Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Chaggar, P.; Vogel, J. W.; Thompson, T. B.; Aldea, R.; Strandberg, O.; Stomrud, E.; Palmqvist, S.; Ossenkoppele, R.; Jbabdi, S.; Magon, S.; Klein, G.; Alzheimer's Disease Neuroimaging Initiative,; Mattsson-Carlgren, N.; Hansson, O.; Goriely, A.

doi:10.64898/2026.01.27.701320

bioRxiv · 10.64898/2026.01.27.701320

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Chaggar, P.·Vogel, J. W.·Thompson, T. B.·Aldea, R.·Strandberg, O.·Stomrud, E.·Palmqvist, S.·Ossenkoppele, R.·Jbabdi, S.·Magon, S.·Klein, G.·Alzheimer's Disease Neuroimaging Initiative,·Mattsson-Carlgren, N.·Hansson, O.·Goriely, A.

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Abstract

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

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Chaggar, P., Vogel, J. W., Thompson, T. B., Aldea, R., Strandberg, O., Stomrud, E., Palmqvist, S., Ossenkoppele, R., Jbabdi, S., Magon, S., Klein, G., Alzheimer's Disease Neuroimaging Initiative,, Mattsson-Carlgren, N., Hansson, O., Goriely, A.. 2026-01-29. Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression. https://doi.org/10.64898/2026.01.27.701320

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