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Introduction: Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response., Objects: We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia., Methods: Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF ratio , was evaluated based on diagnostic tests and clinical correlates., Results: In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset., Conclusions: Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia., (© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)

Objective: Neuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach. Methods: This high-resolution structural magnetic resonance imaging study included 57 patients at baseline. After ECT combined with antipsychotics, 28 and 29 patients were classified as responders and non-responders. Features of gray matter were extracted and compared. The logistic regression model/support vector machine (LRM/SVM) analysis was used to explore the predictive performance. Results: The regularized multivariate LRM accurately discriminated responders from non-responders, with an accuracy of 90.91%. The structural features were further confirmed in the validating data set, resulting in an accuracy of 87.59%. The accuracy of the SVM in the training set was 90.91%, and the accuracy in the validation set was 91.78%. Conclusion: Our results support the possible use of structural brain feature-based radiomics as a potential tool for predicting ECT response in patients with schizophrenia undergoing antipsychotics, paving the way for utilization of markers in psychosis. [ABSTRACT FROM AUTHOR]