Altered resting-state electroencephalography microstate characteristics in young male smokers

Xiaojian Li; Fang Dong; Yunmiao Zhang; Juan Wang; Zhengxi Wang; Yaning Sun; Ming Zhang; Ting Xue; Yan Ren; Xiaoqi Lv; Kai Yuan; Dahua Yu

doi:10.3389/fpsyt.2022.1008007

Altered resting-state electroencephalography microstate characteristics in young male smokers

Front Psychiatry. 2022 Oct 4:13:1008007. doi: 10.3389/fpsyt.2022.1008007. eCollection 2022.

Authors

Xiaojian Li¹, Fang Dong¹, Yunmiao Zhang¹, Juan Wang¹, Zhengxi Wang¹, Yaning Sun¹, Ming Zhang¹, Ting Xue¹, Yan Ren¹, Xiaoqi Lv², Kai Yuan^{1

3}, Dahua Yu¹

Affiliations

¹ Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.
² College of Information Engineering, Inner Mongolia University of Technology, Hohhot, China.
³ School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China.

Abstract

The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.

Keywords: electroencephalography (EEG); microstate; nicotine addiction; resting state; young smokers.