Welcome to C-PAC’s Documentation!

NEW! Latest Release: C-PAC v1.1.0 (May 15, 2018)

New Features

  • The Visual Data Quality Control Interface is back! The QC interface provides HTML pages for each participant, scan, and preprocessing strategy featuring montage images of various preprocessing, analysis, and head motion images, graphs, and histograms. You can use these for a quick glance of your results. More details here.
  • FSL FEAT Group-Level Analysis Presets. A new addition to C-PAC’s group-level analysis model builder that allows you to setup group-level models specified in the FSL User Guide with little effort. The preset generator allows you to select from a few commonly-used FEAT model configurations. The first six model types are in and more to come! More details here.
  • Automated Anatomical Scan Selection (for Multisession datasets). If using a dataset that features multiple anatomical/structural scans per participant, you can now configure the data configuration builder to automatically select which anatomical file to use in your pre-processing run. More details here.

Improvements

  • Leaner and Cleaner Output Directories. The layout of the output directory has been made cleaner and easier to navigate. Many of the usual outputs written to the output directory by default are now optional, saving disk space as well. There are new options in the pipeline configuration enabling you to select which additional outputs should be included in the output directory. Again, see the User Guide for more information on this change. More details here.

General Remarks

  • The data configuration YAML file format has been modified to feature deeper nesting of functional-related files (such as scan parameter files or field map files). Note, data configuration files from versions prior to v1.1.0 will not work with C-PAC v1.1.0 or later - you can use any already-existing data settings YAML files to regenerate these. See the User Guide for more information, or feel free to contact us if any assistance is needed. More details here.

Error Fixes

  • The z-stat output files of group-level analysis are now labeled after the contrast names provided by the user during the group model creation process.

Coming Soon (Releases 1.2 and 1.3 this summer)

  • Multivariate Distance Matrix Regression (MDMR)
  • Bootstrap Analysis for Stable Cluster (BASC)
  • More FSL Group-Level Analysis presets
  • Expanded range of skull stripping options
  • Expanded nuisance regression options

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