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重要网络中枢的连接属性改变可能是原发性失眠患者的神经风险因素。

Altered connection properties of important network hubs may be neural risk factors for individuals with primary insomnia.

机构信息

Department of Radiology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, 325000, Zhejiang, People's Republic of China.

Department of Medical Imaging, The affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, 223300, Jiangsu, People's Republic of China.

出版信息

Sci Rep. 2018 Apr 12;8(1):5891. doi: 10.1038/s41598-018-23699-3.

Abstract

Primary insomnia (PIs) is highly prevalent and can lead to adverse socioeconomic impacts, but the underlying mechanism of its complex brain network impairment remains largely unknown. Functional studies are too few and diverse in methodology, which makes it difficult to glean general conclusions. To answer this question, we first used graph theory-based network analyse, together with seed-based functional connectivity approach, to characterize the topology architecture of whole-brain functional networks associated with PIs. Forty-eight subjects with PIs and 48 age/sex/education-matched good sleepers were recruited. We found PIs is associated with altered connection properties of intra-networks within the executive control network, default mode network and salience network, and inter-network between auditory language comprehension center and executive control network. These complex networks were correlated with negative emotions and insomnia severity in the PIs group. Altered connection properties of these network hubs appeared to be neural risk factors for neuropsychological changes of PIs, and might be used as potential neuroimaging markers to distinguish the PIs from the good sleepers. These findings highlight the role of functional connectivity in the pathophysiology of PIs, and may underlie the neural mechanisms of etiology of PIs.

摘要

原发性失眠(PI)患病率较高,可导致不良的社会经济影响,但其复杂的大脑网络损伤的潜在机制在很大程度上仍不清楚。功能研究方法太少且多样化,难以得出普遍结论。为了回答这个问题,我们首先使用基于图论的网络分析,以及基于种子的功能连接方法,来描述与 PI 相关的全脑功能网络的拓扑结构。共招募了 48 名 PI 患者和 48 名年龄/性别/教育程度相匹配的良好睡眠者。我们发现 PI 与执行控制网络、默认模式网络和突显网络内的网络内连接特性以及听觉语言理解中心和执行控制网络之间的网络间连接特性的改变有关。这些复杂的网络与 PI 组的负面情绪和失眠严重程度相关。这些网络枢纽连接特性的改变似乎是 PI 神经心理变化的神经风险因素,可作为潜在的神经影像学标志物,将 PI 与良好睡眠者区分开来。这些发现强调了功能连接在 PI 病理生理学中的作用,并可能为 PI 的病因学的神经机制提供依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c8d/5897381/cf27898067e1/41598_2018_23699_Fig1_HTML.jpg

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