Welcome to C-PAC’s Documentation!

Latest Release: C-PAC v1.4.3 (May 20, 2019)

NEW FEATURES

  • Quasi-Periodic Pattern (QPP) template generation. QPPs are spatial patterns of dynamic connectivity that may be useful in illustrating differences present in neurological or psychiatric disorders. C-PAC can now easily calculate a QPP template derived from a set of functional data.

Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Majeed W, Magnuson M, Hasenkamp W, Schwarb H, Schumacher EH, Barsalou L, Keilholz SD. Neuroimage. 2011 Jan 15;54(2):1140-50.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity. Thompson GJ, Pan WJ, Magnuson ME, Jaeger D, Keilholz SD. Neuroimage. 2014 Jan 1;84:1018-31.

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal. Yousefi B, Shin J, Schumacher EH, Keilholz SD. Neuroimage. 2018 Feb 15;167:297-308.

  • Custom regressors for nuisance regression. Users can now provide custom regressors to C-PAC’s nuisance regression suite. This could allow a user to regress out any time series from the data, such as physiological noise regressors, task regressors, or QPP time series prior to individual-level analysis. Refer to the updated User Guide for more information.
  • Lesion masking for anatomical registration. For ANTs registration pipelines, users can now provide a lesion mask to improve anatomical registration quality in participant data containing lesions. Refer to the updated User Guide for more information. [http://stnava.github.io/ANTs/]

ERROR FIXES

  • Fixed a bug where the working directory would not be deleted (when configured to do so) if the pipeline did not complete successfully.
  • Fixed a bug where, in some cases, working directory settings in a custom pipeline would be over-ridden by defaults.

COMING SOON (v1.4.4/v1.5.0 - Summer 2019)

  • New Group-Level Model Builder GUI
  • Predictive Eye Estimation Regression (PEER)
  • Non-human primate pipeline optimization
  • Easy integration & analysis of other preprocessing pipeline results

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:

https://groups.google.com/forum/#!forum/cpax_forum

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).

User Guide Index