Feng Mengmeng, Zhang Yue, Liu Yuanqing, Wu Zhiwei, Song Ziyang, Ma Mengya, Wang Yueju, Dai Hui
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, China.
Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou City, China.
Front Aging Neurosci. 2021 Mar 31;13:650377. doi: 10.3389/fnagi.2021.650377. eCollection 2021.
To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (E), and local efficiency (E)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the E values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.
为探讨白质结构网络分析在鉴别阿尔茨海默病（AD）和皮质下缺血性血管性痴呆（SIVD）中的价值，本研究纳入了67名参与者[31例AD患者、19例SIVD患者和19名正常对照（NC）]。每位参与者均接受了3.0T磁共振成像（MRI）扫描。采用图论（GRETNA工具箱）分析扩散张量成像（DTI）数据。获得了全局参数[γ、σ、λ、全局最短路径长度（Lp）、全局效率（E）和局部效率（E）]和节点参数[介数中心性（BC）]的统计分析结果。采用基于网络的统计分析（NBS）来分析结构连接的组间差异。通过受试者工作特征（ROC）分析评估节点BC在鉴别不同类型痴呆中的诊断效能。各组间性别和受教育年限无显著差异。AD与NC、SIVD与NC之间的σ和γ无显著差异，而AD和SIVD的E值在统计学上降低，λ值升高。与NC相比，AD患者额叶皮质、左侧顶上叶和左侧楔前叶的BC明显降低，而SIVD患者前额叶和皮质下区域的BC降低。SIVD患者额叶、前额叶和皮质下区域的结构连接减少，而AD患者颞叶和枕叶区域的结构连接减少，额叶和前额叶区域的结构连接增加。在AD与SIVD的鉴别中，BC在右侧壳核的曲线下面积（AUC）最高，为0.946。白质结构网络分析可能是一种有潜力且有前景的方法，网络的拓扑变化，尤其是右侧壳核的BC变化，在鉴别AD和SIVD患者方面具有重要价值。
