Bayesian physics-based modeling of tau propagation in Alzheimer's disease

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.

Subjects

Alzheimer's disease

Bayesian inference

hierarchical modeling

network diffusion model

tau PET

uncertainty quantification

DDC Class

610: Medizin

More Funding Information

This work was supported by a Brit and Alex d’Arbeloff Stanford Graduate Fellowship to AS, a Belgian American Educational Foundation (B.A.E.F.) Postdoctoral Research Fellowship to MP, a DAAD Fellowship to KL, and a National Science Foundation Grant CMMI 1727268 and BioX-IIP Seed Grant to EK.

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

fphys-12-702975.pdf

Size

2.45 MB

Format

Adobe PDF

Options

Bayesian physics-based modeling of tau propagation in Alzheimer's disease