Repeated Measures of the Brain Age Gap in Mood Disorders: Stable Imprint or Dynamic Process?

Large-scale studies have linked a higher brain age gap (BAG) in adults to poorer mental health and unfavorable lifestyle factors, but its practical utility in clinical settings remains uncertain. While it is suggested that individual variations in the BAG reflect an ongoing process of neurobiological aging, current evidence is mostly based on cross-sectional data and there is a notable scarcity of studies examining repeated brain age measurements over time. Our understanding of how the BAG relates to health and disease is still evolving, and a key open question is whether the BAG is stable or dynamic. For example, it is crucial to determine whether recovery from psychiatric symptoms following treatment is accompanied by a corresponding beneficial change of the BAG. Such covariance is a necessary, but not sufficient condition for causality, and will add to our understanding of whether the BAG could be a useful target for treatment. In this talk, I will review the current state of the field regarding longitudinal BAG studies, with a particular focus on their application in clinical trials. I will explore potential (methodological) reasons for the lack of current evidence demonstrating dynamic changes in the BAG over time, both in general and specifically in response to treatment. Finally, I will end by presenting findings from two clinical trials with repeated scans (i.e., pre- and post-treatment) in which brain age was estimated using an algorithm trained on the largest and most diverse dataset to date (N = 53,542 individuals). This algorithm was applied to both young (15-26 years) and adult (21-66 years) individuals diagnosed with mood disorders—such as major depressive disorder, anxiety, and bipolar disorder —who received either pharmacological treatments (antidepressants, mood stabilizers, antipsychotics) or non-pharmacological treatments (running therapy).