4/29/13 Frontiers | Manage Abstracts | My Submissions | All Abstracts www.frontiersin.org/Journal/MySubmissionViewDetails.aspx?stage=100&articleid=54810&submissionid=54913 1/2 Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-­PAC) Cameron Craddock1, 2*, Sharad Sikka2, 3, Brian Cheung1, Ranjeet Khanuja1, Satrajit S. Ghosh4, Chaogan Yan2, Qingyang Li1, Daniel Lurie1, Joshua Vogelstein1, 5, Randal Burns6, Stanley Colcombe2, Maarten Mennes7, Clare Kelly3, Adriana Di Martino3, Francisco X. Castellanos3 and Michael Milham1, 2* 1 Center for the Developing Brain, Child Mind Institute, USA 2 Nathan Kline Institute for Psychiatric Research, USA 3 Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, USA 4 McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA 5 Departments of Statistical Sciences, Duke University, USA 6 Department of Computer Science, John Hopkins University, USA 7 Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Netherlands Introduction To successfully examine the brain’s functional architecture (connectome) and its behavioral associations, researchers need tools that facilitate reliable, replicable connectivity analyses. Here we introduce C-­PAC, a configurable, open-­source, automated processing pipeline for functional MRI data that builds upon a robust set of existing software packages. Users can rapidly orchestrate automated large-­scale pre-­processing and data analyses, and can easily explore the impact of processing decisions on their findings by specifying multiple analysis pipelines to be run simultaneously. C-­PAC can reliably process hundreds or thousands of subjects through a variety of preprocessing strategies in a single run. Thus C-­PAC has been optimized for use on large data sets such as those made public by the International Neuroimaging Data-­sharing Initiative (INDI, http://fcon_1000.projects.nitrc.org/). Methods C-­PAC has been implemented in Python using the Nipype pipelining library. Nipype provides C-­PAC with mechanisms to automatically detect and exploit parallelism present in a pipeline, iterate over several parameter settings, and to restart a pipeline without having to recompute previously completed processing steps. C-­PAC extends Nipype functionality by providing workflows specific to connectivity analyses, functional connectivity derivatives and analyses not present in other neuroimaging packages, and a simplified interface for specifying and running pipelines. The CPAC workflows are built from AFNI and FSL tools, as well as algorithms coded in Python using Scipy, Numpy and scikit-­learn. The C-­PAC processing and analysis pipeline (fig. 1) is configured through a simple configuration file, which permits the inclusion and exclusion of different steps, and setting of a variety of parameters. A variety of input data organization schemes and subject specific acquisition parameters (slice acquisition, slice timing information, time point censoring) are easily configured through a subject configuration file. Available preprocessing options include: motion correction, anatomical/functional coregistration, spatial normalization, spatial and temporal filtering, tissue segmentation, slice-­timing correction, several variations of nuisance signal removal and volume censoring (motion “scrubbing”). C-­PAC also includes a number of advanced analysis methods that facilitate detailed exploration of connectivity patterns, network structure, and brain-­behavior relationships. Individual-­level measures include: Seed-­ based Correlation Analysis, Amplitude of Low Frequency Fluctuations (ALFF) and Fractional ALFF, Regional Homogeneity, Voxel-­Mirrored Homotopic Connectivity, and Network Centrality (Degree and Eigenvector). At the group level, C-­PAC features Connectome-­Wide Association Studies, Bootstrap Analysis of Stable Clusters, and