Objectives: Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes.
Methods: A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters.
Results: The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001).
Conclusions: The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
目的: 糖尿病前期(prediabetes，PD)与认知缺陷及神经退行性疾病的风险增加有关。然而，PD相关脑疾病的机制尚不明确。PD患者的脑灰质和脑白质会发生不同程度的损害，这些神经结构的改变可能会影响大尺度脑网络的组织。灰质协变网络在推断大规模脑结构网络中具有重要价值。但PD是否影响灰质结构网络的拓扑特性尚不清楚。本研究旨在通过图论分析方法探讨PD患者中脑灰质结构协变网络拓扑特征的变化。方法: 本研究纳入48名受试者，包括23名PD患者(PD组)和年龄、性别匹配的25名正常对照(Ctr组)。采用3.0 T磁共振机器采集所有患者的头部高分辨三维T1结构像图像；采用简易智力状态检查量表(Mini-mental State Examination，MMSE)评估患者的认知状态；使用自动解剖标记(anatomical automatic labeling，AAL)模板计算116个脑区的灰质体积，通过区域间结构相关矩阵的阈值处理和图论分析构建灰质结构协方差网络；采用曲线下面积(area under the curve，AUC)联合非参数检验对小世界参数、标准化聚类系数、标准化路径长度、全局效率、特征路径长度及网络稳健性等参数的组间差异进行分析。结果: 两组灰质结构网络均遵循小世界属性，表现为小世界指数大于1，标准化聚类系数远高于1及标准化路径长度接近1。与Ctr组比较，PD组灰质结构网络的小世界指数、标准化路径长度及标准化聚类系数均增加，其中标准化聚类系数在0.12~0.16的网络稀疏度范围内高于Ctr组，但AUC分析显示两组各参数差异均无统计学意义(均P>0.05)。对于网络整合指标，PD组灰质结构网络表现为特征路径长度增加及全局效率降低，其中特征路径长度在0.12~0.16范围内较Ctr组增加，但AUC分析发现二者的组间差异均无统计学意义(均P>0.05)。对于网络分离指标，PD组灰质结构网络表现为局部效率和平均聚类系数显著增高，AUC分析显示PD组的局部效率(P=0.001)和平均聚类系数(P=0.004)显著高于Ctr组。网络稳健性分析结果显示：PD组灰质结构网络较Ctr组更加容易受到随机损害(P=0.001)。结论: PD患者的灰质结构网络较正常对照表现出平均聚类系数及局部效率增加及更容易受到随机损害的特性，提示PD患者的灰质结构协变网络拓扑组织特性发生了改变，表现为网络分离属性增加及网络稳健性降低，这可能为PD患者相关脑损害提供了新证据。.
Keywords: graph theoretical analysis; gray matter volume; prediabetes; structural covariance network; topological measures.