Beomjun Min1, Minah Kim2, Junhee Lee3, Jung-Ick Byun4, Kon Chu5, Ki-Young Jung5, Sang Kun Lee5, Jun Soo Kwon6. 1. Department of Public Health Medical Services, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. 2. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address: verte82@snu.ac.kr. 3. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. 4. Department of Neurology, Laboratory for Neurotherapeutics, Comprehensive Epilepsy Center, Center for Medical Innovations, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea; Department of Neurology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea; Program in Neuroscience, Seoul National University College of Medicine, Seoul, Republic of Korea. 5. Department of Neurology, Laboratory for Neurotherapeutics, Comprehensive Epilepsy Center, Center for Medical Innovations, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea; Program in Neuroscience, Seoul National University College of Medicine, Seoul, Republic of Korea. 6. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) has strong efficacy in patients with treatment refractory schizophrenia. However, access to ECT has been limited by high costs, professional labor, treatment duration, and significant adverse effects. To provide support for the decision to perform ECT, we aimed to predict individual responses to ECT among patients with schizophrenia using machine learning analysis of resting-state electroencephalography (EEG). METHODS: Forty-seven patients diagnosed with schizophrenia or schizoaffective disorder with EEG recordings before the course of ECT were treated at Seoul National University Hospital. Among these patients, 29 were responders who showed scores of 3 or less on the Clinical Global Impression Severity scale after the course of ECT. Transfer entropy (TE), which represents information flow, was extracted from baseline EEG data and used as a feature. Feature selection was performed with four methods, including Random Subset Feature Selection (RSFS). The random forest classifier was used to predict individual ECT responses. RESULTS: The averaged TE, especially in frontal regions, was higher in ECT responders than in nonresponders. A predictive model using the RSFS method classified ECT responders and nonresponders with 85.3% balanced accuracy, 85.2% accuracy, 88.7% sensitivity, and 81.8% specificity. The positive predictive value was 82.6%, and the negative predictive value was 88.2%. CONCLUSIONS: The results of the current study suggest that higher effective connectivity in frontal areas may be associated with a favorable ECT response. Furthermore, personalized decisions to perform ECT in clinical practice could be augmented by resting-state EEG biomarkers of the ECT response in schizophrenia patients.
BACKGROUND: Electroconvulsive therapy (ECT) has strong efficacy in patients with treatment refractory schizophrenia. However, access to ECT has been limited by high costs, professional labor, treatment duration, and significant adverse effects. To provide support for the decision to perform ECT, we aimed to predict individual responses to ECT among patients with schizophrenia using machine learning analysis of resting-state electroencephalography (EEG). METHODS: Forty-seven patients diagnosed with schizophrenia or schizoaffective disorder with EEG recordings before the course of ECT were treated at Seoul National University Hospital. Among these patients, 29 were responders who showed scores of 3 or less on the Clinical Global Impression Severity scale after the course of ECT. Transfer entropy (TE), which represents information flow, was extracted from baseline EEG data and used as a feature. Feature selection was performed with four methods, including Random Subset Feature Selection (RSFS). The random forest classifier was used to predict individual ECT responses. RESULTS: The averaged TE, especially in frontal regions, was higher in ECT responders than in nonresponders. A predictive model using the RSFS method classified ECT responders and nonresponders with 85.3% balanced accuracy, 85.2% accuracy, 88.7% sensitivity, and 81.8% specificity. The positive predictive value was 82.6%, and the negative predictive value was 88.2%. CONCLUSIONS: The results of the current study suggest that higher effective connectivity in frontal areas may be associated with a favorable ECT response. Furthermore, personalized decisions to perform ECT in clinical practice could be augmented by resting-state EEG biomarkers of the ECT response in schizophreniapatients.