Background: Internet gaming disorder (IGD) has become a worldwide mental health concern; however, the neural mechanism underlying this disorder remains unclear. Multivoxel pattern analysis (MVPA), a newly developed data-driven approach, can be used to investigate the neural features of IGD based on massive neural data. Methods: Resting-state functional magnetic resonance imaging data from four hundred and two participants with varying levels of IGD severity were recruited. Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were calculated and subsequently decoded by applying MVPA. The highly weighted regions in both predictive models were selected as regions of interest (ROIs) for further graph theory and Granger causality analysis (GCA) to explore how they affect IGD severity. Results: The results revealed that the neural patterns of ReHo and ALFF can independently and significantly predict IGD severity. The highly weighted brain regions that contributed to both predictive models were the right precentral gyrus and the left postcentral gyrus. Moreover, the topological properties of the right precentral gyrus were significantly correlated with IGD severity; further GCA analyses revealed effective connectivity from the right precentral gyrus to the left precentral gyrus and the dorsal anterior cingulate cortex, both of which were significantly associated with IGD severity. Conclusions: The present study demonstrated that IGD has distinctive neural patterns, and this pattern could be found by machine learning. In addition, the neural features in the right precentral gyrus play a key role in predicting IGD severity. The current study revealed the neural features of IGD and provided a potential target for IGD interventions using brain modulation.
bioRxiv Subject Collection: Neuroscience