Functional principal component model for high-dimensional brain imaging

被引：48

作者：

Zipunnikov, Vadim ^{[1
]}

Caffo, Brian ^{[1
]}

Yousem, David M. ^{[2
]}

Davatzikos, Christos ^{[3
]}

Schwartz, Brian S. ^{[4
]}

Crainiceanu, Ciprian ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA

[2] Johns Hopkins Univ Hosp, Dept Radiol, Baltimore, MD 21287 USA

[3] Univ Penn, Philadelphia, PA 19104 USA

[4] Johns Hopkins Bloomberg Sch Publ Hlth, Baltimore, MD USA

来源：

NEUROIMAGE | 2011年 / 58卷 / 03期

关键词：

Voxel-based morphometry (VBM); MRI; FPCA; SVD; Brain imaging data; LEAD LEVELS; MORPHOMETRY;

D O I：

10.1016/j.neuroimage.2011.05.085

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：772 / 784

页数：13

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