A Case-Control Study On Corpus Callosum Volume and Clinical Correlates in Treatment-Resistant and Responsive Schizophrenia Patients: Addressing MRI Analysis within Single-Subject and a Novel Artificial Intelligence Paradigm

INTRODUCTION: The corpus callosum (CC) is critical for inter-hemispheric integration, and its structural abnormalities have been implicated in the neurobiology of schizophrenia (SCZ). Ap-proximately 20-35% of SCZ patients exhibit resistance to antipsychotics and are classified as treat-ment-resistant schizophrenia (TRS). To date, no studies have specifically analyzed CC morphology in TRS compared to healthy controls (HCs) and treatment-responsive SCZ patients (non-TRS). METHODS: The study included 75 HCs and 97 SCZ patients (39 TRS and 58 non-TRS, including cannabis users and non-users). Participants underwent cross-sectional assessment using the Neuro-logical Evaluation Scale (NES), the Brief Assessment of Cognition in Schizophrenia, and the Posi-tive and Negative Syndrome Scale (PANSS). CC volume was measured via MRI and analyzed with a novel artificial intelligence program (MRI-AI) for single-subject assessment, with findings con-firmed through group-level voxel-based morphometry (VBM). RESULTS: TRS patients exhibited significantly higher total PANSS (p < 0.001) and NES (p < 0.001) scores and performed worse on working memory tasks compared to non-TRS patients. MRI-AI identified CC volume reduction in 55.88% of TRS patients (TRS CC: 3.02 mL vs. non-TRS with cannabis use: 3.69 mL; without cannabis use: 3.28 mL; p = 0.002). VBM confirmed significant CC volume reduction in TRS patients relative to HCs (p = 0.004). Cannabis use in non-TRS patients did not significantly affect CC volume or cognitive performance. CONCLUSIONS: TRS is associated with pronounced neurological, cognitive, and clinical dysfunctions. Aberrant CC volume may contribute to TRS pathogenesis and executive function deficits. MRI-AI provides a robust, single-subject approach for characterizing CC abnormalities in schizophrenia, of-fering potential for personalized clinical assessment.