Distinctive subcortical connectivity predicts treatment response in first-episode schizophrenia.

BACKGROUND: Subcortical regions are widely implicated in the pathological mechanisms and treatment of schizophrenia, and accumulating evidence, including our prior findings, suggests that subcortical functional dysconnectivity is closely associated with treatment response. Accordingly, the present study aimed to examine the relationship between the subcortical functional connectivity (FC) and treatment outcomes in schizophrenia using multivariate analytical approaches and machine learning algorithms. METHODS: One hundred and nineteen individuals with first-episode schizophrenia were recruited for this study. All patients underwent MRI scanning and completed assessments with the Positive and Negative Syndrome Scale (PANSS) at baseline and at follow-up after 12 weeks of antipsychotic medication. We employed partial least squares analysis to explore the multivariate associations between changes in subcortical FC (∆FC) and changes in symptom severity (∆PANSS). In addition, a machine learning algorithm was used to predict the antipsychotic treatment outcome based on the distinctive subcortical FC pattern at baseline. RESULTS: We identified a distinctive subcortical FC pattern dominated by the striatum that was associated with overall treatment outcomes in first-episode schizophrenia. Furthermore, the reduction in PANSS total scores predicted using baseline subcortical FC patterns was positively correlated with the actual reduction in PANSS total scores following antipsychotic treatment. CONCLUSION: These results indicate that the distinctive subcortical FC pattern holds promise as a biomarker for schizophrenia, supporting individualized treatment approaches and facilitating early intervention to improve clinical outcomes.