Accelerated brain aging has been consistently reported in patients with schizophrenia. Over the past decade, these findings have been replicated using the Brain Age paradigm, which applies machine learning techniques to estimate brain age from neuroimaging data. This approach yields a single index, the Brain Age Gap, defined as the difference between predicted and chronological age. Nevertheless, both the progressive nature of this phenomenon and the potential role of antipsychotic medication remain unclear. To investigate its progression, we compared the Brain Age Gap between individuals experiencing a first episode of psychosis and healthy controls using ANCOVA, adjusting for age, sex, body mass index, and estimated total intracranial volume. To enhance the robustness of our findings, we employed two distinct models: a transformer-inspired model based on harmonized volumetric brain features extracted with FastSurfer, and a previously trained deep learning model. To assess the potential effect of medication, we further compared bipolar patients who received antipsychotic treatment with those who did not. Mann-Whitney U test consistently showed that medicated bipolar patients did not exhibit a significantly larger Brain Age Gap. Both models converge on the conclusion that accelerated brain aging is unlikely to be explained by antipsychotic medication alone. Longitudinal studies are therefore required to clarify the temporal dynamics of brain aging in schizophrenia
