Electroencephalography microstates as novel functional biomarkers for insomnia disorder

Yongjian Guo; Xumeng Zhao; Xiaoyang Liu; Jiayi Liu; Yan Li; Lirong Yue; Fulai Yuan; Yifei Zhu; Xiaona Sheng; Dahua Yu; Kai Yuan

doi:10.1136/gpsych-2023-101171

Electroencephalography microstates as novel functional biomarkers for insomnia disorder

Gen Psychiatr. 2023 Dec 22;36(6):e101171. doi: 10.1136/gpsych-2023-101171. eCollection 2023.

Authors

Yongjian Guo¹, Xumeng Zhao², Xiaoyang Liu¹, Jiayi Liu², Yan Li², Lirong Yue¹, Fulai Yuan³, Yifei Zhu², Xiaona Sheng², Dahua Yu⁴, Kai Yuan^{1

4}

Affiliations

¹ School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
² Department of Psychosomatic Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
³ Health Management Center, Xiangya Hospital, Central South University, Changsha, China.
⁴ Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Abstract

Background: Insomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique in this field is the use of electroencephalography (EEG) microstates, which offer a new method of EEG feature extraction that incorporates information from both temporal and spatial dimensions.

Aims: To explore the electrophysiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) for ID treatment and use baseline microstate metrics for the prediction of its efficacy.

Methods: This study included 60 patients with ID and 40 age-matched and gender-matched good sleep controls (GSC). Their resting-state EEG microstates were analysed, and the Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were collected to assess sleep quality. The 60 patients with ID were equally divided into active and sham groups to receive rTMS for 20 days to test whether rTMS had a moderating effect on abnormal microstates in patients with ID. Furthermore, in an independent group of 90 patients with ID who received rTMS treatment, patients were divided into optimal and suboptimal groups based on their median PSQI reduction rate. Baseline EEG microstates were used to build a machine-learning predictive model for the effects of rTMS treatment.

Results: The class D microstate was less frequent and contribute in patients with ID, and these abnormalities were associated with sleep onset latency as measured by PSG. Additionally, the abnormalities were partially reversed to the levels observed in the GSC group following rTMS treatment. The baseline microstate characteristics could predict the therapeutic effect of ID after 20 days of rTMS, with an accuracy of 80.13%.

Conclusions: Our study highlights the value of EEG microstates as functional biomarkers of ID and provides a new perspective for studying the neurophysiological mechanisms of ID. In addition, we predicted the therapeutic effect of rTMS on ID based on the baseline microstates of patients with ID. This finding carries great practical significance for the selection of therapeutic options for patients with ID.

Keywords: area under curve; brain; clinical trials, phase I as topic; cross-over studies; longitudinal studies.