Functional magnetic resonance imaging in adolescent Internet gaming disorder: A systematic review.

Background: Internet Gaming Disorder (IGD), recognized as a non-substance addictive behavior, has been incorporated into the diagnostic frameworks of DSM-5 and ICD-11. Its high prevalence rate (approximately 10 %) and widespread harmful consequences position it as a significant global public health challenge. Functional Magnetic Resonance Imaging (fMRI) technology offers a powerful tool for elucidating the neural mechanisms underlying addictive behaviors. This is particularly relevant during adolescence, a period characterized by high neurobiological plasticity, enabling a deeper understanding of the neurobiological basis of IGD. Objective: This review aims to systematically synthesize the application of fMRI techniques in adolescent IGD research. It integrates empirical findings from resting-state fMRI (rs-fMRI), task-based fMRI (tb-fMRI), and Diffusion Tensor Imaging (DTI) to clarify IGD-associated abnormalities in brain function and structure, and explores their potential for clinical translation. Methods: Relevant literature published between January 2015 and February 2025 was retrieved from PubMed, Web of Science, and Science Direct databases using keyword searches. Twenty-one studies meeting the inclusion criteria (employing fMRI techniques, utilizing defined IGD diagnostic criteria, focusing on adolescent samples aged 10-20 years) were selected. The risk of bias for included studies was assessed using a standardized tool. The analysis encompassed investigations of resting-state functional connectivity (FC), task-based activation patterns, and white matter microstructure. Conclusion: Adolescents with IGD exhibit functional dysregulation within the prefrontal-striatal circuit, hyperactivation of the reward system, and white matter microstructural impairments. These neural abnormalities are closely associated with behavioral disinhibition and cognitive deficits. fMRI research provides a neuroimaging foundation for the objective diagnosis and targeted intervention of IGD. Future research necessitates the integration of multimodal data to optimize clinical applications.