title: Tensor-based cortical surface morphometry via weighted spherical harmonic representation.
abstract: We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.
authors.affiliation: Department of Biostatistics and Medical Informatics, and the Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI 53706, USA. mkchung@wisc.edu
authors.forename: Moo K
authors.initials: MK
authors.lastname: Chung
authors.forename: Kim M
authors.initials: KM
authors.lastname: Dalton
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Davidson
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity