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

脑电图 神经科学 神经递质 心理学 医学 中枢神经系统
作者
Liyong Yu,Wei Peng,Wenting Lin,Yucai Luo,Daijie Hu,Guangli Zhao,Hao Xu,Zeyang Dou,Qí Zhāng,Xiaojuan Hong,Siyi Yu
出处
期刊:Sleep [Oxford University Press]
卷期号:47 (7) 被引量:7
标识
DOI:10.1093/sleep/zsae080
摘要

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 < .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 < .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.
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