School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
Sleep. 2024 Jul 11;47(7). doi: 10.1093/sleep/zsae080.
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.
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.
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.
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.
本研究旨在探讨慢性失眠障碍（CID）患者静息态脑电图（EEG）全局脑连接（GBC）的变化，并探讨宏观连接变异性与微观神经递质分布之间的相关性。
我们从 35 名女性 CID 患者和 34 名健康女性中采集了 64 通道 EEG。使用个体脑解剖结构对 EEG 信号进行源定位，并进行正交化以减轻容积传导的影响。计算 DK 68 图谱带限源空间功率包络之间的相关系数，并在区域之间平均，以确定特定的 GBC 值。使用支持向量机（SVM）分类器利用 GBC 特征区分 CID 患者和对照组。我们进一步使用 Neurosynth 和神经递质受体/转运体的 3D 图谱，分别评估与 CID 皮质异常图谱相关的认知功能和神经递质景观。
与对照组相比，CID 患者在内侧前额叶皮层和边缘皮层的 GBC 升高，特别是在伽马载波频率下（pFDR <.05）。SVM 模型发现 GBC 模式能够有效地区分 CID 患者和对照组，准确率为 90.8%。皮质异常图谱与“认知控制”和“情绪调节”等元分析术语显著相关。值得注意的是，GBC 模式与神经递质分布有关（pspin <.05），去甲肾上腺素、多巴胺和血清素等神经递质系统有显著贡献。
本研究描述了 CID 的 EEG 连接组学特征，为 EEG 衍生标志物的经济有效的临床转化提供了便利。此外，GBC 模式与神经递质分布之间的联系为 CID 的靶向治疗策略的发展提供了有前途的途径。
