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Understanding the neural mechanisms underlying Internet gaming disorder (IGD) is essential for the condition's diagnosis and treatment. Nevertheless, the pathological mechanisms of IGD remain elusive at present. Hence, we employed multi-voxel pattern analysis (MVPA) and spectral dynamic causal modeling (spDCM) to explore this issue. Resting-state fMRI data were collected from 103 IGD subjects (male = 57) and 99 well-matched recreational game users (RGUs, male = 51). Regional homogeneity was calculated as the feature for MVPA based on the support vector machine (SVM) with leave-one- out cross-validation. Mean time series data extracted from the brain regions in accordance with the MVPA results were used for further spDCM analysis. Results display a high accuracy of 82.67% (sensitivity of 83.50% and specificity of 81.82%) in the classification of the two groups. The most discriminative brain regions that contributed to the classification were the bilateral parahippocampal gyrus (PG), right anterior cingulate cortex (ACC), and middle frontal gyrus (MFG). Significant correlations were found between addiction severity (IAT and DSM scores) and the ReHo values of the brain regions that contributed to the classification. Moreover, the results of spDCM showed that compared with RGU, IGD showed decreased effective connectivity from the left PG to the right MFG and from the right PG to the ACC and decreased self-connection in the right PG. These results show that the weakening of the PG and its connection with the prefrontal cortex, including the ACC and MFG, may be an underlying mechanism of IGD. [ABSTRACT FROM AUTHOR]

Cue-induced brain reactivity has been suggested to be a fundamental and important mechanism explaining the development, maintenance, and relapse of addiction, including Internet gaming disorder (IGD). Altered activity in addiction-related brain regions has been found during cue-reactivity in IGD using functional magnetic resonance imaging (fMRI), but less is known regarding the alterations of coordinated whole brain activity patterns in IGD. To investigate the activity of temporally coherent, large-scale functional brain networks (FNs) during cue-reactivity in IGD, independent component analysis was applied to fMRI data from 29 male subjects with IGD and 23 matched healthy controls (HC) performing a cue-reactivity task involving Internet gaming stimuli (i.e., game cues) and general Internet surfing-related stimuli (i.e., control cues). Four FNs were identified that were related to the response to game cues relative to control cues and that showed altered engagement/disengagement in IGD compared with HC. These FNs included temporo-occipital and temporo-insula networks associated with sensory processing, a frontoparietal network involved in memory and executive functioning, and a dorsal-limbic network implicated in reward and motivation processing. Within IGD, game versus control engagement of the temporo-occipital and frontoparietal networks were positively correlated with IGD severity. Similarly, disengagement of temporo-insula network was negatively correlated with higher game-craving. These findings are consistent with altered cue-reactivity brain regions reported in substance-related addictions, providing evidence that IGD may represent a type of addiction. The identification of the networks might shed light on the mechanisms of the cue-induced craving and addictive Internet gaming behaviors. [ABSTRACT FROM AUTHOR]

Background and Aims: Compulsivity contributes to the development and maintenance of multiple addictive disorders. However, the relationship between compulsivity-related cognitive features and problematic usage of the internet (PUI), an umbrella term for various internet use disorders/interfering behaviors, remains largely unclear, partly due to the multidimensional nature of compulsivity. This scoping review utilized a four-domain framework of compulsivity to consider this topic and aimed to summarize available evidence on compulsivity-related neuropsychological characteristics in PUI based on this framework., Methods: A systematic literature search was conducted by applying the combination of search term to the search engines of PubMed, PsycINFO and Web of Science. A four-domain framework of compulsivity, involving cognitive flexibility, set-shifting, attentional bias, and habit learning, was used to consider its complex structure and frequently used tasks. Main findings in related PUI studies were summarized based on this framework. Our secondary aim was to compare compulsivity-related features between different PUI subtypes., Results: Thirty-four empirical studies were retained, comprising 41 task-results and 35 independent data sets. Overall, individuals with PUI showed more consistent deficits in attentional biases and were relatively intact in set-shifting. Few studies have examined cognitive flexibility and habit learning, and more evidence is thus needed to establish reliable conclusions. Moreover, most studies focused on internet gaming disorder, whereas other PUI sub-types were not sufficiently examined., Conclusion: This systematic review highlights the use of the four-domain framework for advancing understanding of mechanisms underlying compulsivity in PUI. Related therapeutic implications and future directions are discussed.

Background and Aims: Deficits in cognitive control represent a core feature of addiction. Internet Gaming Disorder (IGD) offers an ideal model to study the mechanisms underlying cognitive control deficits in addiction, eliminating the confounding effects of substance use. Studies have reported behavioral and neural deficits in reactive control in IGD, but it remains unclear whether individuals with IGD are compromised in proactive control or behavioral adjustment by learning from the changing contexts., Methods: Here, fMRI data of 21 male young adults with IGD and 21 matched healthy controls (HC) were collected during a stop-signal task. We employed group independent component analysis to investigate group differences in temporally coherent, large-scale functional network activities during post-error slowing, the typical type of behavioral adjustments. We also employed a Bayesian belief model to quantify the trial-by-trial learning of the likelihood of stop signal - P(Stop) - a broader process underlying behavioral adjustment, and identified the alterations in functional network responses to P(Stop)., Results: The results showed diminished engagement of the fronto-parietal network during post-error slowing, and weaker activity in the ventral attention and anterior default mode network in response to P(Stop) in IGD relative to HC., Discussion and Conclusions: These results add to the literatures by suggesting deficits in updating and anticipating conflicts as well as in behavioral adjustment according to contextual information in individuals with IGD.