Diagnostic Contribution of the DSM-5 Criteria for Internet Gaming Disorder

Background: Internet gaming disorder (IGD) can have long-term severe consequences in affected individuals, especially adolescents and young people. Empirical studies of IGD using the DSM-5 criteria are still lacking. This study aimed to evaluate the contribution of specific criteria to the diagnosis of IGD based on the DSM-5 in the context of Chinese culture.Methods: The Chinese version of the Internet Gaming Disorder Scale–Short Form (IGDS9-SF) was applied to investigate the prevalence of IGD in a general sample of 28,689 middle school students aged 12–19 years from two cities in China.Results: The prevalence of IGD was 4.6% among this adolescent sample. The group of IGD students reported longer weekly gaming times and worse academic performance than the group of non-IGD students. Although “preoccupation” and “playing to escape” were the most frequently reported criteria, the conditional inference trees showed that “give up other activities,” ‘negative consequences,” and “continue despite problems” contributed most to the diagnosis of IGD based on the DSM-5.Conclusions: The prevalence of IGD among Chinese adolescents (ages 12–19) was 4.6%. This study provides evidence for retaining or deleting specific diagnostic criteria by the DSM framework in the future.

Evidence on Problematic Online Gaming and Social Anxiety over the Past Ten Years: a Systematic Literature Review

Purpose of Review The present study aimed to review the literature concerning the relationship between problematic online gaming (POG) and social anxiety, taking into account the variables implicated in this relationship. This review included studies published between 2010 and 2020 that were indexed in major databases with the following keywords: Internet gaming, disorder, addiction, problematic, social phobia, and social anxiety. Recent Findings In recent years, scientific interest in POG has grown dramatically. Within this prolific research field, difficulties associated with social anxiety have been increasingly explored in relation to POG. Indeed, evidence showed that individuals who experience social anxiety are more exposed to the risk of developing an excessive or addictive gaming behavior. Summary A total of 30 studies satisfied the initial inclusion criteria and were included in the present literature review. Several reviewed studies found a strong association between social anxiety and online gaming disorder. Furthermore, the relationships among social anxiety, POG, age, and psychosocial and comorbid factors were largely explored. Overall, the present review showed that socially anxious individuals might perceive online video games as safer social environments than face-to-face interactions, predisposing individuals to the POG. However, in a mutually reinforcing relationship, individuals with higher POG seem to show higher social anxiety. Therefore, despite online gaming might represent an activity able to alleviate psychopathological symptoms and/or negative emotional states, people might use online gaming to counterbalance distress or negative situations in everyday life, carrying out a maladaptive coping strategy.

MRI-Based Radiomic Machine-Learning Model May Accurately Distinguish between Subjects with Internet Gaming Disorder and Healthy Controls

Purpose To identify cerebral radiomic features related to the diagnosis of Internet gaming disorder (IGD) and construct a radiomics-based machine-learning model for IGD diagnosis. Methods A total of 59 treatment-naïve subjects with IGD and 69 age- and sex-matched healthy controls (HCs) were recruited and underwent anatomic and diffusion-tensor magnetic resonance imaging (MRI). The features of the morphometric properties of gray matter and diffusion properties of white matter were extracted for each participant. After excluding the noise feature with single-factor analysis of variance, the remaining 179 features were included in an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power. Random forest classifiers were constructed and evaluated based on the identified features. Results No overall differences in the total brain volume (1,555,295.64 ± 152316.31 mm3 vs. 154,491.19 ± 151,241.11 mm3), total gray (709,119.83 ± 59,534.46 mm3 vs. 751,018.21 ± 58,611.32 mm3) and white (465,054.49 ± 51,862.65 mm3 vs. 470,600.22 ± 47,006.67 mm3) matter volumes, and subcortical region volume (63,882.71 ± 5110.42 mm3 vs. 64,764.36 ± 4332.33 mm3) between the IGD and HC groups were observed. The mean classification accuracy was 73%. An altered cortical shape in the bilateral fusiform, left rostral middle frontal (rMFG), left cuneus, left parsopercularis (IFG), and regions around the right uncinate fasciculus (UF) and left internal capsule (IC) contributed significantly to group discrimination. Conclusions: Our study found the brain morphology alterations between IGD subjects and HCs through a radiomics-based machine-learning method, which may help revealing underlying IGD-related neurobiology mechanisms.