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Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study
Author(s) -
Wang Chunfang,
Chen Yuanyuan,
Sun Changcheng,
Zhang Ying,
Ming Dong,
Du Jingang
Publication year - 2018
Publication title -
international journal of geriatric psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.28
H-Index - 129
eISSN - 1099-1166
pISSN - 0885-6230
DOI - 10.1002/gps.4874
Subject(s) - electroencephalography , mood , psychology , stroke (engine) , logistic regression , depression (economics) , analysis of variance , audiology , clinical psychology , psychiatry , medicine , mechanical engineering , engineering , economics , macroeconomics
Background We aimed to explore the electrophysiological changes in poststroke subjects with depressed mood. Methods Resting‐state electroencephalogram (EEG) signals of 16 electrodes in 35 poststroke depressed, 24 poststroke nondepressed, and 35 age‐matched healthy control subjects were analyzed by means of spectral power analysis, a quantitative EEG measurement of different frequency bands. The relationship among depressed mood, functional status, lesion side, and poststroke time was assessed by using variance and Spearman correlation analysis. Multiple analysis of variance was used to compare the differences among the 3 groups. Binary logistic regression analysis was used to establish a regression model to predict depressed mood in stroke subjects and to explore the association between depression and EEG band power. Receiver operating characteristic curves were used to estimate the ability of spectral power selected by binary logistic regression to indicate depressed mood in stroke subjects. Results We found that the hemisphere in which the lesion was located and the time since stroke onset had no effect on depressed mood. Only the patient's functional status was related to emotional symptoms. Quantitative EEG analysis revealed increased delta, theta, and beta2 power in stroke subjects with depressed mood, particularly in temporal regions. The theta and beta2 power in the right temporal area were shown to be highly sensitive to depressed mood, and these parameters showed good discriminatory ability for depressed subjects following stroke. Conclusion Depressed mood after stroke is associated with functional status. Quantitative EEG parameters may be a useful tool in timely screening for depressed mood after stroke.