The Potential Clinical Utility of Brain Age Prediction in Affective Disorders

Neuroimaging-derived brain age metrics may potentially aid in treatment selection. This talk explores the utility of brain age gaps (BAG) in affective disorders - specifically depression, anxiety, and bipolar disorder - where symptom overlap, treatment response heterogeneity, and different treatment options (e.g., psychotherapy, different medications and TMS and ECT) pose clinical challenges. Higher BAG could potentially predict differential treatment outcomes, e.g., greater benefits from medication compared to psychotherapy and increased risk of cognitive side effects from interventions like ECT. I will begin by presenting on the relevance of studying brain age in affective disorders, followed by an overview of current methodological approaches to BAG estimation. This will include an examination of various models and input features derived from structural neuroimaging (MRI and PET) as well as functional neuroimaging (fMRI and EEG). I will conclude this part by addressing neuro-assessment (i.e., cognition), and exploring how brain age models could be scaled and implemented in future routine practice. I will also look at how different multivariate neuroimaging approaches may differentially predict various symptom domains and clinical outcomes, highlighting the importance of matching brain age estimation methods to specific treatment targets and looking beyond mood improvements. The last focus is the often-overlooked role of BAG in predicting treatment side effects, particularly cognitive dysfunction and tolerability across different interventions. Finally, I will present findings on the prognostic potential of BAG in the treatment of depression and anxiety, including both pharmacological (e.g., SSRIs) and non-pharmacological interventions (e.g., running therapy).