Jiayue Cheng, Yanyan Ren, Qiumeng Gu, Yongguang He, Zhen Wang
{"title":"精神分裂症ECT治疗反应的QEEG生物标志物。","authors":"Jiayue Cheng, Yanyan Ren, Qiumeng Gu, Yongguang He, Zhen Wang","doi":"10.1177/15500594211058260","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. <b>Methods:</b> Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. <b>Results:</b> In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (<i>p</i> <i><</i> <i>.05</i>) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (<i>p</i> <i><</i> <i>.05</i>). <b>Conclusions:</b> QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"53 6","pages":"499-505"},"PeriodicalIF":1.6000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QEEG Biomarkers for ECT Treatment Response in Schizophrenia.\",\"authors\":\"Jiayue Cheng, Yanyan Ren, Qiumeng Gu, Yongguang He, Zhen Wang\",\"doi\":\"10.1177/15500594211058260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Background:</b> Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. <b>Methods:</b> Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. <b>Results:</b> In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (<i>p</i> <i><</i> <i>.05</i>) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (<i>p</i> <i><</i> <i>.05</i>). <b>Conclusions:</b> QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.</p>\",\"PeriodicalId\":10682,\"journal\":{\"name\":\"Clinical EEG and Neuroscience\",\"volume\":\"53 6\",\"pages\":\"499-505\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical EEG and Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15500594211058260\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/11/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical EEG and Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15500594211058260","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/11/18 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
QEEG Biomarkers for ECT Treatment Response in Schizophrenia.
Background: Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. Methods: Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. Results: In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (p<.05) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (p<.05). Conclusions: QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.
期刊介绍:
Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.