Yu Zhou, Tao Ling, Qian Zhiyu, Wu Jiangfen, Liu Hongyi, Yu Yun, Song Jiantai, Wang Shaobo, Sun Jinyang
Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao Street, Qinhuai District, Nanjing, 210016, Jiangsu Province, China.
Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, No. 264, Guangzhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
Int J Comput Assist Radiol Surg. 2016 Nov;11(11):2007-2019. doi: 10.1007/s11548-015-1330-y. Epub 2016 Feb 25.
Brain tumor patients are usually accompanied by impairments in cognitive functions, and these dysfunctions arise from the altered diffusion tensor of water molecules and disrupted neuronal conduction in white matter. Diffusion tensor imaging (DTI) is a powerful noninvasive imaging technique that can reflect diffusion anisotropy of water and brain white matter neural connectivity in vivo. This study was aimed to analyze the topological properties and connection densities of the brain anatomical networks in brain tumor patients based on DTI and provide new insights into the investigation of the structural plasticity and compensatory mechanism of tumor patient's brain.
In this study, the brain anatomical networks of tumor patients and healthy controls were constructed using the tracking of white matter fiber bundles based on DTI and the topological properties of these networks were described quantitatively. The statistical comparisons were performed between two groups with six DTI parameters: degree, regional efficiency, local efficiency, clustering coefficient, vulnerability, and betweenness centrality. In order to localize changes in structural connectivity to specific brain regions, a network-based statistic approach was utilized. By comparing the edge connection density of brain network between two groups, the edges with greater difference in connection density were associated with three functional systems.
Compared with controls, tumor patients show a significant increase in small-world feature of cerebral structural network. Two-sample two-tailed t test indicates that the regional properties are altered in 17 regions ([Formula: see text]). Study reveals that the positive and negative changes in vulnerability take place in the 14 brain areas. In addition, tumor patients lose 3 hub regions and add 2 new hubs when compared to normal controls. Eleven edges show much significantly greater connection density in the patients than in the controls. Most of the edges with greater connection density are linked to regions located in the limbic/subcortical and other systems. Besides, most of the edges connect the two hemispheres of the brains.
The stronger small-world property in the tumor patients proves the existence of compensatory mechanism. The changes in the regional properties, especially the betweenness centrality and vulnerability, aid in understanding the brain structural plasticity. The increased connection density in the tumor group suggests that tumors may induce reorganization in the structural network.
脑肿瘤患者通常伴有认知功能障碍，这些功能障碍源于水分子扩散张量的改变和白质中神经元传导的破坏。扩散张量成像（DTI）是一种强大的无创成像技术，可在体内反映水的扩散各向异性和脑白质神经连通性。本研究旨在基于DTI分析脑肿瘤患者脑解剖网络的拓扑特性和连接密度，为肿瘤患者脑结构可塑性和代偿机制的研究提供新见解。
在本研究中，基于DTI通过白质纤维束追踪构建肿瘤患者和健康对照的脑解剖网络，并定量描述这些网络的拓扑特性。使用六个DTI参数（度、区域效率、局部效率、聚类系数、脆弱性和介数中心性）在两组之间进行统计比较。为了将结构连通性的变化定位到特定脑区，采用了基于网络的统计方法。通过比较两组脑网络的边连接密度，连接密度差异较大的边与三个功能系统相关。
与对照组相比，肿瘤患者脑结构网络的小世界特征显著增加。双样本双尾t检验表明17个区域（[公式：见正文]）的区域特性发生改变。研究发现14个脑区的脆弱性出现正负变化。此外，与正常对照组相比，肿瘤患者失去3个枢纽区域并增加2个新的枢纽区域。11条边在患者中的连接密度明显高于对照组。连接密度较大的边大多与位于边缘/皮质下和其他系统的区域相连。此外，大多数边连接大脑的两个半球。
肿瘤患者较强的小世界特性证明了代偿机制的存在。区域特性的变化，尤其是介数中心性和脆弱性的变化，有助于理解脑结构可塑性。肿瘤组连接密度的增加表明肿瘤可能诱导结构网络的重组。
