Tau-induced atrophy drives functional connectivity disruption in Alzheimer's disease

TL;DR

This study develops a multiphysics model linking tau-induced brain atrophy to disruptions in functional connectivity in Alzheimer's disease, providing a predictive framework for disease progression.

Contribution

The paper introduces a novel multiphysics modeling framework that integrates tau propagation, tissue biomechanics, and network analysis to predict functional connectivity changes.

Findings

Model accurately reproduces observed regional atrophy patterns.

Regional atrophy rates predict longitudinal FC changes.

Atrophy-informed degradation matrix captures FC disruption magnitude.

Abstract

Alzheimer's disease involves progressive tau accumulation and spread, leading to regional brain atrophy and disruption of large-scale functional networks. While tau propagation and tissue degeneration have been widely modeled, how atrophy dynamics translate into functional connectivity (FC) degradation remains unclear. Here, we develop a multiphysics framework integrating anisotropic tau reaction-diffusion, finite-deformation biomechanics, and network modeling to link tau-driven atrophy with FC changes. Model fidelity is evaluated by quantitatively comparing simulated atrophy patterns with imaging-derived measurements. Using longitudinal structural and functional MRI, we identify an approximately linear relationship between regional atrophy rates and FC change. We then construct an atrophy-informed structural network degradation matrix from model-predicted region-specific atrophy rates and embed it into a neural oscillation model to predict FC disruption. Our results show that (i) the coupled reaction-diffusion-biomechanical model reproduces observed regional atrophy, (ii) regional atrophy rates parsimoniously predict longitudinal FC changes, and (iii) the atrophy-informed degradation matrix captures the direction and relative magnitude of regional FC disruption. By converting tau-driven atrophy into predictive FC trajectories, the proposed framework offers a clinically interpretable avenue for forecasting disease progression and informing trial design.