Zhou Yu1, Ling Tao2, Zhiyu Qian1, Jiangfen Wu1, Hongyi Liu3, Yun Yu1, Jiantai Song1, Shaobo Wang1, Jinyang Sun1. 1. Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao Street, Qinhuai District, Nanjing, 210016, Jiangsu Province, China. 2. Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao Street, Qinhuai District, Nanjing, 210016, Jiangsu Province, China. taoling@nuaa.edu.cn. 3. Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, No. 264, Guangzhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
PURPOSE:Brain tumorpatients 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 tumorpatients based on DTI and provide new insights into the investigation of the structural plasticity and compensatory mechanism of tumorpatient's brain. METHODS: In this study, the brain anatomical networks of tumorpatients 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. RESULTS: Compared with controls, tumorpatients 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, tumorpatients 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. CONCLUSION: The stronger small-world property in the tumorpatients 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.
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