Enhancing Treatment Predictions in Depression: Integrating the Brain Age Gap and Normative Modelling of Brain Morphometry

This presentation examines how combining Brain Age Gap (BAG) analysis with normative modelling of brain morphometry can enhance predictions of treatment responses in Major Depressive Disorder (MDD). Brain age provides a global quantitative representation of an individual’s brain health, but often lacks detailed regional information. In contrast, normative modelling offers precise regional brain deviations but lacks a standardized summary method across all regions. Together, these methods can provide a comprehensive and complementary assessment of brain health. Utilizing the CentileBrain Model, trained on a comprehensive dataset of over 37,000 individuals from 20 countries, we analyzed structural MRI data from the International Study to Predict Optimized Treatment in Depression (iSPOT-D). This dataset includes pre- and post-treatment scans of 224 MDD patients and 271 healthy controls. MDD patients were treated with one of three pharmacological treatments commonly prescribed as first-line medication treatments for depression. The model integrates extensive MRI metrics such as cortical thickness, surface area, and subcortical volumes to assess brain ages and regional deviations from the normative model. I will present our findings on both BAG and regional deviations for characterizing case-control differences, and predicting responses to the three treatments used in the study. I will discuss the implications of integrating brain age with normative models for clinical psychiatry. This approach not only refines our predictions but also provides deeper insights into the neurobiological mechanisms influencing treatment response. The presentation aims to show how these predictive tools can be used to tailor therapeutic interventions more precisely, potentially transforming treatment strategies based on individual age-related brain profiles.