Welcome to C-PAC’s Documentation!

Latest Release: C-PAC v1.3.0 (October 8, 2018)


  • MOVIE-FMRI ANALYSIS. Inter-Subject Correlation (ISC) and Inter-Subject Functional Connectivity (ISFC) (Simony et al., 2016). Implementation adapted from BRAINIAK (https://github.com/brainiak/brainiak). Dynamic reconfiguration of the default mode network during narrative comprehension. Erez Simony, Christopher J Honey, Janice Chen, Olga Lositsky, Yaara Yeshurun, Ami Wiesel & Uri Hasson. Nature Communications, 7(May 2015), 12141. (https://doi.org/10.1038/ncomms12141)
  • GRAPH GENERATION. Users can now generate functional connectivity matrices for any parcellation set using Pearson Correlation, Partial Correlation and Tangent Embedding. Implementation based on Dadi et al (2018). Dadi K., Rahim M., Abraham A., Chyzhyk D., Milham M., Thirion B., Varoquaux G. (2018) Benchmarking functional connectome-based predictive models for resting-state fMRI. <hal-01824205> https://hal.inria.fr/hal-01824205
  • BOOTSTRAP-BASED FUNCTIONAL CONNECTIVITY-BASED PARCELLATION. Users can now seamlessly run Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2008, 2010) on data preprocessed by C-PAC. This is accomplished using PyBASC - a Python implementation of BASC, by Aki Nikolaidis - which is now integrated in C-PAC. Bellec, P., Marrelec, G., & Benali, H. (2008). A bootstrap test to investigate changes in brain connectivity for functional MRI. Statistica Sinica, 1253-1268. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neuroimage, 51(3), 1126-1139. Garcia-Garcia, M., Nikolaidis, A., Bellec, P., Craddock, R. C., Cheung, B., Castellanos, F. X., & Milham, M. P. (2017). Detecting stable individual differences in the functional organization of the human basal ganglia. NeuroImage. (https://github.com/AkiNikolaidis/PyBASC)
  • ICA-AROMA. Robust de-noising using the ICA-AROMA implementation of Independent Components Analysis for the removal of motion artifacts, as implemented by Maarten Mennes. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. Neuroimage. 2015 May 15;112:267-277. (https://github.com/maartenmennes/ICA-AROMA)
  • CUSTOM BRAIN EXTRACTION MASKS. You can now provide brain masks created with your own preferred skull-stripping method.


  • AWS S3 links can now be provided for all ROI and mask inputs in the pipeline configuration. This makes it easier to kick off runs without needing to gather or transfer ROI and mask files to a local disk.
  • C-PAC is now compatible with Nipype version 1.1.2 (latest).


  • AWS S3 bucket credentials fixed to allow for anonymous connections.
  • An error causing the Visual Quality Control interface to print warnings has been fixed.
  • An error where the FSL FEAT Model Preset GUI dialogs would sometimes clear fields prematurely has been fixed.

COMING SOON (Releases 1.4 and 1.5 this winter)

  • ICA
  • FSL Randomise
  • Supervised Learning
  • A new Graphical User Interface (GUI) Upgrade

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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