• Jinquan Zhai, Lin Luo, Lijun Qiu, Yongqiang Kang, Bo Liu, Dahua Yu, Xiaoqi Lu, and Kai Yuan.
• Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, People's Republic of China.
• Brain Imaging Behav. 2017 Dec 1; 11 (6): 1769-1778.

AbstractWhite matter (WM) integrity abnormalities had been reported in Internet gaming disorder (IGD). Diffusion tensor imaging (DTI) tractography allows identification of WM tracts, potentially providing information about the integrity and organization of relevant underlying WM fiber tracts' architectures, which has been used to investigate the connectivity of cortical and subcortical structures in several brain disorders. Unfortunately, relatively little is known about the thoroughly circuit-level characterization of topological property changes of WM network with IGD. Sixteen right-hand adolescents with IGD participated in our study, according to the diagnostic criteria of IGD in DSM-5. Meanwhile, 16 age and gender-matched healthy controls were also enrolled. DTI tractography was employed to generate brain WM networks in IGD individuals and healthy controls. The 90 cortical and subcortical regions derived from AAL template were chosen as the nodes. The network parameters (i.e., Network strength, clustering coefficient, shortest path length, global efficiency, local efficiency, regional efficiency) were calculated and then correlated with the Internet addiction test (IAT) scores in IGD. IGD group showed decreased global efficiency, local efficiency and increased shortest path length. Further analysis revealed the reduced nodal efficiency in frontal cortex, anterior cingulate cortex and pallidium in IGD. In addition, the global efficiency of WM network was correlated with the IAT scores in IGD (r = -0.5927; p = 0.0155). We reported the abnormal topological organization of WM network in IGD and the association with the severity of IGD, which may provide new insights into the neural mechanism of IGD from WM network level.

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