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


Sharad Sikka (Nathan S Kline Institute), Brian Cheung (Child Mind Institute), Ranjit Khanuja (Child Mind Institute), Satra Ghosh (Massachusetts Institute of Technology), Chao-gan Yan (Nathan S Kline Institute), Qingyang Li (Child Mind Institute), Joshua Vogelstein (Johns Hopkins University), Randal Burns (Johns Hopkins University), Stanley Colcombe (Nathan S Kline Institute), Cameron Craddock (Virginia Tech Carilion Reseach Institute), Maarten Mennes (Donders Centre for Cognitive Neuroimaging), Clare Kelly (NYU Child Study Center), Adriana Dimartino (NYU Child Study Center), Francisco Castellanos (NYU Child Study Center), Michael Milham (Child Mind Institute)

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.
Preferred presentation format: Poster
Topic: Computational neuroscience