Li Wan, Yang Chunlan, Shi Feng, Wu Shuicai, Wang Qun, Nie Yingnan, Zhang Xin
College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China.
Department of Biomedical Sciences, Cedars-Sinai Medical Center, Biomedical Imaging Research InstituteLos Angeles, CA, USA.
Front Neuroanat. 2017 Apr 25;11:34. doi: 10.3389/fnana.2017.00034. eCollection 2017.
In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test-retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.
近年来，研究人员越来越关注形态学脑网络，该网络通常通过使用特定的形态测量特征（如区域皮质厚度和体素强度）来测量区域间的数学相关性构建而成。然而，脑结构可由多种因素表征，如区域体积、表面积和曲率。此外，大多数形态学脑网络都是基于群体的，这在个体差异研究和临床应用方面存在局限性。因此，我们扩展了先前的研究，提出了一种新方法，通过结合多种形态测量特征来实现基于个体的形态学脑网络构建。具体而言，使用我们新引入的特征向量估计区域间连接，即七个形态测量特征串联的皮尔逊相关系数。对55名健康受试者（24名年龄在20至29岁之间的男性和31名年龄在20至28岁之间的女性）组成的队列进行了实验，每人扫描两次，并通过重测信度评估可重复性。首先测量形态测量特征的稳健性，以选择更具可重复性的特征来形成连接组。然后分析拓扑特性，并与先前不同模态的报告进行比较。在整个网络稀疏度范围（20 - 40%）内，所有受试者均观察到小世界特性，在23%的稀疏度下配置与先前发现相当。发现枢纽的空间分布受个体差异显著影响，通过对受试者和稀疏度进行平均得到的枢纽与先前报告一致。图形属性的组内系数（聚类系数 = 0.83，特征路径长度 = 0.81，介数中心性 = 0.78）表明了本方法的稳健性。结果表明，多种形态测量特征可用于形成合理的、可重复的基于个体的形态学脑网络。
