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

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Title: Bayesian modelling of amyloid-beta dynamics and astrocyte influence in Alzheimer's disease.
Authors: Shaheen, Hina1 (AUTHOR) hina.shaheen@umanitoba.ca, Melnik, Roderick2 (AUTHOR) rmelnik@wlu.ca
Source: Journal of Neuroscience Methods. Sep2026, Vol. 433, pN.PAG-N.PAG. 1p.
Subjects: Amyloid beta-protein, Astrocytes, Stochastic models, Medical model, Alzheimer's disease, Bayesian analysis, Neurodegeneration
Abstract: Alzheimer's disease (AD) is a complicated neurological condition defined by the deposition of amyloid-beta (A β) plaques. Despite extensive research, the dynamics of A β 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 A β 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 A β accumulation in different disease stages. Our findings show that higher astrocyte levels can suppress A β 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. • Develops a Bayesian stochastic modelling framework for amyloid-beta (A β) dynamics in Alzheimer's disease using longitudinal ADNI data and Approximate Bayesian Computation (ABC) for likelihood-free parameter inference. • Incorporates astrocyte-mediated effects through threshold-based stochastic models and compares multiple formulations (sM1–sM3) to investigate their role in disease progression. • Demonstrates that astrocyte-dependent stochastic models better capture observed Alzheimer's disease progression trends compared with baseline formulations. • Provides a data-driven computational framework integrating biological mechanisms, stochastic modelling, and Bayesian inference for studying neurodegenerative processes. • Discusses current model limitations and outlines future directions, including multi-state astrocyte modelling and extension to early-stage disease dynamics. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Alzheimer's disease (AD) is a complicated neurological condition defined by the deposition of amyloid-beta (A β) plaques. Despite extensive research, the dynamics of A β 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 A β 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 A β accumulation in different disease stages. Our findings show that higher astrocyte levels can suppress A β 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. • Develops a Bayesian stochastic modelling framework for amyloid-beta (A β) dynamics in Alzheimer's disease using longitudinal ADNI data and Approximate Bayesian Computation (ABC) for likelihood-free parameter inference. • Incorporates astrocyte-mediated effects through threshold-based stochastic models and compares multiple formulations (sM1–sM3) to investigate their role in disease progression. • Demonstrates that astrocyte-dependent stochastic models better capture observed Alzheimer's disease progression trends compared with baseline formulations. • Provides a data-driven computational framework integrating biological mechanisms, stochastic modelling, and Bayesian inference for studying neurodegenerative processes. • Discusses current model limitations and outlines future directions, including multi-state astrocyte modelling and extension to early-stage disease dynamics. [ABSTRACT FROM AUTHOR]
ISSN:01650270
DOI:10.1016/j.jneumeth.2026.110785