Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis

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.

Published in at http://dx.doi.org/10.1214/14-AOAS748 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Multilevel functional principal component analysis
• Fast Covariance Estimation for High-dimensional Functional Data
• Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data
• Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
• Reliable Eigenspectra for New Generation Surveys
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Cited by in corpus (4)

• Fast Covariance Estimation for High-dimensional Functional Data
• Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
• Markov Chain Monte Carlo for generating ranked textual data
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