Modeling progression of neurodegenerative disease with longitudinal neuroimaging data.

摘要

Conventional methods in the study of neurodegenerative disease, such as cognitive function tests, are indirect measures of brain function. The development of neuroimaging techniques enables researchers to visualize brain structural change through magnetic resonance imaging (MRI), or quantify brain activity through positron emission tomography (PET). Studies are beginning to collect serial images on subjects and there is a need for the development of a strategy for modeling change in the brain over time using high-dimensional, spatially structured neuroimaging data. In this dissertation, two methods have been proposed.;The first method is a novel conditional approach that defines a transition model addressing the high dimensionality, spatial temporal correlations and potential measurement error in the data. We first gave a theoretical argument for the asymptotic properties of the parameter estimates based on the exact likelihood, then proposed a near MLE estimation strategy which approximates the likelihood when the calculation becomes infeasible in high dimensional cases. The second approach is a marginal model that adapts the widely used Bayesian hierarchical model in disease mapping to the longitudinal setting and to the brain anatomy, so it can be applied to neuroimaging data. We evaluated the small sample properties of parameter estimates under both models and each model's robustness under model misspecification through simulations.;We then modeled the progression of metabolism reduction that has been proposed as a tool in diagnosing AD by applying our methods to data from sequential PET scans obtained in a large scale longitudinal imaging study.