Yang Fangxue, Qu Minli, Zhang Youming, Zhao Linmei, Xing Wu, Zhou Gaofeng, Tang Jingyi, Wu Jing, Zhang Yuanchao, Liao Weihua
Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China.
Front Neurosci. 2020 Dec 4;14:585588. doi: 10.3389/fnins.2020.585588. eCollection 2020.
Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN ( = 67) compared to matched healthy controls (HCs; = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.
糖尿病性周围神经病变（DPN）是周围神经病变最常见的形式之一，其发病率一直在上升。越来越多的证据表明，DPN患者与大脑结构、功能和连通性的广泛改变有关，这表明大规模脑网络可能存在改变。我们使用结构协方差网络以及基于先进图论的计算方法，对相对较大样本的DPN患者（n = 67）与匹配的健康对照者（HCs；n = 88）进行比较，研究了大规模脑网络的拓扑异常。与HCs相比，DPN患者的结构协方差网络显示特征路径长度、聚类系数、西格玛、传递性和模块化增加，提示整体整合效率低下和局部隔离增加。这些发现可能从大规模脑结构网络的角度提高我们对DPN患者中枢神经系统改变潜在病理生理机制的理解。
