Applications of Epsilon Radial Networks in Neuroimage Analyses

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.

Effects of DTI spatial normalization on white matter tract reconstructions

“…Hence one can also build network models using normalized space tractography (e.g. (9, 10)) for studying topological differences between individuals and groups.…”

“…One can also use tractography data for obtaining various superresolution contrasts of the WM under the tract density imaging framework (5). Despite the challenge of quantitation using tractography (6), there are three popular and strong clinical applications (or population studies), namely [1] delineating specific pathway or region of interest in the white matter to test region specific hypotheses using various DTI metrics such as fractional anisotropy (FA), mean diffusivity (MD) in those regions and [2] studying various shape characteristics of the pathways for applications such as classification (7), medial axes based tract specific statistical mapping (8) and [3] for structural network modeling (9; 10). …”

Evaluating consistency of deterministic streamline tractography in non-linearly warped DTI data

“…However if one can rely on tractography in the warped DTI such inverse warping and manual editing could be minimized. More recent applications relying on individual level normalized space tractography are in assessing more global structural brain connectivity patterns (Hagmann et al, 2007;Chung et al, 2011;Adluru et al, 2012a).…”

“…Potential advantages of this strategy include performing tractography in a very standard way at the most consistent spatial scale of brain structures. This may be desirable for using tractography to obtain tract-specific regional measurements of DTI measures like FA and MD in normalized space, comparing global brain tractography networks (Chung et al, 2011;Adluru et al, 2012a) or studying various shape characteristics of the pathways for applications such as classification (Adluru et al, 2009).…”

“…Tract reconstructions in the population-averaged DTI data appear much smoother and emphasize the larger, and less complex pathways. However, relatively few studies have performed tractography on the warped DTI data of the individual subjects (Park et al, 2003;Chung et al, 2011;Adluru et al, 2012a). Due to the intricacies involved in warping DTI for analytical inference, we believe it is important to have data to resolve whether tractography performed in the warped tensors is consistent with tractography in the unwarped native tensors in practical situations.…”