  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.
