Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53705, USA.
Neuroimage. 2010 Nov 1;53(2):491-505. doi: 10.1016/j.neuroimage.2010.06.032. Epub 2010 Jul 8.
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.
尽管有许多关于传统基于 ROI 的杏仁核容积成像的研究,但关于杏仁核形状变化建模的研究却很少。本文提出了一种用于在临床人群中建模杏仁核形状变化的统一计算和统计框架。加权球谐函数表示用于参数化、平滑和归一化杏仁核表面。该表示随后被用作多元线性模型的输入,该模型考虑了年龄和大脑大小差异等混杂协变量,使用 SurfStat 包完全避免了指定设计矩阵的复杂性。该方法已应用于量化 22 名高功能自闭症患者的局部杏仁核形状异常变化。