Abstract
Biological brain age is a brain-predicted age using machine learning to indicate brain health and its associated conditions. The presence of an older predicted brain age relative to the actual chronological age is indicative of accelerated aging processes. Consequently, the disparity between the brain’s chronological age and its predicted age (brain-age gap) and the factors influencing this disparity provide critical insights into cerebral health dynamics during aging. In this study, we employed a Lasso regression model and analyzed multimodal imaging data from 124 participants aged 53 to 76 to formulate and predict brain age. Additionally, we conducted partial correlation analyses to explore the complex relationship between the brain-age gap and network metrics, cognitive assessments, and emotional evaluations, while controlling for chronological age, gender, and education. Our findings highlight psychological resilience as a significant mitigating factor against premature brain aging. It is established that psychological resilience significantly influences the modulation of the brain-age gap. Moreover, psychological resilience and the brain-age gap exhibit a high accuracy (above 0.72) in segregating Montreal Cognitive Assessment score–based cohorts. This observation underscores significant insight into the potential of utilizing the brain-age gap as a diagnostic tool for the early detection of accelerated aging. It advocates for the timely application of interventions, including the development of programs aimed at bolstering psychological resilience.