Heat kernel smoothing via laplace-beltrami eigenfunctions and its application to subcortical structure modeling

Authors:
Seung-Goo Kim;Moo K. Chung;Seongho Seo;Stacey M. Schaefer;Carien M. van Reekum;Richard J. Davidson
Affiliations:
Department of Brain and Cognitive Sciences, Seoul National University, Korea;Department of Brain and Cognitive Sciences, Seoul National University, Korea;Department of Brain and Cognitive Sciences, Seoul National University, Korea;Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI;Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, UK;Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI
Venue:
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Year:
2011

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Abstract

We present a new subcortical structure shape modeling framework using heat kernel smoothing constructed with the Laplace-Beltrami eigenfunctions. The cotan discretization is used to numerically obtain the eigenfunctions of the Laplace-Beltrami operator along the surface of subcortical structures of the brain. The eigenfunctions are then used to construct the heat kernel and used in smoothing out measurements noise along the surface. The proposed framework is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shape. We detected a significant age effect on hippocampus in accordance with the previous studies. In addition, we also detected a significant gender effect on amygdala. Since we did not find any such differences in the traditional volumetric methods, our results demonstrate the benefit of the current framework over traditional volumetric methods.