Amygdala Surface Modeling with Weighted Spherical Harmonics

Authors:
Moo K. Chung;Brendon M. Nacewicz;Shubing Wang;Kim M. Dalton;Seth Pollak;Richard J. Davidson
Affiliations:
Department of Statistics, Biostatistics and Medical Informatics, and Waisman Laboratory for Brain Imaging and Behavior,;Waisman Laboratory for Brain Imaging and Behavior,;Department of Statistics, Biostatistics and Medical Informatics,;Waisman Laboratory for Brain Imaging and Behavior,;Department of Psychology and Psychiatry, University of Wisconsin, Madison, USA WI 53706;Waisman Laboratory for Brain Imaging and Behavior, and Department of Psychology and Psychiatry, University of Wisconsin, Madison, USA WI 53706
Venue:
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Year:
2008

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Abstract

Although there are numerous publications on amygdala volumetry, so far there has not been many studies on modeling local amygdala surface shape variations in a rigorous framework. This paper present a systematic framework for modeling local amygdala shape. Using a novel surface flattening technique, we obtain a smooth mapping from the amygdala surface to a sphere. Then taking the spherical coordinates as a reference frame, amygdala surfaces are parameterized as a weighted linear combination of smooth basis functions using the recently developed weighted spherical harmonic representation. This new representation is used for parameterizing, smoothing and nonlinearly registering a group of amygdala surfaces. The methodology has been applied in detecting abnormal local shape variations in 23 autistic subjects compared against 24 normal controls. We did not detect any statistically significant abnormal amygdala shape variations in autistic subjects. The complete amygdala surface modeling codes used in this study is available at http://www.stat.wisc.edu/~mchung/research/amygdala.