A challenge to early intervention in Alzheimer’s disease (AD) is the substantial inter-individual variability in cognitive aging trajectories, with some individuals showing greater resilience to age- and disease-related brain changes than others. Therefore, biomarkers are needed to accurately identify who is at greatest risk for cognitive decline and would benefit most from early intervention. The overall goal of the current study is to identify the neural mechanisms and lifespan determinants of individual differences in brain aging, potentially revealing new targets for primary and secondary interventions to boost resilient brain aging and delay cognitive decline. The objectives of the study are four-fold: (1) to optimize the multimodal ‘brainAGE’ metric (i.e., brain age gap estimation, an estimate of the discrepancy between an individual’s chronological age and the state of their brain health) to comprehensively capture risk and resilience to age- and AD-related cognitive decline at the individual-level by incorporating network-level metrics of structural and functional neural systems, which may better reflect adaptive resilience mechanisms that moderate the manifestation of cognitive impairment to normal and pathological age-related neurodegeneration; (2) to examine the underlying functional and structural biological subtypes of advanced and resilient aging; (3) to explore the lifespan determinants of multimodal brainAGE and the biological subtypes of advanced and resilient brain aging; and (4) using an external data source (Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4)) we aim to determine if the optimized brainAGE metric and associated subtypes of advanced and resilient aging (aims 1/2) are predictive of cognitive decline within the preclinical stage of AD beyond traditional neuropathology biomarkers (beta-amyloid, tau, neurodegeneration), clarifying its potential clinical relevance.
