High-fidelity and Network-based Spatio-temporal Mathematical Models of Alzheimer's Disease Progression and their Validation Against PET-SUVR Imaging Data

TL;DR

This paper develops and compares high-fidelity 3D and reduced network-based mathematical models of Alzheimer's disease progression, validated against PET imaging data, to understand protein accumulation patterns.

Contribution

It introduces a novel framework combining 3D biophysical and network-based models on the brain connectome for Alzheimer's disease simulation.

Findings

3D model offers more accurate disease progression predictions.

Network-based model is computationally cheaper but less reliable.

Models validated against clinical PET-SUVR imaging data.

Abstract

Alzheimer's disease is the most common neurodegenerative disorder. Its pathological development is connected with the misfolding and accumulation of two toxic proteins: amyloid-beta and tau proteins. Mathematical models provide a valuable quantitative tool for monitoring disease progression. In this work, we proposed and compare a novel framework where the spatio-temporal dynamics of amyloid-beta and tau proteins is modeled based on employing either three-dimensional patient-specific geometries or through reduced network-based models defined on the brain connectome. More specifically, a high-fidelity biophysical model is proposed on three-dimensional brain geometries reconstructed from magnetic resonance imaging, whereas a network-based reduced formulation is defined on the brain connectome. For both approaches, a suitable numerical discretisation is proposed. A sensitivity analysis is presented to quantify the influence of model parameters on protein concentration patterns as well as compare the quality of the predictions. For both approaches, the results are validated against PET-SUVR clinical data using 18FAZD4694 for amyloid-beta and 18FMK6240 for tau protein. The results indicate that the three-dimensional model provides the most accurate and biologically consistent description of the disease progression, but remains computationally demanding. On the other hand, the reduced graph-based model is cheaper, but it is not always able to achieve reliable results.