Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder

被引:10
作者
Wang, Lingxiao [1 ,2 ,3 ,6 ]
Zhang, Zhengjie [1 ,2 ,3 ]
Wang, Shizhen [1 ,2 ,3 ]
Wang, Min [1 ,4 ]
Dong, Haohao [1 ]
Chen, Shuaiyu [1 ,2 ,3 ]
Du, Xiaoxia [5 ]
Dong, Guang-Heng [1 ,2 ,3 ,6 ]
机构
[1] Affiliated Hosp Hangzhou Normal Univ, Ctr Cognit & Brain disorders, Hangzhou, Zhejiang, Peoples R China
[2] Hangzhou Normal Univ, Inst Psychol Sci, Hangzhou, Zhejiang, Peoples R China
[3] Zhejiang Key Lab Res Assessment Cognit Impairments, Hangzhou, Zhejiang, Peoples R China
[4] Univ Sci & Technol China, Sch Humanities & Social Sci, Dept Psychol, Hefei, Anhui, Peoples R China
[5] Shanghai Univ Sport, Sch Psychol, Shanghai, Peoples R China
[6] Hangzhou Normal Univ, Ctr Cognit & Brain Disorders, 2318 Yuhangtang Rd, Hangzhou 311121, Zhejiang, Peoples R China
关键词
Internet gaming disorder; Co -activation pattern; Machine learning; Prefrontal-striatal network; Striatal-default mode network; STATE FUNCTIONAL CONNECTIVITY; COGNITIVE CONTROL; FRONTOSTRIATAL CIRCUITS; NUCLEUS-ACCUMBENS; CUE-REACTIVITY; BRAIN; ADDICTION; ACTIVATION; CRAVINGS; REWARD;
D O I
10.1016/j.jad.2022.11.074
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Studies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accord-ingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now. Methods: Resting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/ 50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity.Results: Compared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity.Limitations: The causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students.Conclusions: The present results revealed the underlying brain networks of uncontrollable craving and game -seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.
引用
收藏
页码:336 / 344
页数:9
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