期刊
ANNALS OF APPLIED STATISTICS
卷 8, 期 4, 页码 2175-2202出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/14-AOAS748
关键词
Principal components; linear mixed model; diffusion tensor imaging; brain imaging data; multiple sclerosis
资金
- National Institute of Neurological Disorders and Stroke [R01NS060910]
- NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) [EB012547]
- German Research Foundation through the Emmy Noether Programme, Grant [GR 3793/1-1]
- Intramural Research Program of the National Institute of Neurological Disorders and Stroke
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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