Lu Feng-Mei, Liu Chun-Hong, Lu Shun-Li, Tang Li-Rong, Tie Chang-Le, Zhang Juan, Yuan Zhen
Bioimaging Core, Faculty of Health Sciences, University of MacauMacau, China.
Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese MedicineBeijing, China.
Front Neurosci. 2017 Apr 19;11:214. doi: 10.3389/fnins.2017.00214. eCollection 2017.
Insomnia is one of the most common health complaints, with a high prevalence of 30~50% in the general population. In particular, neuroimaging research has revealed that widespread dysfunctions in brain regions involved in hyperarousal are strongly correlated with insomnia. However, whether the topology of the intrinsic connectivity is aberrant in insomnia remains largely unknown. In this study, resting-state functional magnetic resonance imaging (rsfMRI) in conjunction with graph theoretical analysis, was used to construct functional connectivity matrices and to extract the attribute features of the small-world networks in insomnia. We examined the alterations in global and local small-world network properties of the distributed brain regions that are predominantly implicated in the frontostriatal network between 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without insomnia symptoms (NIS). Correlations between the small-world properties and clinical measurements were also generated to identify the differences between the two groups. Both the IS group and the NIS group exhibited a small-worldness topology. Meanwhile, the global topological properties didn't show significant difference between the two groups. By contrast, participants in the IS group showed decreased regional degree and efficiency in the left inferior frontal gyrus (IFG) compared with subjects in the NIS group. More specifically, significantly decreased nodal efficiency in the IFG was found to be negatively associated with insomnia scores, whereas the abnormal changes in nodal betweenness centrality of the right putamen were positively correlated with insomnia scores. Our findings suggested that the aberrant topology of the salience network and frontostriatal connectivity is linked to insomnia, which can serve as an important biomarker for insomnia.
失眠是最常见的健康问题之一，在普通人群中的患病率高达30%至50%。特别是，神经影像学研究表明，与过度觉醒相关的脑区广泛功能障碍与失眠密切相关。然而，失眠患者内在连接的拓扑结构是否异常在很大程度上仍不清楚。在本研究中，静息态功能磁共振成像（rsfMRI）结合图论分析，用于构建功能连接矩阵，并提取失眠患者小世界网络的属性特征。我们检查了30名有失眠症状的健康受试者（IS）和62名无失眠症状的健康受试者（NIS）之间，主要涉及额纹状体网络的分布式脑区的全局和局部小世界网络属性的变化。还生成了小世界属性与临床测量之间的相关性，以识别两组之间的差异。IS组和NIS组均表现出小世界拓扑结构。同时，两组之间的全局拓扑属性没有显著差异。相比之下，与NIS组的受试者相比，IS组的参与者左侧额下回（IFG）的区域度和效率降低。更具体地说，发现IFG中节点效率的显著降低与失眠评分呈负相关，而右侧壳核节点介数中心性的异常变化与失眠评分呈正相关。我们的研究结果表明，突显网络和额纹状体连接的异常拓扑结构与失眠有关，这可以作为失眠的重要生物标志物。
