EEG microstate in first-episode drug-naive adolescents with depression. (1st October 2022)

• Record Type: Journal Article
• Authors: Zhao, Zongya; Niu, Yanxiang; Zhao, Xiaofeng; Zhu, Yu; Shao, Zhenpeng; Wu, Xingyang; Wang, Chong; Gao, Xudong; Wang, Chang; Xu, Yongtao; Zhao, Junqiang; Gao, Zhixian; Ding, Junqing; Yu, Yi
• Is Part Of: Journal of neural engineering. Volume 19:Number 5(2022)
• Publication Date: 2022-10-01
• Subjects: EEG microstates -- depression -- Lempel–Ziv complexity -- sample entropy; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac88f6
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: A growing number of studies have revealed significant abnormalities in electroencephalography (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. 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 an SVM for classification of patients with depression. 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. 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.