We are happy to announce the beta release of The Configurable Pipeline for the Analysis of Connectomes (C-PAC version 1.0.1; http://fcp-indi.github.io/). Built in Python using Nipype, C-PAC is an open-source software pipeline for automated preprocessing and analysis of resting-state fMRI data. C-PAC builds upon a robust set of existing software packages including AFNI, FSL, and ANTS, and makes it easy for both novice users and experts to explore their data using a wide array of analytic tools. Users define analysis pipelines by specifying a combination of preprocessing options and analyses to be run on an arbitrary number of subjects. Results can then be compared across groups using the integrated group statistics feature.

 

More than just a pipeline for preprocessing data, C-PAC provides an end-to-end functional connectomes software that also includes calculating many common resting-state derivatives and group level analyses:

 

Configurability

• C-PAC provides the ability to efficiently execute a range of pipelines with various configurations and parameters in parallel
• Pipeline details are easily specified using a graphical user interface, which produces a human readable pipeline configuration file that can be shared to replicate an analysis on different data or at a different site
• Can access data in the BIDS raw data format and a variety other file structures using easy customization, even from S3
• An intelligent pipeline builder enables the pipeline to be after a configuration change and only recomputes the affected files
• Group-level statistical analysis can be easily specified and run on all generated derivatives

 

Reproducibility

• C-PAC design embodies best practice software design principles to ensure stability between versions
• C-PAC users can readily share pipeline configuration files as a means of facilitating replication of their analyses by others
• Container-based distribution of C-PAC allows others to reproduce an user’s installation in its entirety, independent of platform
• C-PAC is ideal for pipeline comparison of evaluation due to its ability to launch a plurality of pipelines differing on one or more desired parameters/processing steps

 

Comprehensive Array Functional Connectivity Analyses

• amplitude of low frequency fluctuations
• fractional amplitude of low frequency fluctuations
• dual regression to user specified templates
• local functional connectivity density
• degree and eigenvector network centrality
• regional homogeneity
• seed-based correlation analysis using user specified seeds
• voxel-matched homotopic connectivity

 

High Performance Computing

• Resource aware scheduler maximizes parallel execution
• Support for a range of cluster technologies (e.g., SGE, PBS, SLURM)
• C-PAC is now available as Docker or Singularity containers making it easier to install and improving reproduciblity

 

Usability

• Extensive user guide, support and education pages
• Dedicated support staff answers questions on support forum and can provide hands-on demonstrations

 

 

-- 

Cameron Craddock, PhD

 

Director of Imaging, Center for the Developing Brain

Child Mind Institute

childmind.org

445 Park Avenue (entrance on 56th Street)

New York, NY 10022

 

Director, Computational Neuroimaging Laboratory

Nathan S. Kline Institute for Psychiatric Research

www.rfmh.org/nki

1040 Old Orangeburg Road

Orangeburg, NY, 10962

 

Research Professor, Department of Computer Science and Engineering

NYU Tandon School of Engineering

engineering.nyu.edu

Two Metrotech Tower, 10th Floor

Brooklyn, NY 11201

 

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