A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer's Disease

TL;DR

This paper introduces a network-guided biochemical model that combines systems biology and network neuroscience to understand the spread of AD biomarkers and predict disease progression using longitudinal neuroimaging data.

Contribution

It presents a novel system-level model that captures the interaction and diffusion of AT[N] biomarkers across brain networks, integrating structural connectivity and resilience factors.

Findings

Model explains biomarker propagation patterns

Predicts early cognitive decline signs

Provides insights into AD progression mechanisms

Abstract

Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.