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Tsai HJ, Yang WC, Tsai SJ, Lin CH, Yang AC. Right-side frontal-central cortical hyperactivation before the treatment predicts outcomes of antidepressant and electroconvulsive therapy responsivity in major depressive disorder. J Psychiatr Res 2023; 161:377-385. [PMID: 37012197 DOI: 10.1016/j.jpsychires.2023.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 04/05/2023]
Abstract
Major depressive disorder places a great burden on healthcare resources worldwide. Antidepressants are the first-line treatment for major depressive disorder, but if patients don't respond adequately, brain stimulation therapy may be needed as second-line treatment. Digital phenotyping in patients with major depressive disorder will aid in the timely prediction of treatment effectiveness. This study explored electroencephalographic (EEG) signatures that diversify depression treatment responsivity, including antidepressant administration or brain stimulation therapy. Resting-state, pre-treatment EEG sequences from depressive patients who received fluoxetine treatment (n = 55; 26 remitters and 29 poor responders) or electroconvulsive therapy (ECT, n = 58; 36 remitters and 22 nonremitters) were recorded on 19 channels. Twenty-nine EEG segments were obtained from each patient per recording electrode. Power spectral analysis was conducted for feature extraction and showed the highest predictive accuracy for fluoxetine or ECT outcomes. Both occurred with beta-band oscillations within right-side frontal-central (F1-score = 0.9437) or prefrontal areas of the brain (F1-score = 0.9416), respectively. Significantly higher beta-band power was observed among patients who lacked adequate treatment response than the remitters, specifically at 19.2 Hz or 24.5 Hz for fluoxetine administration or ECT outcome, respectively. Our findings indicated that pre-treatment, right-side cortical hyperactivation is associated with poor outcomes of antidepressant-based or ECT-based treatment in major depression. Whether depression treatment response rates can be improved by reducing the high-frequency EEG power in corresponding areas of the brain to provide a protective effect against depression recurrence warrants further study.
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Affiliation(s)
- Hsin-Jung Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Cheng Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Psychiatry, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Ching-Hua Lin
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan.
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
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Zhao Z, Niu Y, Zhao X, Zhu Y, Shao Z, Wu X, Wang C, Gao X, Wang C, Xu Y, Zhao J, Gao Z, Ding J, Yu Y. EEG microstate in first-episode drug-naive adolescents with depression. J Neural Eng 2022; 19. [PMID: 35952647 DOI: 10.1088/1741-2552/ac88f6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/11/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far. METHODS Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression. RESULTS Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained. CONCLUSION Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.
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Affiliation(s)
- Zongya Zhao
- Xinxiang Medical University, College of Medical Engineering, Xinxiang, Henan, 453003, CHINA
| | - Yanxiang Niu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xiaofeng Zhao
- First Affiliated Hospital of Zhengzhou University, Department of Psychiatry, Zhengzhou, 450000, CHINA
| | - Yu Zhu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Zhenpeng Shao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xingyang Wu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Chong Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xudong Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Chang Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Yongtao Xu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqiang Zhao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Zhixian Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqing Ding
- First Affiliated Hospital of Zhengzhou University Zhengzhou, Department of Neurology, Zhengzhou, 450000, CHINA
| | - Yi Yu
- Xinxiang Medical University, college of Biomedical Engineering, Xinxiang, 453003, CHINA
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