  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.
