Related Experiment Video
Updated: Jun 11, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
Published on: November 14, 2019
General multivariate linear modeling of surface shapes using SurfStat
Moo K Chung1, Keith J Worsley, Brendon M Nacewicz
1Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53705, USA. mkchung@wisc.edu
Abstract:
Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.
More Related Videos
Related Concept Videos
Quadric Surfaces
Parametric Surfaces
Calculus with Parametric Curves: Surface Areas
Surface Integrals
Response Surface Methodology
The process of RSM involves several key steps:
Interpretations of Partial Derivatives

