title: Riemannian Statistical Analysis of Cortical Geometry with Robustness to Partial Homology and Misalignment.
abstract: Typical studies of the geometry of the cerebral cortical structure focus on either cortical folding or thickness. They rely on spatial normalization, but use cortical descriptors that are sensitive to misregistration arising from the well-known problems of partial homologies between subject brains and local optima in nonlinear registration. In contrast to these approaches, we propose a novel framework for studying the geometry of theentire cortical sheet, subsuming its folding and thickness characteristics. We propose a novel descriptor of local cortical geometry to increase robustness to partial homology and misregistration. The proposed descriptor lies on a Riemannian manifold, and we describe a method for hypothesis testing on manifolds for cross-sectional studies. Results on simulated and clinical data show the benefits of the proposed approach for detecting between-group differences with greater accuracy and consistency.
authors.affiliation: Computer Science and Engineering Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India.
authors.forename: Suyash P
authors.initials: SP
authors.lastname: Awate
authors.affiliation: Signal and Image Processing Institute (SIPI), University of Southern California (USC), Los Angeles, USA.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Leahy
authors.affiliation: Signal and Image Processing Institute (SIPI), University of Southern California (USC), Los Angeles, USA.
authors.forename: Anand A
authors.initials: AA
authors.lastname: Joshi
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003430:Cross-Sectional Studies; D006801:Humans; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging / Q000379:methods / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Brain cortex; Folding; Hypothesis tests; Riemannian space; Thickness