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
Reproducibility
Comprehensive Array Functional Connectivity Analyses
High Performance Computing
Usability
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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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