A coupled-mechanisms modelling framework for neurodegeneration

TL;DR

This paper introduces a flexible, individual-specific modeling framework for neurodegeneration that combines multiple mechanisms of pathology progression, accounting for heterogeneity and identifying patient subgroups based on mechanism importance.

Contribution

It presents a novel coupled-mechanisms modeling framework that integrates network topology and spreading processes, fitted at the individual level with Bayesian model selection.

Findings

Model explains individual neurodegeneration patterns.

Identifies patient subgroups with similar mechanisms.

Provides insights into mechanisms driving disease heterogeneity.

Abstract

Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer's disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. This provides a combination of mechanisms that best explains the observations for each individual from the ADNI dataset. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms.