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

TL;DR

This paper introduces a scalable, flexible framework for analyzing high-dimensional longitudinal imaging data, decomposing variability into subject-specific and visit-specific components, demonstrated on multiple sclerosis DTI data.

Contribution

It presents a novel, fast method for modeling high-dimensional longitudinal imaging data with additive components, adaptable to ultrahigh-dimensional datasets.

Findings

Effective decomposition of variability in DTI data

Scalable to ultrahigh-dimensional data

Applied successfully to multiple sclerosis study

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.