Effect of Intranasal Oxytocin on Resting-state Effective Connectivity in Schizophrenia.

OBJECTIVES: Evidence from several lines of research suggests the critical role of neuropeptide oxytocin in social cognition and social behavior. Though a few studies have examined the effect of oxytocin on clinical symptoms of schizophrenia, the underlying neurobiological changes are underexamined. Hence, in this study, we examined the effect of oxytocin on the brain's effective connectivity in schizophrenia. METHODS: 31 male patients with schizophrenia (SCZ) and 21 healthy male volunteers (HV) underwent resting functional magnetic resonance imaging scans with intra-nasal oxytocin (24 IU) and placebo administered in counterbalanced order. We conducted a whole-brain effective connectivity analysis using a multivariate vector autoregressive granger causality model. We performed a conjunction analysis to control for spurious changes and canonical correlation analysis between changes in connectivity and clinical and demographic variables. RESULTS: Three connections, sourced from the left caudate survived the FDR correction threshold with the conjunction analysis; connections to the left supplementary motor area, left precentral gyrus, and left frontal inferior triangular gyrus. At baseline, SCZ patients had significantly weaker connectivity from caudate to these three regions. Oxytocin, but not placebo, significantly increased the strength of connectivity in these connections. Better cognitive insight and lower negative symptoms were associated with a greater increase in connectivity with oxytocin. CONCLUSIONS: These findings provide a preliminary mechanistic understanding of the effect of oxytocin on brain connectivity in schizophrenia. The study findings provide the rationale to examine the potential utility of oxytocin for social cognitive deficits in schizophrenia.

Deep learning model using retinal vascular images for classifying schizophrenia.

Contemporary psychiatric diagnosis still relies on the subjective symptom report of the patient during a clinical interview by a psychiatrist. Given the significant variability in personal reporting and differences in the skill set of psychiatrists, it is desirable to have objective diagnostic markers that could help clinicians differentiate patients from healthy individuals. A few recent studies have reported retinal vascular abnormalities in patients with schizophrenia (SCZ) using retinal fundus images. The goal of this study was to use a trained convolution neural network (CNN) deep learning algorithm to detect SCZ using retinal fundus images. A total of 327 subjects [139 patients with Schizophrenia (SCZ) and 188 Healthy volunteers (HV)] were recruited, and retinal images were acquired using a fundus camera. The images were preprocessed and fed to a convolution neural network for the classification. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The CNN achieved an accuracy of 95% for classifying SCZ and HV with an AUC of 0.98. Findings from the current study suggest the potential utility of deep learning to classify patients with SCZ and assist clinicians in clinical settings. Future studies need to examine the utility of the deep learning model with retinal vascular images as biomarkers in schizophrenia with larger sample sizes.