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.

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Moo K. Chung, Kim M. Dalton, Richard J. Davidson
• Keywords: Cortical Shape Variations, Local Area Element, Smooth Functional Parametrization, TMI 2008