AbstractImportanceFinding the neural features that could predict internet gaming disorder severity is important in finding the targets for potential interventions using brain modulation methods.ObjectiveTo determine whether resting-state neural patterns can predict individual variations of internet gaming disorder by applying machine learning method and further investigate brain regions strongly related to IGD severity.DesignThe diagnostic study lasted from December 1, 2013, to November 20, 2019. The data were analyzed from December 31, 2019, to July 10, 2020.SettingThe resting-state fMRI data were collected at East China Normal University, Shanghai.ParticipantsA convenience sample consisting of 402 college students with diverse IGD severityMain Outcomes and MeasuresThe neural patterns were represented by regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF). Predictive model performance was assessed by Pearson correlation coefficient and standard mean squared error between the predicted and true IGD severity. The correlations between IGD severity and topological features (i.e., degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE)) of consensus highly weighted regions in predictive models were examined.ResultsThe final dataset consists of 402 college students (mean [SD] age, 21.43 [2.44] years; 239 [59.5%] male). The predictive models could significantly predict IGD severity (model based on ReHo: r = 0.11, p(r) = 0.030, SMSE = 3.73, p(SMSE) = 0.033; model based on ALFF: r=0.19, p(r) = 0.002, SMSE = 3.58, p(SMSE) = 0.002). 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 (DC: r = 0.16, p = 0.001; BC: r = 0.14, p = 0.005; NE: r = 0.15, p = 0.003) whereas no significant result was found for the left postcentral gyrus (DC: r = 0.02, p = 0.673; BC: r = 0.04, p = 0.432; NE: r = 0.02, p = 0.664).Conclusions and RelevanceThe machine learning models could significantly predict IGD severity from resting-state neural patterns at the individual level. The predictions of IGD severity deepen our understanding of the neural mechanism of IGD and have implications for clinical diagnosis of IGD. In addition, we propose precentral gyrus as a potential target for physiological treatment interventions for IGD.Key PointsQuestionCan machine learning algorithms predict internet gaming disorder (IGD) from resting-state neural patterns?FindingsThis diagnostic study collected resting-state fMRI data from 402 subjects with diverse IGD severity. We found that machine learning models based on resting-state neural patterns yielded significant predictions of IGD severity. In addition, the topological neural features of precentral gyrus, which is a consensus highly weighted region, is significantly correlated with IGD severity.MeaningThe study found that IGD is a distinctive disorder and its dependence severity could be predicted by brain features. The precentral gyrus and its connection with other brain regions could be view as targets for potential IGD intervention, especially using brain modulation methods.
Altered resting-state neural activity and changes following a craving behavioral intervention for Internet gaming disorder