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] |
| Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194447750 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian modelling of amyloid-beta dynamics and astrocyte influence in Alzheimer's disease. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shaheen%2C+Hina%22">Shaheen, Hina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hina.shaheen@umanitoba.ca</i><br /><searchLink fieldCode="AR" term="%22Melnik%2C+Roderick%22">Melnik, Roderick</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rmelnik@wlu.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Sep2026, Vol. 433, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Amyloid+beta-protein%22">Amyloid beta-protein</searchLink><br /><searchLink fieldCode="DE" term="%22Astrocytes%22">Astrocytes</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+model%22">Medical model</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Neurodegeneration%22">Neurodegeneration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jneumeth.2026.110785 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Amyloid beta-protein Type: general – SubjectFull: Astrocytes Type: general – SubjectFull: Stochastic models Type: general – SubjectFull: Medical model Type: general – SubjectFull: Alzheimer's disease Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Neurodegeneration Type: general Titles: – TitleFull: Bayesian modelling of amyloid-beta dynamics and astrocyte influence in Alzheimer's disease. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shaheen, Hina – PersonEntity: Name: NameFull: Melnik, Roderick IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01650270 Numbering: – Type: volume Value: 433 Titles: – TitleFull: Journal of Neuroscience Methods Type: main |
| ResultId | 1 |
