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

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.