Network-based amyloid-beta pathology predicts subsequent cognitive decline in cognitively normal older adults

Abstract

The deposition of amyloid-{beta} (A{beta}) protein in the human brain is a hallmark of Alzheimers disease and is related to cognitive decline. However, the relationship between early A{beta} deposition and future cognitive impairment remains poorly understood, particularly concerning its spatial distribution and network-level effects. Here, we employed a cross-validated machine learning approach and investigated whether integrating subject-specific brain connectome information with A{beta} burden measures improves predictive validity for subsequent cognitive decline. Baseline regional A{beta} pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the A{beta} measures improved predictive performance. We further identified a neuropathological signature pattern linked to future cognitive decline, which was validated in an independent cohort. These findings advance our understanding of how A{beta} pathology relates to brain networks and highlight the potential of network-based metrics for A{beta}-PET imaging to identify individuals at higher risk of cognitive decline.