Cheng Yongxin, Xue Ting, Dong Fang, Hu Yiting, Zhou Mi, Li Xiaojian, Huang Ruoyan, Lu Xiaoqi, Yuan Kai, Yu Dahua
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
School of Science, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Brain Imaging Behav. 2022 Apr;16(2):930-938. doi: 10.1007/s11682-021-00567-9. Epub 2021 Oct 23.
The salience network plays an important role in detecting stimuli related to behavior and integrating neural processes. The aim of this study was to investigate changes in functional connectivity of the salience network in insomnia patients. Independent component analysis combined with a dual regression approach was used to examine functional connectivity differences in the salience network between patients with insomnia (n = 33) and healthy controls (n = 33). Pearson correlation analysis was used to analyze the relationship between differences in functional connectivity and the clinical characteristics of insomnia patients. Compared to healthy controls, insomnia patients showed increased functional connectivity in the dorsal anterior cingulate cortex within the salience network, as well as greater connectivity between the salience network and other brain regions including the dorsolateral prefrontal cortex, superior frontal gyrus, sensorimotor area and brain stem. The correlation analysis showed that increased functional connectivity between the salience network and left dorsolateral prefrontal cortex was positively correlated with Pittsburgh Sleep Quality Index score. Increased functional connectivity between salience network and several brain regions may be related to hyperarousal in insomnia patients. The connectivity between salience network and dorsolateral prefrontal cortex may potentially be used as a neuroimaging biomarker of sleep quality.
突显网络在检测与行为相关的刺激以及整合神经过程中发挥着重要作用。本研究的目的是调查失眠患者突显网络功能连接性的变化。采用独立成分分析结合双回归方法，检查失眠患者（n = 33）和健康对照者（n = 33）之间突显网络的功能连接差异。使用Pearson相关分析来分析功能连接差异与失眠患者临床特征之间的关系。与健康对照者相比，失眠患者在突显网络内的背侧前扣带回皮质中功能连接增加，并且突显网络与其他脑区（包括背外侧前额叶皮质、额上回、感觉运动区和脑干）之间的连接性更强。相关分析表明，突显网络与左侧背外侧前额叶皮质之间功能连接的增加与匹兹堡睡眠质量指数量表得分呈正相关。突显网络与几个脑区之间功能连接的增加可能与失眠患者的过度觉醒有关。突显网络与背外侧前额叶皮质之间的连接性可能潜在地用作睡眠质量的神经影像学生物标志物。
