Welcome to C-PAC’s Documentation!

NEW! Latest Release: C-PAC v1.2.0 (August 10, 2018)

New Features

  • Multivariate Distance Matrix Regression (MDMR). Exploratory, connectome-wide group-level analysis that allows researchers to explore relationships between patterns of functional connectivity and phenotypic variables. Compared to traditional univariate techniques which require rigorous correction for multiple comparisons, this multivariate approach significantly reduces the number of connectivity-phenotype comparisons needed for connectome-wide associations studies. See: A multivariate distance-based analytic framework for connectome-wide association studies.


  • Improved Command-Line Interface. C-PAC is now much easier to use through the command-line interface using the “cpac” CLI tool. Users can kick off individual and group-level analyses using a nested menu, generate new pipeline and data configuration files, and set up FSL FEAT model presets, all without using the Graphical User Interface. More details available here.
  • Increased Skull-Stripping Configurability. You can now modify the full range of parameters for both AFNI’s 3dSkullStrip and FSL’s BET for anatomical skull-stripping during preprocessing.
  • Default pipeline configuration. For those who don’t want the options, C-PAC can run as a turnkey system using parameter selections recommended by our team. More details available here.
  • Group-level Analysis Usability. Group-level analyses now also accept tab-separated (.tsv) files for phenotypic information. This allows users to seamlessly pull in the participants.tsv files which often accompany BIDS datasets.

Error Fixes

  • An error in v1.1.0 that was causing the QC pages to crash on SNR image generation in some pipeline runs has been fixed.

Coming Soon (Release 1.3 early Fall)

  • Bootstrap Analysis for Stable Clusters (BASC)
  • Inter-subject Correlation (ISC)
  • Independent Components Analysis (ICA)-based Denoising
  • More FSL Group-Level Analysis presets
  • Supervised learning

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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