The topological organization of white matter network in internet gaming disorder individuals

Brain structural covariance network features are robust markers of early heavy alcohol use

BACKGROUND AND AIMS

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

DESIGN AND SETTING

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

CASES

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

MEASUREMENTS

Graph theory metrics of segregation and integration were used to summarize SCN.

FINDINGS

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

CONCLUSION

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

White matter microstructural changes in internet addiction disorder: A systematic review of diffusion tensor imaging studies

Diffusion tensor imaging (DTI) is a kind of magnetic resonance imaging (MRI) modality that helps designate tracts with brain microstructural changes. Internet gaming disorder (IGD) is an internet addiction that can cause many social and personality problems, such as problems in social communication, anxiety, and depression. There are several pieces of evidence showing the impact of this condition on brain regions, and many studies have investigated DTI measurements in these individuals. Therefore, we decided to systematically review the studies that have reported DTI parameters in IGD individuals. We searched the PubMed and Scopus databases to find relevant articles. Two reviewers separately screened the studies, and finally, 14 articles, including diffusion and network studies, were found eligible for our systematic review. Most of the studies reported findings on FA, showing an increase in the thalamus, anterior thalamic radiation, corticospinal tract, and inferior longitudinal fasciculus (ILF), while other regions mentioned in the studies demonstrated inconsistent findings. Moreover, in network studies, IGD individuals showed a decrease in nodal and global efficiencies. In conclusion, our study illuminates the neuropsychological basis of this condition and suggests that internet gaming can correlate with microstructural abnormalities in the central nervous system. Some correlate with the characteristics of online gaming, the addiction state, and the illness's duration.

Graph analysis of diffusion tensor imaging-based connectome in young men with internet gaming disorder

Although recent evidence suggests that dysfunctional brain organization is associated with internet gaming disorder (IGD), the neuroanatomical alterations related to IGD remain unclear. In this diffusion tensor imaging (DTI) study, we aimed to examine alterations in white matter (WM) structural connectomes and their association with IGD characteristics in 47 young men with IGD and in 34 well-matched healthy controls. Two approaches [namely, network-based statistics (NBS) and graph theoretical measures] were applied to assess differences in the specific topological features of the networks and to identify the potential changes in the topological properties, respectively. Furthermore, we explored the association between the alterations and the severity of internet addiction. An NBS analysis revealed widespread alterations of the cortico-limbic-striatal structural connectivity networks in young people with IGD: (1) an increased subnet1 comprising the insula and the regions responsible for visual, auditory, and sensorimotor functions and (2) two decreased subnet2 and subnet3 comprising the insula, striatum, and limbic regions. Additional correlation analysis showed a significant positive association between the mean fractional anisotropy- (FA-) weighted connectivity strength of subnet1 and internet addiction test (IAT) scores in the IGD group. The present study extends our knowledge of the neuroanatomical correlates in IGD and highlights the role of the cortico-limbic-striatal network in understanding the neurobiological mechanisms underlying this disorder.

Reciprocal modulation between cigarette smoking and internet gaming disorder on participation coefficient within functional brain networks

Many reports indicated that cigarette smoking was associated with internet gaming disorder (IGD). However, the underlying mechanism of comorbidity between smoking and IGD and whether they had interaction effects on topological organization of brain functional network are still unknown. Therefore, we investigated the interaction between smoking and IGD in resting-state brain functional networks for 60 healthy controls, 46 smokers, 38 IGD individuals and 34 IGD comorbid with smoking participants. The modular structures of functional networks were explored and participation coefficient (Pc) was used to characterize the importance of each brain region in the communication between modules. Significant main effect of IGD was found in the left superior frontal gyrus, bilateral medial part of superior frontal gyrus and bilateral posterior cingulate gyrus with lower Pc in IGD group than in non-IGD group. Significant interaction effects between smoking and IGD were found in the left posterior orbital gyrus, right lateral orbital gyrus, left supramarginal gyrus, left middle temporal gyrus and left inferior temporal gyrus. The interaction in these brain regions was characterized by no significant difference or significantly decreased Pc in smokers or IGD individuals while significantly increased Pc in IGD comorbid with smoking group under the influence of IGD or smoking. Our findings provide valuable information underlying the neurophysiological mechanisms of smoking and IGD, and also offer a potential target for future clinical treatment of smoking and IGD comorbidity.

Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents

BACKGROUND AND AIMS

Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol.

DESIGN

Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics.

SETTING AND PARTICIPANTS

A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe.

MEASUREMENTS

Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency).

FINDINGS

The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048).

CONCLUSIONS

Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

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 ± 152,316.31 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.

Factors related to the dual condition of gambling and gaming disorders: A path analysis model

BACKGROUND AND AIMS

Gaming disorder has experienced rapid growth in the last decade among youth and adult populations, in parallel to the expansion of the videogame industry. The objective of this study was to explore the underlying process to explain the dual diagnosis of gaming with gambling disorder.

METHODS

The sample included n = 117 patients who met clinical criteria for gaming disorder, recruited from a tertiary care unit specialized in the treatment of behavioral addictions. Path analysis (implemented through structural equation modeling) assessed the direct and mediational mechanisms between the dual condition of gaming + gambling disorder and sociodemographic variables and personality traits.

RESULTS

The comorbid gaming + gambling disorder was met for 14.5% of the participants (additionally, 6.0% of the sample also met criteria for problematic gambling). The dual diagnosis was directly related to an older age at onset of the addiction problems, a higher level of the novelty seeking trait and being in active work. Employment status also mediated the relationship between persistence levels and chronological age. Greater psychopathological distress was related to females, higher levels of harm avoidance and persistence and lower levels of self-directedness.

CONCLUSIONS

The results of this study provide empirical evidence for the specific factors that increase the likelihood of the dual gaming + gambling disorder. Clinical settings should consider these features to improve gaming diagnosis and treatment. Preventive programs should also be focused on the most vulnerable groups to prevent onset and progression of this comorbid condition.

Disturbed craving regulation to gaming cues in internet gaming disorder: Implications for uncontrolled gaming behaviors

BACKGROUND

The ability to control craving for games is very important to abstain from Internet gaming disorder (IGD) and abundant clinical evidence has suggested that craving dysregulation is the essential pathogenesis for IGD. However, the neural mechanism underlying this feature remains unclear.

METHODS

Subjective evaluation and fMRI data from 44 participants (IGD participants: 21; recreational Internet game users (RGUs): 23) were collected while they were performing a regulation of craving task. We analyzed and compared their brain features while they regulated cravings to gaming stimuli.

RESULTS

Compared to RGUs, IGD participants showed enhanced brain activation in the right anterior cingulate cortex, posterior cingulate cortex (PCC), orbitofrontal cortex and middle temporal gyrus and in the left dorsolateral prefrontal cortex and thalamus during the regulation of craving task. Generalized psychophysiological interaction (gPPI) analysis revealed that IGD participants showed decreased functional connectivity between the right PCC and right inferior parietal lobule compared to that in RGU participants.

CONCLUSIONS

The results suggested that deficits of craving regulation in IGD participant were associated with the imbalanced coordination between the reward network and the executive network. Enhanced game-seeking motivation and disturbed executive control are responsible for craving dysregulation in IGD participants. These findings suggest a biological mechanism for IGD that may help in finding potential interventions.

Neurobiological mechanisms underlying internet gaming disorder

This review summarizes studies on the neurobiological correlates of internet gaming disorder (IGD), presently the most direct approach to analyzing the impact of digital technology and the internet on brain mechanisms. Brain imaging studies have shown that IGD shares, to a large extent, neurobiological alterations that are typical for other addictions, such as: (i) activation in brain regions associated with reward, as evident from cue exposure and craving studies and neurotransmitter systems studies that indicate an involvement of dopamine-mediated reward mechanisms; (ii) reduced activity in impulse control areas and impaired decision making; and (iii) reduced functional connectivity in brain networks that are involved in cognitive control, executive function, motivation, and reward. Moreover, there are structural changes, mainly reduction in gray-matter volume and white-matter density. Comorbidity studies indicate that executive control networks in attention deficit-hyperactivity disorder (ADHD) may increase the susceptibility to develop IGD. Most importantly, this review also outlines findings that show the effects of excessive use of screens, here referring to the playing of computer games, which activate many brain regions associated with cognitive, motor, and sensory function and not directly involved in other forms of addiction. This review describes and summarizes comprehensively the neurobiological correlates of addictive internet use in adolescents and young adults.
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Association of Extensive Video Gaming and Cognitive Function Changes in Brain-Imaging Studies of Pro Gamers and Individuals With Gaming Disorder: Systematic Literature Review

BACKGROUND

The World Health Organization announced the inclusion of gaming disorder (GD) in the International Classification of Diseases, 11th Revision, despite some concerns. However, video gaming has been associated with the enhancement of cognitive function. Moreover, despite comparable extensive video gaming, pro gamers have not shown any of the negative symptoms that individuals with GD have reported. It is important to understand the association between extensive video gaming and alterations in brain regions more objectively.

OBJECTIVE

This study aimed to systematically explore the association between extensive video gaming and changes in cognitive function by focusing on pro gamers and individuals with GD.

METHODS

Studies about pro gamers and individuals with GD were searched for in the PubMed and Web of Science databases using relevant search terms, for example, "pro-gamers" and "(Internet) gaming disorder." While studies for pro gamers were searched for without date restrictions, only studies published since 2013 about individuals with GD were included in search results. Article selection was conducted by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

By following the PRISMA guidelines, 1903 records with unique titles were identified. Through the screening process of titles and abstracts, 86 full-text articles were accessed to determine their eligibility. A total of 18 studies were included in this systematic review. Among the included 18 studies, six studies included pro gamers as participants, one study included both pro gamers and individuals with GD, and 11 studies included individuals with GD. Pro gamers showed structural and functional alterations in brain regions (eg, the left cingulate cortex, the insula subregions, and the prefrontal regions). Cognitive function (eg, attention and sensorimotor function) and cognitive control improved in pro gamers. Individuals with GD showed structural and functional alterations in brain regions (eg, the striatum, the orbitofrontal cortex, and the amygdala) that were associated with impaired cognitive control and higher levels of craving video game playing. They also showed increased cortical thickness in the middle temporal cortex, which indicated the acquisition of better skills. Moreover, it was suggested that various factors (eg, gaming expertise, duration or severity of GD, and level of self-control) seemed to modulate the association of extensive video game playing with changes in cognitive function.

CONCLUSIONS

Although a limited number of studies were identified that included pro gamers and/or individuals who reported showing symptoms of GD for more than 1 year, this review contributed to the objective understanding of the association between extensive video game playing and changes in cognitive function. Conducting studies with a longitudinal design or with various comparison groups in the future would be helpful in deepening the understanding of this association.

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder

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.

Altered brain network topology related to working memory in internet addiction

BACKGROUND AND AIMS

The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.

METHODS

A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.

RESULTS

We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.

CONCLUSIONS

In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

Aberrant structural network of comorbid attention deficit/hyperactivity disorder is associated with addiction severity in internet gaming disorder

BACKGROUND

Internet gaming disorder (IGD) is commonly comorbid with attention-deficit/hyperactivity disorder (ADHD). Although the addiction is more severe when comorbid with ADHD, little is known about the neural correlates of the association. This study aimed to identify whether an ADHD-related structural brain network exists in IGD patients with comorbid ADHD (IGDADHD+) by comparing them with those without comorbid ADHD (IGDADHD-) and elucidating how the sub-network is associated with addiction severity.

METHODS

Brain structural networks were constructed based on streamline tractography with diffusion tensor imaging in a cohort of 46 male IGDADHD+ patients, 48 male IGDADHD- patients, and 34 healthy controls (HC). We used network-based statistics (NBS) to identify the sub-network differences between the two IGD groups. Furthermore, the edges in the sub-network that significantly contributed to explaining the Young Internet Addiction Scale (YIAS) score were delineated using partial least square (PLS) regression analyses in IGD patients.

RESULTS

The YIAS score was higher in the IGDADHD+ group than in the IGDADHD- group and was correlated with the Korean Dupaul's ADHD scale score (r = 0.42, p <0.01). The NBS detected a sub-network with stronger connectivity in the IGDADHD+ group than in the IGDADHD-group. The PLS regression model showed that the sub-network is associated with the YIAS score in the IGDADHD+ group (q2 = 0.019). Edges connecting the left pre- and postcentral gyri, bilateral superior frontal gyri, medial orbital parts, and left fusiform to the inferior temporal gyrus were most important predictors in the regression model.

CONCLUSION

Our results suggest that an aberrant increase in some structural connections within circuits related to inhibitory function or sensory integration can indicate how comorbid ADHD is associated with addiction severity in IGD.

Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis

OBJECTIVES

Internet addiction (IA) has been associated with widespread brain alterations. Functional connectivity (FC) and network analysis results related to IA are inconsistent between studies, and how network hubs change is not known. The aim of this study was to evaluate functional and topological networks using an unbiased minimum spanning tree (MST) analysis on electroencephalography (EEG) data in IA and healthy control (HC) college students.

METHODS

In this study, Young's internet addiction test was used as an IA severity measure. EEG recordings were obtained in IA (n = 30) and HC participants (n = 30), matched for age and sex, during rest. The phase lag index (PLI) and MST were applied to analyze FC and network topology. We expected to obtain evidence of underlying alterations in functional and topological networks related to IA.

RESULTS

IA participants showed higher delta FC between left-side frontal and parieto-occipital areas compared to the HC group (p < 0.001), global MST measures revealed a more star-like network in IA participants in the upper alpha and beta bands, and the occipital brain region was relatively less important in the IA relative to the HC group in the lower band. The correlation results were consistent with the MST results: higher IA severity correlated with higher Max degree and kappa, and lower eccentricity and diameter.

CONCLUSIONS

Functional networks of the IA group were characterized by increased FC, a more random organization, and a decrease of relative functional importance of the visual processing area. Taken together, these alterations can help us understand the influence of IA to brain mechanism.

Alterations in the connection topology of brain structural networks in Internet gaming addiction

Internet gaming addiction (IGA), as the most popular subtype of Internet addiction, is becoming a common and widespread mental health concern, but there are still debates on whether IGA constitutes a psychiatric disorder. The view on the brain as a complex network has developed network analysis of neuroimaging data, revealing that abnormalities of brain functional and structural systems are related to alterations in brain network configuration, such as small-world topology, in neuropsychiatric disorders. Here we applied network analysis to diffusion-weighted MRI data of 102 gaming individuals and 41 non-gaming healthy individuals to seek changes in the small-world topology of brain structural networks in IGA. The connection topology of brain structural networks shifted to the direction of random topology in the gaming individuals, irrespective of whether they were diagnosed with Internet gaming disorder. Furthermore, when we simulated targeted or untargeted attacks on nodes, the connection topology of the gaming individuals' brain structural networks under no attacks was comparable to that of the non-gaming healthy individuals' brain structural networks under targeted attacks. Alterations in connection topology provide a clue that Internet gaming addicted brains could be as abnormal as brains suffering from targeted damage.

Internet Game Overuse Is Associated With an Alteration of Fronto-Striatal Functional Connectivity During Reward Feedback Processing

Internet gaming disorder is associated with abnormal reward processing in the reward circuit, which is known to interact with other brain regions during feedback learning. Kim et al. (1) observed that individuals with internet game overuse (IGO) exhibit altered behavior and neural activity for non-monetary reward, but not for monetary reward. Here, we extend our analysis of IGO to the functional connectivity of the reward network. Functional MRI data were obtained during a stimulus-response association learning task from 18 young males with IGO and 20 age-matched controls, where either monetary or non-monetary rewards were given as positive feedback for a correct response. Group differences in task-dependent functional connectivity were examined for the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS), which are known for reward evaluation and hedonic response processing, respectively, using a generalized form of the psychophysiological interaction approach. For non-monetary reward processing, no differences in functional connectivity were found. In contrast, for monetary reward, connectivity of the vmPFC with the left caudate nucleus was weaker for the IGO group relative to controls, while vmPFC connectivity with the right nucleus accumbens (NAcc) was elevated. The strength of vmPFC-NAcc functional connectivity appeared to be behaviorally relevant, because individuals with stronger vmPFC-NAcc connectivity showed lower learning rates for monetary reward. In addition, the IGO group showed weaker ventral striatum functional connectivity with various brain regions, including the right ventrolateral prefrontal cortex, dorsal anterior cingulate regions, and left pallidum. Thus, for monetary reward, the IGO group exhibited stronger functional connectivity within the brain regions involved in motivational salience, whereas they showed reduced functional connectivity the widely distributed brain areas involved in learning or attention. These differences in functional connectivity of reward networks, along with related behavioral impairments of reward learning, suggest that internet gaming disorder is associated with the increased incentive salience or "wanting" of addiction disorders, and may serve as the neurobiological mechanisms underlying the impaired goal-directed behavior.

An Update Overview on Brain Imaging Studies of Internet Gaming Disorder

There are a growing number of studies on structural and functional brain mechanisms underlying Internet gaming disorder (IGD). Recent functional magnetic resonance imaging studies showed that IGD adolescents and adults had reduced gray matter volume in regions associated with attention motor coordination executive function and perception. Adolescents with IGD showed lower white matter (WM) integrity measures in several brain regions that are involved in decision-making, behavioral inhibition, and emotional regulation. IGD adolescents had also disruption in the functional connectivity in areas responsible for learning memory and executive function, processing of auditory, visual, and somatosensory stimuli and relay of sensory and motor signals. IGD adolescents also had decreased functional connectivity of PFC-striatal circuits, increased risk-taking choices, and impaired ability to control their impulses similar to other impulse control disorders. Recent studies indicated that altered executive control mechanisms in attention deficit hyperactivity disorder (ADHD) would be a predisposition for developing IGD. Finally, patients with IGD have also shown an increased functional connectivity of several executive control brain regions that may related to comorbidity with ADHD and depression. The behavioral addiction model argues that IGD shows the features of excessive use despite adverse consequences, withdrawal phenomena, and tolerance that characterize substance use disorders. The evidence supports the behavioral addiction model of IGD by showing structural and functional changes in the mechanisms of reward and craving (but not withdrawal) in IGD. Future studies need to investigate WM density and functional connectivity in IGD in order to validate these findings. Furthermore, more research is required about the similarity in neurochemical and neurocognitive brain circuits in IGD and comorbid conditions such as ADHD and depression.