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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. Accordingly, 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 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-default mode network 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. [ABSTRACT FROM AUTHOR]