Abstract
"Is the brain 'wiring' different between groups of populations?" is an increasingly important question with advances in diffusion MRI and abundance of network analytic tools. Recently, automatic, data-driven and computationally efficient framework for extracting brain networks using tractography and epsilon neighborhoods were proposed in the diffusion tensor imaging (DTI) literature [1]. In this paper we propose new extensions to that framework and show potential applications of such epsilon radial networks (ERN) in performing various types of neuroimage analyses. These extensions allow us to use ERNs not only to mine for topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus we demonstrate the use of ERNs as a novel image processing lens for statistical and machine learning based analyses. We demonstrate its application in an autism study for identifying topological and quantitative group differences, as well as performing classification. Finally, these views are not restricted to ERNs but can be effective for population studies using any computationally efficient network-extraction procedures.