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December 2014 Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
Vadim Zipunnikov, Sonja Greven, Haochang Shou, Brian S. Caffo, Daniel S. Reich, Ciprian M. Crainiceanu
Ann. Appl. Stat. 8(4): 2175-2202 (December 2014). DOI: 10.1214/14-AOAS748

Abstract

We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations, and a subject-visit-specific imaging deviation that quantifies exchangeable effects between visits. The proposed method is very fast, scalable to studies including ultrahigh-dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.

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Vadim Zipunnikov. Sonja Greven. Haochang Shou. Brian S. Caffo. Daniel S. Reich. Ciprian M. Crainiceanu. "Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis." Ann. Appl. Stat. 8 (4) 2175 - 2202, December 2014. https://doi.org/10.1214/14-AOAS748

Information

Published: December 2014
First available in Project Euclid: 19 December 2014

zbMATH: 06408774
MathSciNet: MR3292493
Digital Object Identifier: 10.1214/14-AOAS748

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 4 • December 2014
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