Estimating chronological and brain age using risk-taking behavior under uncertainty

Risky decision-making under uncertainty reflects complex cognitive processes supported by distributed brain networks that are vulnerable to aging. However, it remains unclear whether risk-taking behavior can serve as a behavioral marker of brain aging. In the present study, we combined behavioral tasks, computational modeling, and structural magnetic resonance imaging to investigate the relationship between risky decision-making, chronological age, and brain age. A total of 55 young adults and 112 healthy older adults completed the Iowa Gambling Task (IGT) and the Balloon Analogue Risk Task (BART), along with neuropsychological assessments and neuroimaging scanning. Decision processes were quantified using computational models, including the Value-Plus-Perseveration model and Exponential-Weight Mean-Variance. Brain age was estimated from gray matter volume. The results showed significant age-related alterations in parameters reflecting feedback sensitivity, learning rate, and loss aversion in both tasks. Within older adults, several decision parameters were significantly associated with both chronological age and brain age. Regression analyses further showed that computational parameters significantly predicted chronological age and brain age, whereas traditional cognitive screening measures did not show significant predictive effect. Structural brain analyses indicated that IGT-related parameters were primarily associated with the basal ganglia, while BART-related parameters were linked to a broader network including prefrontal, cingulate, and temporal regions. These findings suggest that computational markers of risk-taking behavior capture subtle age-related changes in cognitive processes and brain deterioration. Therefore, risk-taking parameters may serve as reliable functional markers of brain aging, providing critical insights into the mechanisms underlying successful aging.