Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures

Liyong Yu; Wei Peng; Wenting Lin; Yucai Luo; Daijie Hu; Guangli Zhao; Hao Xu; Zeyang Dou; Qi Zhang; Xiaojuan Hong; Siyi Yu

doi:10.1093/sleep/zsae080

Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures

Sleep. 2024 Mar 23:zsae080. doi: 10.1093/sleep/zsae080. Online ahead of print.

Authors

Liyong Yu¹, Wei Peng², Wenting Lin³, Yucai Luo¹, Daijie Hu¹, Guangli Zhao³, Hao Xu¹, Zeyang Dou¹, Qi Zhang², Xiaojuan Hong¹, Siyi Yu¹

Affiliations

¹ School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
² Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
³ School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

PMID: 38520362
DOI: 10.1093/sleep/zsae080

Abstract

Study objectives: This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions.

Methods: We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively.

Results: CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR<0.05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin<0.05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions.

Conclusions: This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.

Keywords: chronic insomnia disorder; global brain connectivity; machine learning; neurotransmitter systems; resting-state electroencephalography.