Resting- state functional connectivity as a predictor of response to electroconvulsive therapy in schizophrenia

Abstract

For treatment-resistant schizophrenia, electroconvulsive therapy (ECT) augmentation is a recommended option, which produced rapid reduction of symptoms. However, there are still about one third of patients can’t reach the minimal criterion. Predictive evaluation of clinical efficacy before treatment, will be of great value for diagnosing patients who are suitable for ECT in case unnecessary economic burden, prolonged treatment time and possible side effects. Currently, the biomarker for predicting ECT efficacy in schizophrenia is still lacking. Previous studies showed that brain regions suffered with the strongest electric field strengths during ECT often had most structural or functional change. Therefore, we speculate whether the pre-treatment function of these regions can predict the outcome of ECT in schizophrenia at the individual level. Forty three schizophrenia patients were included and functional magnetic resonance imaging scans and clinical data were collected before and after the course of ECT. We first simulated the distribution of ECT charge in the brain to identify the region of interest (ROI) which had the highest current density. Then, using the functional connectivity among ROIs to build classifier to distinguish high responders from low responders. Our results showed that that hippocampus, amygdala, insula, orbital frontal and most area in temporal lobe had strong electric field strength. The classifier established by the baseline connectivity network within these regions had classification accuracy of 77.55%, with sensitivity of 81.25% and specificity of 70.59%. These preliminary results demonstrate that the baseline resting state synchronization activity among regions having dense charge during ECT may predict response of ECT in schizophrenia, and can be used as a potential biomarker for individualized diagnosis.