SimBRAge: A Framework for Region-Level Morphometric Simulation for What-If Brain Age Analysis

Understanding how localized anatomical variations influence brain age predictions is critical for clinical interpretability. However, existing explainability methods primarily capture associations and do not support controlled interventions to assess how specific morphometric changes affect model output. These approaches lack the ability to simulate coherent “what-if” scenarios at the regional level. We present a simulation framework that enables localized morphometric interventions with biologically consistent propagation of effects. When a single feature (e.g., gray matter volume) is perturbed, the framework automatically adjusts the remaining features within the same brain region to preserve statistical dependencies, estimated from a reference population. This produces realistic, covarying input profiles that any pretrained brain age model can evaluate without retraining. The method is tested on the publicly available OpenBHB dataset, focusing on two regions of interest selected for their contrasting correlation with age. Controlled perturbations of 1%, 5%, and 10% are applied, along with a no-propagation baseline. To assess the interpretability and plausibility of the simulated profiles, we introduce a suite of evaluation metrics: relative age shift, semantic consistency with population-level trends, structural consistency across features within the region, and SBAPI, a synthetic biological plausibility index. Results show interpretable, progressive changes in predicted age, strong internal consistency, and full plausibility under all conditions.