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Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)

Sharad Sikka1*, Brian Cheung2, Ranjit Khanuja2, Satra Ghosh3, Chao-gan Yan1, Qingyang Li2, Joshua Vogelstein4, Randal Burns4, Stanley Colcombe1, Cameron Craddock5, Maarten Mennes6, Clare Kelly7, Adriana Dimartino7, Francisco Castellanos7 and Michael Milham2
  • 1 Nathan S Kline Institute, United States
  • 2 Child Mind Institute, United States
  • 3 Massachusetts Institute of Technology, United States
  • 4 Johns Hopkins University, United States
  • 5 Virginia Tech Carilion Reseach Institute, United States
  • 6 Donders Centre for Cognitive Neuroimaging, Netherlands
  • 7 NYU Child Study Center, United States

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 its International Neuroimaging Data-sharing Initiative (INDI). Beyond access to data, scientists need access to appropriate tools to facilitate data exploration - particularly those who are inexperienced with the nuances of fMRI image analysis, or lack the programming support necessary for handling and analyzing large-scale datasets. Here, we announce the creation of the Configurable Pipeline for the Analysis of Connectomes (C-PAC) - a configurable, open-source, Nipype-based, automated processing pipeline for resting state fMRI (R-fMRI) data, for use by both novices and experts. C-PAC brings the power, flexibility and elegance of Nipype to users in a plug-and-play fashion -without any programming. Using an easy to read, text-editable configuration file, C-PAC users can rapidly orchestrate automated procedures central to R-fMRI analyses, including:
•quality assurance measurements
•standard image-preprocessing based on user specified preferences
•generation of connectivity maps (e.g., seed-based correlation analyses, independent component analysis)
•customizable extraction of timeseries data
•generation of connectome graphs at various scales (e.g., voxel, parcellation unit)
•generation of local R-fMRI measures (e.g. regional homogeneity, voxel-match homotopic connectivity, frequency amplitudes)

C-PAC makes it possible to use a single configuration file to launch a product set of pipelines that differ with respect to specific parameters in each set (e.g., spatial/temporal filter setting, global correction strategies, motion correction strategies) though conserve computational and storage resources. Additionally, C-PAC can handle any systematic directory organization and distributed processing via Nipype. C-PAC maintains key Nipype strengths, including the ability to (i)interface with different software packages (e.g., FSL, AFNI), (ii)protect against redundant computation and/or storage. The C-PAC beta-release will be distributed via INDI in the summer 2012. Future updates will include a graphical user interface, advanced analytic features (e.g. support vector machines, cluster analysis) and diffusion tensor imaging.

Keywords: computational neuroscience, Neuroimaging, open source, fMRI, imaging techniques

Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012.

Presentation Type: Poster

Topic: Neuroinformatics

Citation: Sikka S, Cheung B, Khanuja R, Ghosh S, Yan C, Li Q, Vogelstein J, Burns R, Colcombe S, Craddock C, Mennes M, Kelly C, Dimartino A, Castellanos F and Milham M (2014). Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC). Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00117

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Received: 21 Mar 2013; Published Online: 27 Feb 2014.

* Correspondence: Dr. Sharad Sikka, Nathan S Kline Institute, Orangeburg, United States, ssikka25@gmail.com