General multivariate linear modeling of surface shapes using SurfStat

被引：114

作者：

Chung, Moo K. ^{[1
,2
,5
]}

Worsley, Keith J. ^{[4
]}

Nacewicz, Brendon M. ^{[2
]}

Dalton, Kim M. ^{[2
]}

Davidson, Richard J. ^{[2
,3
]}

机构：

[1] Univ Wisconsin, Waisman Ctr, Dept Biostat & Med Informat, Madison, WI 53705 USA

[2] Univ Wisconsin, Waisman Lab Brain Imaging & Behav, Madison, WI 53705 USA

[3] Univ Wisconsin, Dept Psychiat & Psychol, Madison, WI 53706 USA

[4] McGill Univ, Dept Math & Stat, Montreal, PQ, Canada

[5] Seoul Natl Univ, Dept Brain & Cognit Sci, Seoul 151, South Korea

来源：

NEUROIMAGE | 2010年 / 53卷 / 02期

基金：

美国国家卫生研究院;

关键词：

Amygdala; Spherical harmonics; Fourier analysis; Surface flattening; Multivariate linear model; SurfStat; UNIFIED STATISTICAL APPROACH; CORTICAL THICKNESS ANALYSIS; CEREBRAL-CORTEX; BRAIN; AUTISM; AMYGDALA; DIFFUSION; SEGMENTATION; ABNORMALITIES; ADOLESCENTS;

D O I：

10.1016/j.neuroimage.2010.06.032

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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 (C) 2010 Elsevier Inc All rights reserved

引用

页码：491 / 505

页数：15

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