Machine learning-based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia.

In this study, we integrated multiple surface-based cortical features-cortical thickness, gyrification, sulcal depth, cortical complexity, and surface ratio-from a large multi-site cohort (329 schizophrenia, 669 healthy controls) and implemented a rigorous cross-validated model with repeated random split framework to evaluate their relevance to schizophrenia.

These alterations were predominantly localized to prefrontal, insular, and temporal regions.