Constraining Disease Progression Models Using Subject Specific Connectivity Priors

Abstract

We propose a simple yet powerful extension for event-based progression disease model by exploiting the Network Diffusion Hypothesis. Our approach allows incorporating connectivity information derived from diffusion MRI data in the form of an informative prior on event ordering. This simple extension using a definition of transition probability based on network path length leads to improved reproducibility and discriminative power. We report experimental results on a subset of the Alzheimer’s Disease Neuroimaging Initiative data set (ADNI 2). Though trained solely on cross-sectional data, our model successfully assigns higher progression scores to patients converting to more severe stages of dementia.