Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease

• Published in: Frontiers in physiology Vol. 12; p. 702975
• Main Authors: Schäfer, Amelie, Peirlinck, Mathias, Linka, Kevin, Kuhl, Ellen
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 16.07.2021
• Subjects: Alzheimer's disease; Bayesian inference; hierarchical modeling; network diffusion model; Physiology; tau PET; uncertainty quantification; network diffusion model; uncertainty quantification; Bayesian inference; Alzheimer's disease; hierarchical modeling; tau PET
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2021.702975

Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain in vivo , allowing for new insights to the dynamics of this biomarker. Here we personalize a network diffusion model with global spreading and local production terms to longitudinal tau positron emission tomography data of 76 subjects from the Alzheimer's Disease Neuroimaging Initiative. We use Bayesian inference with a hierarchical prior structure to infer means and credible intervals for our model parameters on group and subject levels. Our results show that the group average protein production rate for amyloid positive subjects is significantly higher with 0.019±0.27/yr, than that for amyloid negative subjects with −0.143±0.21/yr ( p = 0.0075). These results support the hypothesis that amyloid pathology drives tau pathology. The calibrated model could serve as a valuable clinical tool to identify optimal time points for follow-up scans and predict the timeline of disease progression.