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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)2175-2202
Number of pages28
JournalAnnals of Applied Statistics
Volume8
Issue number4
DOIs
StatePublished - Dec 1 2014

Keywords

  • Brain imaging data
  • Diffusion tensor imaging
  • Linear mixed model
  • Multiple sclerosis
  • Principal components

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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