Development of computational infrastructure accessible to all NRG participants, CNARI (Computational Neuroscience and Applications Research Infrastructure)

The CNARI infrastructure consists of two basic interacting elements, (1) a database system for storing and retrieving brain imaging and behavioral data {Hasson, 2008 #6990}, and (2) a collection of workflows written in the specialized language, SWIFT {Zhao, 2007 #7065}, that simplify and facilitate parallel computation {Stef-Praun, 2007 #6724} using worldwide computing Grid resources {Foster, 2006 #6902; Foster, 2001 #5774}.

To achieve some of our goals for brain imaging requires high performance computing and infrastructures focused on supporting such research {Pordes, 2007 #7064; Beckman, 2005 #7063}. We use Grid computing technologies with existing Grid infrastructures. We also use the Swift workflow system to reduce the complexities of parallel distributed computing by automating the management of computing resources and data transfer. For example, a researcher can express a workflow in the form of a Swift script utilizing multiple analysis tools and packages as a single function and allowing Swift to parallelize independent processes implicitly and send them to remote grid sites. During a typical functional MRI experiment, the scanner collects data from around 70,000 voxels in the brain, and during the transformations carried out during the analyses, these 70,000 time series can increase to ~ 400K functional time series per participant via interpolation. The Swift workflow system permits us to analyze these data sets on grid resources, providing a location-independent way to specify logical computations at a high level suitable for use by non-programming scientists.

We have used CNARI for several types of imaging tasks, including extraction of cortical surfaces, permutation methods for statistical analysis, Peak and Valley Analysis (PVA) for probing time series, and two types of connectivity analysis, particularly relevant to the goals of the JSMF collaboration.

The first of these is a functional connectivity approach that involves cross-correlation of the time course of activation of a voxel of interest (or an average of the time courses of a cluster of such voxels) with the time course of some or all other voxels in the brain {Biswal, 1995 #2314}. The computation for a single seed region typically takes about 3 computing hours per participant per seed region (60 computing hours per group analysis). If the goal were to perform such computations on the entire brain, using many anatomical or functional seeds, it would be critical to employ a system like CNARI. We have performed functional connectivity analysis using seeds in the precuneus region and the angular gyrus in a study of resting networks {Hasson, 2008 #7026}.

The second of these connectivity approaches is structural equation modeling (SEM), an extension of the correlational methods that uses known anatomy to augment the functional information with structural connectivity information, to create a model of both static and dynamic relationships. This approach was pioneered by the PI of the JSMF consortium {McIntosh, 1994 #2324}. SEM has certain limitations, one of which is that it does not lead to a unique solution. There are many possible models that are a good fit to the anatomy and to the data, but it has generally been impossible to explore the large space of models. We have developed an unbiased approach to the construction of such network models {Skipper, 2007 #6980}. Although the approach requires massive amounts of computational resources, requiring use of cluster and grid computing methods {Horn, 2005 #5746; Foster, 2001 #5774; Hasson, 2008 #6990}, it has the advantage that the resulting model can be assured to be a “best” model to describe the system in a formal mathematical sense. We have now implemented this approach in Swift and it now forms part of the CNARI infrastructure.