Longitudinal Mixed Membership Image-on-Scalar Model

Thomas M. Menino Convention & Exhibition Center 

Magnetic resonance imaging (MRI) data has been extensively applied in diagnosing
and predicting Alzheimer's disease (AD). However, there has been a notable oversight
in addressing individual heterogeneity within longitudinal MRI data. This paper introduces
a novel modeling framework to elucidate the diverse dynamic patterns inherent
in longitudinal imaging data, thereby facilitating a better understanding of individualized
AD progression. The framework commences with a basis expansion approach
to approximate the longitudinal images. Subsequently, a vector of probability weights
is introduced, delineating a subject's partial membership across clusters. Such partial
membership allows the subject's repeatedly measured imaging data to belong to different
clusters. Finally, a nonlinear trajectory model is employed to capture the typical
normal aging process and the potential transition from normal to severe stages during
the disease course. A Bayesian approach coupled with efficient MCMC algorithms is
developed for statistical inference. Extensive simulation results demonstrate the efficacy
of the proposed methods in parameter estimation and model selection. The framework
is applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, yielding
insights into the evolving patterns of brain structures throughout AD progression.

Mixed membership model

Imaging data

Longitudinal analysis

Alzheimer's disease