title: 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 ultra-high 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.
authors: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205.
authors: Vadim
authors: V
authors: Zipunnikov
authors: Department of Statistics, Ludwig-Maximilians-Universität and Miinchen, 80539 Munich, Germany.
authors: Sonja
authors: S
authors: Greven
authors: Haochang
authors: H
authors: Shou
authors: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205.
authors: Brian
authors: B
authors: Caffo
authors: Translational Neurology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
authors: Daniel S
authors: DS
authors: Reich
authors: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205.
authors: Ciprian
authors: C
authors: Crainiceanu
keywords: brain imaging data; diffusion tensor imaging; linear mixed model; multiple sclerosis; principal components