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将解剖形状与神经心理学测量相关联的变形动量的多变量统计分析。

Multivariate statistical analysis of deformation momenta relating anatomical shape to neuropsychological measures.

作者信息

Singh Nikhil, Fletcher P Thomas, Preston J Samuel, Ha Linh, King Richard, Marron J Stephen, Wiener Michael, Joshi Sarang

机构信息

University of Utah, Salt Lake City, UT, USA.

出版信息

Med Image Comput Comput Assist Interv. 2010;13(Pt 3):529-37. doi: 10.1007/978-3-642-15711-0_66.

Abstract

The purpose of this study is to characterize the neuroanatomical variations observed in neurological disorders such as dementia. We do a global statistical analysis of brain anatomy and identify the relevant shape deformation patterns that explain corresponding variations in clinical neuropsychological measures. The motivation is to model the inherent relation between anatomical shape and clinical measures and evaluate its statistical significance. We use Partial Least Squares for the multivariate statistical analysis of the deformation momenta under the Large Deformation Diffeomorphic framework. The statistical methodology extracts pertinent directions in the momenta space and the clinical response space in terms of latent variables. We report the results of this analysis on 313 subjects from the Mild Cognitive Impairment group in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

摘要

本研究的目的是描述在诸如痴呆症等神经疾病中观察到的神经解剖学变异。我们对脑解剖结构进行全面的统计分析,并识别出能够解释临床神经心理学测量中相应变异的相关形状变形模式。其动机是建立解剖形状与临床测量之间的内在关系模型,并评估其统计显著性。我们在大变形微分同胚框架下使用偏最小二乘法对变形动量进行多变量统计分析。该统计方法根据潜在变量在动量空间和临床反应空间中提取相关方向。我们报告了来自阿尔茨海默病神经影像倡议(ADNI)中轻度认知障碍组的313名受试者的这一分析结果。

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