Imaging Department, Yantaishan Hospital, Yantai, China.
Intelligence Control System, Yantai Vocational College, Yantai, China.
Sci Rep. 2024 Nov 28;14(1):29644. doi: 10.1038/s41598-024-80731-5.
The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher's Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.
采用图论和基于网络的统计学（NBS）方法，研究了基于分形维数（FD）和脑网络连接变化的结构协变网络（SCN）的拓扑属性，探讨了噪声性听力损失（NIHL）患者的 SCN。对 40 名 NIHL 患者和 38 名健康对照（HC）的高分辨率 3D T1 图像进行了分析。计算了 FD 基于 Pearson 相关系数，并将其转换为 Fisher Z 值以构建 SCN。使用图论计算拓扑属性和网络枢纽。使用非参数置换检验比较组间拓扑度量。使用 NBS 分析识别异常连接网络。NIHL 组右侧内侧眶额回的归一化聚类系数、归一化特征路径长度增加，节点效率降低。此外，NIHL 组基于介数中心度和度数中心度的网络枢纽都是右侧横颞回和左侧海马旁回。NBS 分析显示有两个具有异常连接的子网。增强连接的子网主要分布在默认模式、额顶叶、背侧注意和躯体运动网络中，而连接减少的子网主要分布在边缘、视觉、默认模式和听觉网络中。这些发现表明 NIHL 患者 FD 基 SCN 的异常拓扑结构，这可能有助于理解网络水平上大脑损伤的复杂机制，为神经病理学机制提供新的理论基础。
