Bayesian modelling of amyloid-beta dynamics and astrocyte influence in Alzheimer's disease

Alzheimer's disease (AD) is a complicated neurological condition defined by the deposition of amyloid-beta (Aβ) plaques. Despite extensive research, the dynamics of Aβ growth, particularly the role of astrocytes, remain poorly understood, limiting the development of effective treatments.

This study addresses this gap by introducing a Bayesian inference framework for modelling Aβ dynamics, incorporating both strong and weak astrocyte effects utilizing Alzheimer's Disease Neuroimaging Initiative (ADNI) clinical data.

Through a combination of stochastic growth models and approximate Bayesian computation (ABC), we evaluate how astrocyte concentrations influence Aβ accumulation in different disease stages. Our findings show that higher astrocyte levels can suppress Aβ growth, while lower levels promote it, suggesting that astrocyte-targeted interventions may alter disease progression.

This data-driven probabilistic approach not only captures the inherent biological variability but also provides a tractable method to estimate uncertain parameters.

The present research offers a valuable tool for therapeutic modelling and prediction in AD.