Ren Jinxia, Xu Dan, Mei Hao, Zhong Xiaoli, Yu Minhua, Ma Jiaojiao, Fan Chenhong, Lv Jinfeng, Xiao Yaqiong, Gao Lei, Xu Haibo
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Front Aging Neurosci. 2023 Jan 12;14:1091829. doi: 10.3389/fnagi.2022.1091829. eCollection 2022.
Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS).
Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms.
In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests.
We conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.
无症状性颈动脉狭窄患者，即使未发生中风，也存在认知障碍的高风险，其神经解剖学基础仍不清楚。我们使用一种基于个体单受试者皮质厚度网络的新型以边缘为中心的结构连接性（eSC）分析方法，旨在研究严重（>70%）无症状性颈动脉狭窄（SACS）患者的eSC和网络指标。
纳入24例SACS患者和24例人口统计学及合并症匹配的对照，采集结构MRI和多领域认知数据。通过成对皮质厚度直方图的曼哈顿距离估计个体eSC。
在eSC分析中，SACS患者从左侧颞叶区域起始的半球间曼哈顿距离较长，但半球内曼哈顿距离较短；在网络分析中，SACS患者的系统分离度降低，与白质高信号负荷和回忆记忆相关。进一步的基于网络的统计分析确定了几个以岛周区域为中心的eSC和子图特征，这些特征可预测无症状性病变负荷和认知测试结果。
我们得出结论，SACS表现出异常的eSC以及在物理成本和网络分离之间不太优化的权衡，为识别高危个体提供了参考和视角。
