Network-based amyloid-beta pathology predicts subsequent cognitive decline in cognitively normal older adults.
Overview
abstract
The deposition of amyloid-beta protein in the human brain is a hallmark of Alzheimer's disease and is related to cognitive decline. However, the relationship between early amyloid-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 amyloid-beta burden measures improves predictive validity for subsequent cognitive decline. Baseline regional amyloid-beta pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the amyloid-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 amyloid-beta pathology relates to brain networks and highlight the potential of network-based metrics for amyloid-beta-PET imaging to identify individuals at higher risk of cognitive decline.
