Associations between prospective symptom changes and slow-wave activity in patients with Internet gaming disorder: A resting-state EEG study

Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study

BACKGROUND

Internet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system.

METHODS

Twenty-two male individuals with IGD (18-23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system.

RESULTS

Results revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers.

CONCLUSION

This study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.

Biomarkers of Internet Gaming Disorder-A Narrative Review

Since game mechanics and their visual aspects have become more and more addictive, there is concern about the growing prevalence of Internet gaming disorder (IGD). In the current narrative review, we searched PubMed and Google Scholar databases for the keywords "igd biomarker gaming" and terms related to biomarker modalities. The biomarkers we found are grouped into several categories based on a measurement method and are discussed in the light of theoretical addiction models (tripartite neurocognitive model, I-PACE). Both theories point to gaming-related problems with salience and inhibition. The first dysfunction makes an individual more susceptible to game stimuli (raised reward seeking), and the second negatively impacts resistance to these stimuli (decreased cognitive control). The IGD patients' hypersensitivity to reward manifests mostly in ventral striatum (VS) measurements. However, there is also empirical support for a ventral-to-dorsal striatal shift and transition from goal-directed to habitual behaviors. The deficits in executive control are demonstrated in parameters related to the prefrontal cortex (PFC), especially the dorsolateral prefrontal cortex (DLPFC). In general, the connection of PFC with reward under cortex nuclei seems to be dysregulated. Other biomarkers include reduced P3 amplitudes, high-frequency heart rate variability (HRV), and the number of eye blinks and saccadic eye movements during the non-resting state. A few studies propose a diagnostic (multimodal) model of IGD. The current review also comments on inconsistencies in findings in the nucleus accumbens (NAcc), anterior cingulate cortex (ACC), and precuneus and makes suggestions for future IGD studies.

Effect of excessive internet gaming on inhibitory control based on resting EEG and ERP

Previous research indicates that individuals with Internet gaming disorder (IGD) show impaired inhibitory control and abnormal EEG/ERP patterns. However, it is unclear how those individuals with excessive Internet game use (EUG) but without addiction differ. This study examined inhibitory control, resting EEG, and ERP in EUG gamers compared to non-gamers. Fifteen participants in each group underwent 4-min eyes-closed EEG recordings and a color-word Stroop task. Results showed no significant differences in reaction time, accuracy, or P3 amplitude between EUG gamers and non-gamers. However, EUG gamers exhibited shortened P3 latency, which may suggest enhanced inhibitory control. Additionally, EUG gamers showed reduced theta and alpha band power during the resting state compared to non-gamers. These findings suggest that excessive gaming without addiction may enhance inhibitory control and influence brain activity differently from IGD.

Concurrent behavioral modeling and multimodal neuroimaging reveals how feedback affects the performance of decision making in internet gaming disorder

Internet gaming disorder (IGD) prompts inquiry into how feedback from prior gaming rounds influences subsequent risk-taking behavior and potential neural mechanisms. Forty-two participants, including 15 with IGD and 27 health controls (HCs), underwent a sequential risk-taking task. Hierarchy Bayesian modeling was adopted to measure risky propensity, behavioral consistence, and affection by emotion ratings from last trial. Concurrent electroencephalogram and functional near-infrared spectroscopy (EEG-fNIRS) recordings were performed to demonstrate when, where and how the previous-round feedback affects the decision making to the next round. We discovered that the IGD illustrated heightened risk-taking propensity as compared to the HCs, indicating by the computational modeling (p = 0.028). EEG results also showed significant time window differences in univariate and multivariate pattern analysis between the IGD and HCs after the loss of the game. Further, reduced brain activation in the prefrontal cortex during the task was detected in IGD as compared to that of the control group. The findings underscore the importance of understanding the aberrant decision-making processes in IGD and suggest potential implications for future interventions and treatments aimed at addressing this behavioral addiction.

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study

BACKGROUND

Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function.

METHODS

In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed.

RESULTS

The IGD group exhibited higher absolute powers of D_C, D_PO, T_C, T_PO, B_C, and B_PO than HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test).

CONCLUSIONS

These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs.

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young's internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5-73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10-20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.

Enhanced resting-state EEG source functional connectivity within the default mode and reward-salience networks in internet gaming disorder

BACKGROUND

The two key mechanisms affected by internet gaming disorder (IGD) are cognitive and reward processing. Despite their significance, little is known about neurophysiological features as determined using resting-state electroencephalography (EEG) source functional connectivity (FC).

METHODS

We compared resting-state EEG source FC within the default mode network (DMN) and reward/salience network (RSN) between patients with IGD and healthy controls (HCs) to identify neurophysiological markers associated with cognitive and reward processing. A total of 158 young male adults (79 patients with IGD and 79 HCs) were included, and the source FC of the DMN and RSN in five spectral bands (delta, theta, alpha, beta, and gamma) were assessed.

RESULTS

Patients with IGD showed increased theta, alpha, and beta connectivity within the DMN between the orbitofrontal cortex and parietal regions compared with HCs. In terms of RSN, patients with IGD exhibited elevated alpha and beta connectivity between the anterior cingulate gyrus and temporal regions compared with HCs. Furthermore, patients with IGD showed negative correlations between the severity of IGD symptoms and/or weekly gaming time and theta and alpha connectivity within the DMN and theta, alpha, and beta connectivity within the RSN. However, the duration of IGD was not associated with EEG source FC.

CONCLUSIONS

Hyper-connectivities within the DMN and RSN may be considered potential state markers associated with symptom severity and gaming time in IGD.

Development and Validation of a Risk Assessment Tool for Gaming Disorder in China: The Gaming Hazard Assessment Scale

Despite the growing research interest in gaming disorder, risk screening tools developed specifically for the Chinese population are still lacking. This study aimed to construct a screening tool to evaluate the risk of gaming disorder (GD) development, by assessing the severity of GD symptoms among Chinese gamers, based on clinical expert interviews, structured interviews with GD patients, a background literature review, and IGD/GD criteria proposed by the DSM-5 and ICD-11. It introduced the Gaming Hazard Assessment Scale—a multidimensional GD risk screening tool—and evaluated the dimension structure, reliability, and validity of the scale among 959 Chinese gamers. A three-level structure, consisting of 18 items scored from 0 to 54, ultimately indicated satisfactory reliability, good validity, and acceptable model fit. The scale will help large-scale initial screening and early identification of patients with a high risk of GD. Further evaluation of the Gaming Hazard Assessment Scale in clinical settings is highly recommended.

Slow-Wave EEG Activity Correlates with Impaired Inhibitory Control in Internet Addiction Disorder

Impaired inhibitory control is a core feature of internet addiction disorder (IAD). It is therefore of interest to determine the neurophysiological markers associated with it. The present study aimed to find such biomarkers with a resting-state electroencephalogram (EEG). We specifically used scores on the Chinese Internet Addiction Scale revised edition (CIAS-R) to divide 46 participants into two groups: the IAD group (>53, n = 23) and control group (<46, n = 23). Both behavioral aspects (Go/NoGo responses and impulsivity) and EEG were measured in the lab. The results suggest that the IAD group presented a decreased slow-wave (1−8 Hz) absolute power across the whole brain. The slow-wave activities in the frontal areas were also correlated with the commission error rate in the Go/NoGo task in the IAD group. These results imply that the frontal slow-wave EEG activity may serve as a neurophysiological marker of IAD, helping to understand the underlying neural mechanisms of inhibitory control deficits in IAD and point to possible interventions.

Neurophysiological Markers of Internet Gaming Disorder: A Literature Review of Electroencephalography Studies

Pathological online gaming is a relatively newer psychiatric disorder. It is the second behavioral addiction (after internet gambling disorder) included in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM), 5^th edition as “Internet Gaming Disorder” (IGD). Recent research is showing high morbidity associated with IGD, thus encouraging researchers to find valid and reliable biomarkers of IGD. So that, early diagnosis and proper treatment could limit the high disability level accompanying excessive online gaming. As electroencephalography (EEG) is a non-invasive and relatively easily available diagnostic technique, we aimed at collecting EEG studies that investigated EEG changes associated with IGD, with a specific focus on finding diagnostic and predictive neurophysiological biomarkers of IGD. We searched PubMed and Google Scholar for EEG studies of IGD. We eliminated those EEG studies that were not focused on finding biomarkers. Scale for the Assessment of Narrative Review Articles (SANRA) is followed in the writing of this review article. Our results showed that increased slow-wave resting-state activity and reduced P300 and N100 can serve as useful IGD diagnostic markers of IGD. Moreover, increased resting-state theta activity can be a predictive biomarker of IGD. Lastly, increased late low potentials (LLPs) can be specific trait markers of cue-induced cravings in IGD.

Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.

Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG

Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.

Altered topological connectivity of internet addiction in resting-state EEG through network analysis

The results of some neuroimaging studies have revealed that people with internet addiction (IA) exhibit structural and functional changes in specific brain areas and connections. However, the understanding about global topological organization of IA may also require a more integrative and holistic view of brain function. In the present study, we used synchronization likelihood combined with graph theory analysis to investigate the functional connectivity (FC) and topological differences between 25 participants with IA and 27 healthy controls (HCs) based on their spontaneous EEG activities in the eye-closed resting state. There were no significant differences in FC (total network or sub-networks) between groups (p > .05 for all). Graph analysis showed significantly lower characteristic path length and clustering coefficient in the IA group than in the HC group in the beta and gamma bands, respectively. Altered nodal centralities of the frontal (FP₁, FPz) and parietal (CP₁, CP₅, PO₃, PO₇, P₅, P_6, TP₈) lobes in the IA group were also observed. Correlation analysis demonstrated that the observed regional alterations were significantly correlated with the severity of IA. Collectively, our findings showed that IA group demonstrated altered topological organization, shifting towards a more random state. Moreover, this study revealed the important role of altered brain areas in the neuropathological mechanism of IA and provided further supportive evidence for the diagnosis of IA.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

Longitudinal Changes in Neural Connectivity in Patients With Internet Gaming Disorder: A Resting-State EEG Coherence Study

Aims: The present study investigated neural connectivity associated with treatment responses in patients with Internet gaming disorder (IGD) using resting-state electroencephalography (EEG) coherence analyses. Methods: We included 30 patients with IGD and 32 healthy control subjects (HCs). Of the IGD patients, 18 completed an outpatient treatment that included pharmacotherapy with selective serotonin reuptake inhibitors for 6 months. Resting-state EEG coherence and self-report questionnaires were used to evaluate clinical and psychological features pre- and post-treatment, and data were analyzed using generalized estimating equations. Results: Compared with HCs, patients with IGD showed increased beta and gamma intrahemispheric coherence and increased delta intrahemispheric coherence of the right hemisphere at baseline. After 6 months of outpatient management, patients with IGD exhibited improvements in IGD symptoms compared with baseline, but they continued to show increased beta and gamma intrahemispheric coherence compared with that of HCs. No significant EEG coherence changes between the pre- and post-treatment assessments were detected in any band in the IGD group. Conclusion: These findings suggest that significantly greater intrahemispheric fast-frequency coherence may be an important neurophysiological trait marker of patients with IGD.

Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review

Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and Abstracts, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET, and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex (PFC) functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders.

Sustained dysfunctional information processing in patients with Internet gaming disorder: 6-month follow-up ERP study

Internet gaming disorder (IGD), defined as an inability to control Internet-based game play, leads to serious impairment in psychological and social functioning, but few studies have identified the neurophysiological characteristics of patients with IGD. The aim of this study was to determine neurophysiological markers of P300 components associated with changes in symptoms after outpatient management with pharmacotherapy in patients with IGD. The present prospective longitudinal study included 18 patients with IGD and 29 healthy controls. The patients with IGD completed a 6-month outpatient management program including selective serotonin reuptake inhibitor-based pharmacotherapy. Event-related potentials (ERPs) were acquired during the auditory oddball task. ERPs of the patients with IGD were recorded before and after treatment. Between-group differences and the pre-to-post treatment differences in P300 components were investigated using repeated-measures analysis of variance. The primary treatment outcome was a change in score on Young Internet Addiction Test between before and after treatment. At baseline assessments, the IGD group showed significantly reduced P300 amplitudes and delayed latencies at the midline centro-parietal site compared with those in the healthy controls. No significant changes in the P300 indices were observed between pre and post-treatment in the patients with IGD after 6 months of treatment, even though the patients with IGD exhibited significant improvements in their IGD symptoms. Furthermore, no significant difference in ERPs was observed between responders and nonresponders to a 6-month treatment in patients with IGD. These results suggest that reduced P300 amplitudes and delayed latencies are candidate endophenotypes in the pathophysiology of IGD.