & v1.5.0 - Spring 2019)

  • Quasi-Periodic Patterns (QPP) template generation and regression
  • New Group-Level Model Builder GUI
  • Predictive Eye Estimation Regression (PEER)
  • Non-human primate pipeline optimization
  • Easy integration & analysis of other preprocessing pipeline results

Latest Release: C-PAC v1.4.0 (February 4, 2019)


  • Quick Start Guide. By pulling our Docker or Singularity container, you can kick off C-PAC with your dataset in minutes, without any prior package or library installations other than Docker or Singularity. More info available on our User Guide.


  • TURNKEY MODE. For users who prefer not to make decisions regarding their pipeline, C-PAC now includes a pre-configured default pipeline that includes the most commonly used decisions. The pre-configured pipeline selections are described in the Quick-start guide under “Running Turnkey Mode”.
  • Nonparametric Permutation Inference. FMRIB’s FSL Randomise has been integrated into C-PAC’s suite of group-level analyses. You can use the already-existing FSL group-level presets or the group model builder to specify your model.


  • Early Access to the new C-PAC GUI. The first part of C-PAC’s new graphical user interface (GUI) for generating and editing custom pipelines is available! All are encouraged to take a quick test-drive of the pipeline builder and let us know your thoughts. All feedback welcome on our forum.
  • Group Model-Building Modularity. As part of an ongoing process of improving usability, C-PAC’s group-level analysis model builder now offers more control over your model design. It is now easier to review changes to your design matrix before specifying contrasts.


  • An error preventing users from running only anatomical preprocessing has been fixed.
  • An error in the Unpaired Two-Group Difference preset of the FSL Group Model Presets, which was causing certain covariate labels to occasionally be formatted improperly, has been fixed.

COMING SOON (Release 1.5 early 2019)

  • More denoising options
  • Quasi-periodic pattern (QPP) identification
  • New Graphical User Interface (GUI) Upgrade
  • Further modularity & usability improvements

In addition, the C-PAC Docker image and AWS AMI have both been updated. These provide a quick way to get started without needing to go through the install process.

And as always, you can contact us here for user support and discussion:


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:

The C-PAC Team

Primary Development Team:
Michael Milham (Founder, Co-Principal Investigator)
Cameron Craddock (Co-Principal Investigator)
Steven Giavasis (Lead Developer)
Nanditha Rajamani (Developer)
Anibal Solon Heinsfeld (Developer)

Project Alumni:
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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