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Alterations in brain connectivity have been implicated in internet gaming disorder (IGD). However, little is known about alterations in whole-brain connectivity and their associations with long-term treatment outcomes. Here, we used a relatively new analytic approach, intrinsic connectivity distribution (ICD) analysis, to examine brain connectivity in 74 IGD participants and 41 matched healthy controls (HCs) and conducted post hoc seed-based resting-state functional connectivity (rsFC) analyses based on the ICD findings. We also examined how these findings related to outcomes involving a craving behavioral intervention (CBI) for IGD. IGD participants showed less whole-brain connectivity in the left angular gyrus and ventromedial prefrontal cortex (vmPFC) compared with HC participants. Seed-based rsFC analyses revealed that the left angular gyrus in the IGD group showed less connectivity with areas involved in the default-mode network and greater connectivity with areas in the salience and executive control networks. CBI was associated with improved connectivity within regions in the default-mode network and regions across the default-mode and salience networks. ICD-identified connectivity differences in the left angular gyrus and vmPFC were related to changes in craving and severity of addiction 6 months after the intervention. The findings suggest that IGD is associated with alterations in brain connectivity that may be sensitive to interventions. Thus, the findings have implications for understanding mechanisms underlying CBI effects and for further treatment development. [ABSTRACT FROM AUTHOR]

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting‐state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome‐based predictive modeling (CPM)—a recently developed machine‐learning approach—has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting‐state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting‐state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole‐brain and network‐based analyses showed that the default‐mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting‐state activity in the DMN, the identified networks have been mapped into a three‐subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes. [ABSTRACT FROM AUTHOR]