Alzheimer's disease (AD), one of the most common causes of dementia in adults, is a progressive neurodegenerative disorder exhibiting well-defined neuropathological hallmarks. It is known that disease pathology involves misfolded amyloid-β (Aβ) and tau proteins, and exhibits a relatively stereotyped progression over decades. The relationship between AD neuropathological hallmarks (Aβ, hypometabolism, and tau proteins) and imaging biomarkers (MRI, AV-45/FDG-PET) is not fully understood. In addition, biomarker pathologies are oftentimes discordant, wherein it may show varying levels of abnormality across brain regions. Evidence based on recent elucidation of trans-neuronal "prion-like" transmission and other available data already suggests that disease spread follows the brain's fiber connectivity network. Thereby, the brain's connectome information can be used to predict the process of disease spread in AD. A recently established mathematical model of AD pathology spread using a connectome-based network diffusion model was successful in encapsulating neurodegenerative progression. Motivated by these network-based findings, the current study explores whether and how network connectivity mediates the interactions between various AD biomarkers. We hypothesized that the structural connectivity matrix will mediate the cross-sectional association between regional AD-associated hypometabolism and Aβ deposition. Given recent reports of inherent or lifetime activity of brain regions as strong predictors of Aβ deposition in patients, we also tested whether healthy metabolism exerts a network-mediated effect on Aβ deposition and hypometabolism in AD patients. We found that regional Aβ deposition is best predicted by a linear combination of both regional healthy local metabolism and connectome-mediated regional healthy metabolism.
Keywords: AV-45-PET; Alzheimer’s disease; FDG-PET; amyloid-β; biomarkers; cross-sectional; hypometabolism; metabolism; structural connectivity.