Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study

Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning

Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting the risk of DUIA despite limited data availability. Thus, this study aimed at comparing six well-known ML algorithms based on electroencephalographic (EEG) signals to differentiate adults with AUD and DUIA (AUD-DD) from those with AUD without DUIA (AUD-NDD) and controls. Fifteen AUD-DD and 10 AUD-NDD participants were recruited from a single tertiary referral center. Fourteen social drinkers without DUIA served as controls. Their EEG signals related to driving conditions were gathered using a VR headset with eight electrodes (F3, F4, Fz, C3, C4, Cz, P3, and P4). Based on the labeled features of EEG asymmetry and theta/beta ratio (TBR), comparisons between different algorithms were conducted. Fz and Cz electrodes exhibited differences in TBR across the three groups (all p < 0.02), while there were no significant differences between AUD-DD individuals and social drinkers. In contrast, asymmetries of between-group differences were not observed (all p > 0.09). K-nearest neighbors (KNN) with TBR showed the highest accuracy (83 %) in distinguishing AUD-DD individuals from controls, while logistic regression (LR), support vector machines (SVM), and naive Bayes (NB) with EEG asymmetric features demonstrated high accuracy in identifying DUIA (all 80 %) in AUD adults. LR, SVM, and NB with asymmetry may be employed in predicting DUIA among AUD adults, while KNN with TBR may be used for identifying DUIA in the general population.

Clinical, Genomic, and Neurophysiological Correlates of Lifetime Suicide Attempts among Individuals with an Alcohol Use Disorder

Introduction

Research has identified multiple risk factors associated with suicide attempt (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA.

Methods

We examined lifetime SA in 4,068 individuals with an AUD from the Collaborative Study on the Genetics of Alcoholism (23% lifetime SA; 53% female; mean age: 38). We explored risk for lifetime SA across other clinical conditions ascertained from a clinical interview, polygenic scores for comorbid psychiatric problems, and neurocognitive functioning.

Results

Participants with an AUD who attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, other substance use disorders (SUDs), and suicidal ideation. Polygenic scores for SA, depression, and PTSD were associated with increased odds of reporting an SA (ORs = 1.22-1.44). Participants who reported an SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small.

Conclusions

Overall, individuals with an AUD who report lifetime SA experience greater levels of trauma, have more severe comorbidities, and carry increased polygenic risk for other psychiatric problems. Our results demonstrate the need to further investigate SAs in the presence of SUDs.

Childhood trauma is associated with developmental trajectories of EEG coherence, alcohol-related outcomes, and PTSD symptoms

BACKGROUND

Associations between childhood trauma, neurodevelopment, alcohol use disorder (AUD), and posttraumatic stress disorder (PTSD) are understudied during adolescence.

METHODS

Using 1652 participants (51.75% female, baseline Mage = 14.3) from the Collaborative Study of the Genetics of Alcoholism, we employed latent growth curve models to (1) examine associations of childhood physical, sexual, and non-assaultive trauma (CPAT, CSAT, and CNAT) with repeated measures of alpha band EEG coherence (EEGc), and (2) assess whether EEGc trajectories were associated with AUD and PTSD symptoms. Sex-specific models accommodated sex differences in trauma exposure, AUD prevalence, and neural development.

RESULTS

In females, CSAT was associated with higher mean levels of EEGc in left frontocentral (LFC, ß = 0.13, p = 0.01) and interhemispheric prefrontal (PFI, ß = 0.16, p < 0.01) regions, but diminished growth in LFC (ß = -0.07, p = 0.02) and PFI (ß = -0.07, p = 0.02). In males, CPAT was associated with lower mean levels (ß = -0.17, p = 0.01) and increased growth (ß = 0.11, p = 0.01) of LFC EEGc. Slope of LFC EEGc was inversely associated with AUD symptoms in females (ß = -1.81, p = 0.01). Intercept of right frontocentral and PFI EEGc were associated with AUD symptoms in males, but in opposite directions. Significant associations between EEGc and PTSD symptoms were also observed in trauma-exposed individuals.

CONCLUSIONS

Childhood assaultive trauma is associated with changes in frontal alpha EEGc and subsequent AUD and PTSD symptoms, though patterns differ by sex and trauma type. EEGc findings may inform emerging treatments for PTSD and AUD.

Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder

INTRODUCTION

To investigate the neurophysiological aspects of addiction, the microstate characteristics of internet gaming disorder (IGD), alcohol use disorder (AUD), and healthy control (HC) groups were compared using resting-state electroencephalography (EEG).

METHODS

In total, 199 young adults (75 patients with IGD, 57 patients with AUD, and 67 HCs) participated in this study. We conducted EEG microstate analysis among the groups and also compared the obtained parameters with the results of psychological assessments.

RESULTS

The global explained variance, occurrence, and coverage of microstate C were significantly lower in the AUD group than in the IGD group. Additionally, rates of transition from microstates A, B, and D to C were significantly lower in the AUD group than in the IGD group, whereas rates of transition from microstate A to B were lower in the IGD group compared to HCs. Furthermore, the occurrence of microstate C and transition from microstate B to C were negatively correlated with the Alcohol Use Disorder Identification and Behavioural Inhibition Scale score.

CONCLUSION

There were significant differences in microstate characteristics among the groups, which correlated with the psychological scores. These findings suggest that microstate features can be used as neuromarkers in clinical settings to differentiate between addictive disorders and evaluate the pathophysiology of AUD and IGD.

The relationship between physical activity and Internet addiction among adolescents in western China: a chain mediating model of anxiety and inhibitory control

This study aims to investigate the mediating effect of anxiety and inhibitory control in the relationship between physical activity and Internet addiction (IA) among adolescents. A total of 951 adolescents from western China completed a self-report survey assessing physical activity, anxiety, inhibitory control, and IA. Descriptive analysis, correlation analysis, and mediation analysis were conducted using SPSS software and the Process plug-in. Controlling for age, gender, and only child status, the findings revealed a negative association between physical activity and anxiety, inhibitory control, and IA. Moreover, anxiety were positively correlated with inhibitory control and IA. Additionally, anxiety exhibited a positive association with inhibitory control. Notably, physical activity directly and negatively predicted IA in adolescents, while also indirectly predicting it through anxiety and inhibitory control. This study contributes to a deeper understanding of the mechanisms that underlie the complex effects of physical activity on IA among adolescents.

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 Forward Head Posture on Resting State Brain Function

Forward head posture (FHP) is a common postural problem experienced by most people. However, its effect on brain activity is still unknown. Accordingly, we aimed to observe changes in brain waves at rest to determine the effect of FHP on the nervous systems. A total of 33 computer users (Male = 17; Female = 16; age = 22.18 ± 1.88) were examined in both FHP and neutral posture. For each session, brain waves were measured for 5 min, and then muscle mechanical properties and cranio-vertebral angle (CVA) were measured. Changes in brain waves between the neutral posture and FHP were prominent in gamma waves. A notable increase was confirmed in the frontal and parietal lobes. That is, eight channels in the frontal lobe and all channels in the parietal lobe showed a significant increase in FHP compared to neutral posture. Additionally, FHP changes were associated with a decrease in CVA (p < 0.001), an increase in levator scapulae tone (Right, p = 0.014; Left, p = 0.001), and an increase in right sternocleidomastoid stiffness (p = 0.002), and a decrease in platysma elasticity (Right, p = 0.039; Left, p = 0.017). The change in CVA was found to have a negative correlation with the gamma activity (P7, p = 0.044; P8, p = 0.004). Therefore, increased gamma wave activity in FHP appears to be related to CVA decrease due to external force that was applied to the nervous system and cervical spine.

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder

Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; mean age: 38). Within participants with an AUD diagnosis, we explored risk across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning for lifetime suicide attempt. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22 - 1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with an AUD who report a lifetime suicide attempt appear to experience greater levels of trauma, have more severe comorbidities, and carry polygenic risk for a variety of psychiatric problems. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.

The Predictive Potential of Heart Rate Variability for Depression

Heart rate variability (HRV),a measure of the fluctuations in the intervals between consecutive heartbeats, is an indicator of changes in the autonomic nervous system. A chronic reduction in HRV has been repeatedly linked to clinical depression. However, the chronological and mechanistic aspects of this relationship, between the neural, physiological, and psychopathological levels, remain unclear. In this review we present evidence by which changes in HRV might precede the onset of depression. We describe several pathways that can facilitate this relationship. First, we examine a theoretical model of the impact of autonomic imbalance on HRV and its role in contributing to mood dysregulation and depression. We then highlight brain regions that are regulating both HRV and emotion, suggesting these neural regions, and the Insula in particular, as potential mediators of this relationship. We also present additional possible mediating mechanisms involving the immune system and inflammation processes. Lastly, we support this model by showing evidence that modification of HRV with biofeedback leads to an improvement in some symptoms of depression. The possibility that changes in HRV precede the onset of depression is critical to put to the test, not only because it could provide insights into the mechanisms of the illness but also because it may offer a predictive anddiagnosticphysiological marker for depression. Importantly, it could also help to develop new effective clinical interventions for treating depression.

Multimodal-based machine learning approach to classify features of internet gaming disorder and alcohol use disorder: A sensor-level and source-level resting-state electroencephalography activity and neuropsychological study

OBJECTIVES

Addictions have recently been classified as substance use disorder (SUD) and behavioral addiction (BA), but the concept of BA is still debatable. Therefore, it is necessary to conduct further neuroscientific research to understand the mechanisms of BA to the same extent as SUD. The present study used machine learning (ML) algorithms to investigate the neuropsychological and neurophysiological aspects of addictions in individuals with internet gaming disorder (IGD) and alcohol use disorder (AUD).

METHODS

We developed three models for distinguishing individuals with IGD from those with AUD, individuals with IGD from healthy controls (HCs), and individuals with AUD from HCs using ML algorithms, including L1-norm support vector machine, random forest, and L1-norm logistic regression (LR). Three distinct feature sets were used for model training: a unimodal-electroencephalography (EEG) feature set combined with sensor- and source-level feature; a unimodal-neuropsychological feature (NF) set included sex, age, depression, anxiety, impulsivity, and general cognitive function, and a multimodal (EEG + NF) feature set.

RESULTS

The LR model with the multimodal feature set used for the classification of IGD and AUD outperformed the other models (accuracy: 0.712). The important features selected by the model highlighted that the IGD group had differential delta and beta source connectivity between right intrahemispheric regions and distinct sensor-level EEG activities. Among the NFs, sex and age were the important features for good model performance.

CONCLUSIONS

Using ML techniques, we demonstrated the neurophysiological and neuropsychological similarities and differences between IGD (a BA) and AUD (a SUD).

Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction

Previous studies have reported a role of alterations in the brain's inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with alcohol dependence

Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; 17% Admixed African American ancestries; mean age: 38). We 1) conducted a genome-wide association study (GWAS) of SA and performed downstream analyses to determine whether we could identify specific biological pathways of risk, and 2) explored risk in aggregate across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning between those with AD who have and have not reported a lifetime suicide attempt. The GWAS and downstream analyses did not produce any significant associations. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22-1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with alcohol dependence who report SA appear to experience a variety of severe comorbidities and elevated polygenic risk for SA. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.

Measuring the Intensity of Stress Experienced and Its Impact on Life in Patients with Diagnosed Alcohol Use Disorder

Alcohol addiction is characterized by extensive alcohol consumption that dominates other behaviours previously important to a patient. According to data from The State Agency for Prevention of Alcohol-Related Problems, up to 900,000 people in Poland are addicted to alcohol. On average, approximately 9.7 L of pure alcohol per capita was consumed in 2021. Alcohol addiction may cause severe health problems and is one the key risk factors for various diseases. Stress plays an important role in the process of alcohol addiction and is also a predictor for lower enjoyment in life. On the other hand, sense of coherence may be a stronger protective factor. The aim of our study was to verify the relation between the level of perceived stress among patients with alcohol addiction and satisfaction with life. Because sense of coherence is a disposition that allows for managing stress effectively, the latter should be reflected in the results of multivariate analyses that take both the level of stress and sense of coherence into account. In the present study, sense of coherence and perceived stress were negatively correlated; therefore, strengthening internal resources for managing difficult and stressful situations is recommended.

Postural supporting cervical traction workstation to improve resting state brain activity in digital device users: EEG study

Objective

This study aimed to determine the effect of postural support workstation on inducing effective brain activity during rest.

Methods

Thirty-five healthy digital overusers were recruited as participants. We conducted two interventions of head weight support traction (ST) and conventional traction (CT) strength on all participants in random order. Participants' arousal levels and psychological comfort were assessed. In addition, changes in brain activity caused by traction were confirmed by measuring changes in resting state brain activity using an electroencephalogram (EEG).

Results

Under the ST condition, psychological comfort improved while alert levels were maintained. In addition the resting brain activity of EEG was characterized by strong focused attention and relaxed activity, as evidenced by increased alpha waves throughout the brain. By contrast, in the CT condition, no significant improvement in comfort was observed. Furthermore, high-frequency brain activity, such as beta 3 and gamma waves, was observed across the entire brain regions.

Conclusion

In this study, the ST workstation was shown to effectively improve resting attention and psychological comfort in individuals who excessively use digital devices by inducing resting state alpha activity without stimulating high-frequency brain waves, while maintaining an upright posture with appropriate traction.

COVID-19 pandemic stressors are associated with reported increases in frequency of drunkenness among individuals with a history of alcohol use disorder

Some sources report increases in alcohol use have been observed since the start of the COVID-19 pandemic, particularly among women. Cross-sectional studies suggest that specific COVID-19-related stressful experiences (e.g., social disconnection) may be driving such increases in the general population. Few studies have explored these topics among individuals with a history of Alcohol Use Disorders (AUD), an especially vulnerable population. Drawing on recent data collected by the Collaborative Study on the Genetics of Alcoholism (COGA; COVID-19 study N = 1651, 62% women, age range: 30-91) in conjunction with AUD history data collected on the sample since 1990, we investigated associations of COVID-19 related stressors and coping activities with changes in drunkenness frequency since the start of the pandemic. Analyses were conducted for those without a history of AUD (N: 645) and three groups of participants with a history of AUD prior to the start of the pandemic: (1) those experiencing AUD symptoms (N: 606), (2) those in remission who were drinking (N: 231), and (3) those in remission who were abstinent (had not consumed alcohol for 5+ years; N: 169). Gender-stratified models were also examined. Exploratory analyses examined the moderating effects of 'problematic alcohol use' polygenic risk scores (PRS) and neural connectivity (i.e., posterior interhemispheric alpha EEG coherence) on associations between COVID-19 stressors and coping activities with changes in the frequency of drunkenness. Increases in drunkenness frequency since the start of the pandemic were higher among those with a lifetime AUD diagnosis experiencing symptoms prior to the start of the pandemic (14% reported increased drunkenness) when compared to those without a history of AUD (5% reported increased drunkenness). Among individuals in remission from AUD prior to the start of the pandemic, rates of increased drunkenness were 10% for those who were drinking pre-pandemic and 4% for those who had previously been abstinent. Across all groups, women reported nominally greater increases in drunkenness frequency when compared with men, although only women experiencing pre-pandemic AUD symptoms reported significantly greater rates of increased drunkenness since the start of the pandemic compared to men in this group (17% of women vs. 5% of men). Among those without a prior history of AUD, associations between COVID-19 risk and protective factors with increases in drunkenness frequency were not observed. Among all groups with a history of AUD (including those with AUD symptoms and those remitted from AUD), perceived stress was associated with increases in drunkenness. Among the remitted-abstinent group, essential worker status was associated with increases in drunkenness. Gender differences in these associations were observed: among women in the remitted-abstinent group, essential worker status, perceived stress, media consumption, and decreased social interactions were associated with increases in drunkenness. Among men in the remitted-drinking group, perceived stress was associated with increases in drunkenness, and increased relationship quality was associated with decreases in drunkenness. Exploratory analyses indicated that associations between family illness or death with increases in drunkenness and increased relationship quality with decreases in drunkenness were more pronounced among the remitted-drinking participants with higher PRS. Associations between family illness or death, media consumption, and economic hardships with increases in drunkenness and healthy coping with decreases in drunkenness were more pronounced among the remitted-abstinent group with lower interhemispheric alpha EEG connectivity. Our results demonstrated that only individuals with pre-pandemic AUD symptoms reported greater increases in drunkenness frequency since the start of the COVID-19 pandemic compared to those without a lifetime history of AUD. This increase was more pronounced among women than men in this group. However, COVID-19-related stressors and coping activities were associated with changes in the frequency of drunkenness among all groups of participants with a prior history of AUD, including those experiencing AUD symptoms, as well as abstinent and non-abstinent participants in remission. Perceived stress, essential worker status, media consumption, social connections (especially for women), and relationship quality (especially for men) are specific areas of focus for designing intervention and prevention strategies aimed at reducing pandemic-related alcohol misuse among this particularly vulnerable group. Interestingly, these associations were not observed for individuals without a prior history of AUD, supporting prior literature that demonstrates that widespread stressors (e.g., pandemics, terrorist attacks) disproportionately impact the mental health and alcohol use of those with a prior history of problems.

Emotion Regulation Choice in Internet Addiction: Less Reappraisal, Lower Frontal Alpha Asymmetry

Individuals with internet addiction (IA) show difficulties in emotion regulation. However, they could effectively employ emotion regulation strategies when instructed. We speculate that this discrepancy might be caused by maladaptive emotion regulation choices. Recent studies indicated that decreased activity of the left frontal cortex could be a neural marker of reappraisal use. To address this problem, individuals with IA (n = 17, IA group) and healthy individuals (n = 23, healthy control [HC] group) were required to choose an emotion regulation strategy between reappraisal and distraction to regulate their emotions varying in emotional intensity and valence. We also compared the resting state frontal alpha asymmetry (FAA) of these 2 groups. The results replicated more choices of reappraisal in low- versus high-intensity emotional contexts across groups. More importantly, the IA group chose reappraisal less frequently compared with the HC group, irrespective of emotional intensity. Furthermore, we found individuals with IA have lower FAA than healthy controls, and FAA shows a positive correlation with the use of reappraisal. These findings suggest that IA alters individuals' patterns of emotion regulation choice and impairs frontal activities, causing difficulties in emotion regulation.

Multiple-Kernel Support Vector Machine for Predicting Internet Gaming Disorder Using Multimodal Fusion of PET, EEG, and Clinical Features

Internet gaming disorder (IGD) has become an important social and psychiatric issue in recent years. To prevent IGD and provide the appropriate intervention, an accurate prediction method for identifying IGD is necessary. In this study, we investigated machine learning methods of multimodal neuroimaging data including Positron Emission Tomography (PET), Electroencephalography (EEG), and clinical features to enhance prediction accuracy. Unlike the conventional methods which usually concatenate all features into one feature vector, we adopted a multiple-kernel support vector machine (MK-SVM) to classify IGD. We compared the prediction performance of standard machine learning methods such as SVM, random forest, and boosting with the proposed method in patients with IGD (N = 28) and healthy controls (N = 24). We showed that the prediction accuracy of the optimal MK-SVM using three kinds of modalities was much higher than other conventional machine learning methods, with the highest accuracy being 86.5%, the sensitivity 89.3%, and the specificity 83.3%. Furthermore, we deduced that clinical variables had the highest contribution to the optimal IGD prediction model and that the other two modalities were also indispensable. We found that more efficient integration of multimodal data through kernel combination could contribute to better performance of the prediction model. This study is a novel attempt to integrate each method from different sources and suggests that integrating each method, such as self-administrated reports, PET, and EEG, improves the prediction of IGD.

Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis

To compare the pattern of brain waves in video game addicts and normal individuals, a case-control study was carried out on both. Thirty participants were recruited from 14 to 20 years old males from two gaming centers. Twenty healthy participants were gathered from different schools in Tehran using the available sampling method. The QEEG data collection was performed in three states: closed-eye and open-eye states, and during a working memory task. As expected, the power ratios did not show a significant difference between the two groups. Regarding our interest in the complexity of signals, we used the Higuchi algorithm as the feature extractor to provide the input materials for the multilayer perceptron classifier. The results showed that the model had at least a 95% precision rate in classifying the addicts and healthy controls in all three types of tasks. Moreover, significant differences in the Higuchi Fractal Dimension of a few EEG channels have been observed. This study confirms the importance of brain wave complexity in QEEG data analysis and assesses the correlation between EEG-complexity and gaming disorder. Moreover, feature extraction by Higuchi algorithm can render support vector machine classification of the brain waves of addicts and healthy controls more accurate.

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.

Neuromodulatory Effect of Transcranial Direct Current Stimulation on Resting-State EEG Activity in Internet Gaming Disorder: A Randomized, Double-Blind, Sham-Controlled Parallel Group Trial

Transcranial direct current stimulation (tDCS) has been used as an adjunct therapy for psychiatric disorders; however, little is known about the underlying neurophysiological effects of tDCS in Internet gaming disorder (IGD). We investigated the effects of tDCS on cortical activity using resting-state electroencephalography (EEG) in patients with IGD. This randomized, double-blind, sham-controlled parallel group study of tDCS (ClinicalTrials.gov NCT03347643) included 31 IGD patients. Participants received 10 sessions (2 sessions per day for 5 consecutive days) of active repetitive tDCS (2 mA for 20 min per session) or sham stimulation. Anode/cathode electrodes were placed over the left and right dorsolateral prefrontal cortex, respectively. In total, 26 participants (active group n = 14; sham group n = 12) completed the trial. Resting-state EEG spectral activity (absolute power) and functional connectivity (coherence) were used to assess the effects of tDCS on cortical activity before stimulation and 1 month after the intervention. Active stimulation of tDCS suppressed increase of intra-hemispheric beta coherence after 1 month, which was observed in the sham group. The 1-month follow-up assessment revealed that absolute gamma power in the left parietal region was decreased in the active group relative to the sham group. Our findings suggest that repetitive tDCS stabilizes fast-wave activity in IGD.

Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive-compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

Gaming disorder and internet addiction: A systematic review of resting-state EEG studies

Neurophysiological studies of Gaming Disorder (GD) and internet addiction (IA) are providing important insight into neurocognitive mechanisms underpinning these disorders, which will enable more accurate diagnostic classification. Electroencephalography (EEG) has been widely used to investigate addictive behaviours, and offers advantages of accessibility, low cost, and excellent temporal resolution. The present systematic review evaluates resting-state EEG studies in GD and IA. Papers (n = 7293) were identified in the PsychARTICLES, PsychINFO, Scopus, and Pubmed databases. Following inclusion/exclusion criteria, ten studies remained for evaluation. Results suggest individuals with GD have raised delta and theta activity and reduced beta activity, with coherence analysis suggesting altered brain activity in the mid-to-high frequency range. IA individuals demonstrate raised gamma activity and reduced beta and delta activity. Results suggest that the altered brain activity found in GD/IA may represent distinct underlying neurophysiological markers or traits, lending further support to their unique constructs. Results are also discussed in relation to relevant psychometric measurements and similar (higher frequency) activity found in substance addiction. Future research should focus on replicating the findings in a wider variety of cultural contexts to support the neurophysiological basis of classifying GD and IA.

Maladaptive neurovisceral interactions in patients with Internet gaming disorder: A study of heart rate variability and functional neural connectivity using the graph theory approach

Heart rate variability (HRV) can be used to represent the regulatory adaptive system and is a proxy for neurovisceral integration. Consistent with the view that, like other addictions, Internet gaming disorder (IGD) involves disrupted regulatory function, the present study hypothesized that IGD patients would show (a) decreased HRV, (b) ineffective functional neural connectivity, and (c) differential patterns of association between HRV and functional neural connectivity relative to healthy controls (HCs). The present study included 111 young adults (53 IGD patients and 58 age‐ and sex‐matched HCs) who underwent simultaneous recordings with an electrocardiogram and electroencephalogram during a resting state. Heart rate (HR), HRV, and functional neural connectivity were calculated using the graph theory approach. Compared with the HCs, the IGD patients exhibited elevated HR and decreased HRV based on the high frequency (HF), which reflects suppression of parasympathetic and/or vagal tone. The IGD patients also exhibited a heightened theta band characteristic path length (CPL) compared with HCs, indicating decreased efficacy of the functional network. Furthermore, IGD patients exhibited negative correlations between the standard deviation of the normal‐to‐normal interval index (SDNNi) and theta and delta CPL values, which were not observed in HCs. In conclusion, the present findings suggest that IGD patients might have maladaptive brain‐body integration features involving disruptions of the autonomic nervous system and brain function.

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.

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.

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.

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.

Neurophysiological Mechanisms of Resilience as a Protective Factor in Patients with Internet Gaming Disorder: A Resting-State EEG Coherence Study

Background: Resilience, an important protective factor against Internet gaming disorder (IGD), is the ability to recover from negative emotional experiences and constitutes a flexible adaptation to stress. Despite the importance of resilience in predicting IGD, little is known about the relationships between resilience and the neurophysiological features of IGD patients. Methods: We investigated these relationships using resting-state electroencephalography (EEG) coherence, by comparing IGD patients (n = 35) to healthy controls (n = 36). To identify the resilience-related EEG features, the IGD patients were divided into two groups based on the 50th percentile score on the Connor–Davidson Resilience Scale: IGD with low resilience (n = 16) and IGD with high resilience (n = 19). We analyzed differences in EEG coherence among groups for each fast frequency band. The conditional indirect effects of resilience were examined on the relationships between IGD and resilience-related EEG features through clinical symptoms. Results: IGD patients with low resilience had higher alpha coherence in the right hemisphere. Particularly, resilience moderated the indirect effects of IGD on alpha coherence in the right hemisphere through depressive symptoms and stress level. Conclusion: These neurophysiological findings regarding the mechanisms underlying resilience may help to establish effective preventive measures against IGD.

Electrophysiological activity is associated with vulnerability of Internet addiction in non-clinical population

This study investigated the electrophysiological activity associated with vulnerability of problematic Internet use in non-clinical population. The resting EEG spectrum of alpha (8-13 Hz) rhythm was measured in 22 healthy subjects who have used the Internet for recreational purpose. The vulnerability of Internet addiction was assessed using Young's Internet Addiction Test (IAT) and Assessment for Computer and Internet Addiction-Screener (AICA-S) respectively. Depression and impulsivity were also measured with Beck Depression Inventory (BDI) and Barratt Impulsiveness Scale 11(BIS-11) respectively. The IAT was positively correlated with alpha power obtained during eyes closed (EC, r = 0.50, p = 0.02) but not during eyes open (EO). This was further supported by a negative correlation (r = -0.48, p = 0.02) between IAT scores and alpha desynchronization (EO-EC). These relationships remained significant following correction for multiple comparisons. Furthermore, The BDI score showed positive correlation with alpha asymmetry at mid-lateral (r = 0.54, p = 0.01) and mid-frontal (r = 0.46, p = 0.03) regions during EC, and at mid-frontal (r = 0.53, p = 0.01) region during EO. The current findings suggest that there are associations between neural activity and the vulnerability of problematic Internet use. Understanding of the neurobiological mechanisms underlying problematic Internet use would contribute to improved early intervention and treatment.

EEG correlates associated with the severity of gambling disorder and serum BDNF levels in patients with gambling disorder

Background and aims This study aimed to evaluate the association between the severity of pathological gambling, serum brain-derived neurotrophic factor (BDNF) level, and the characteristics of quantitative electroencephalography (EEG) in patients with gambling disorder. Methods A total of 55 male patients aged 18-65 with gambling disorder participated. The severity of pathological gambling was assessed with the nine-item Problem Gambling Severity Index from the Canadian Problem Gambling Index (CPGI-PGSI). The Beck Depression Inventory and Lubben Social Network Scale were also assessed. Serum BDNF levels were assessed from blood samples. The resting-state EEG was recorded while the eyes were closed, and the absolute power of five frequency bands was analyzed: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-50 Hz). Results Serum BDNF level was positively correlated with theta power in the right parietal region (P4, r = .403, p = .011), beta power in the right parietal region (P4, r = .456, p = .010), and beta power in the right temporal region (T8, r = .421, p = .008). Gambling severity (CPGI-PGSI) was positively correlated with absolute beta power in the left frontal region (F7, r = .284, p = .043) and central region [(C3, r = .292, p = .038), (C4, r = .304, p = .030)]. Conclusions These findings support the hypothesis that right-dominant lateralized correlations between BDNF and beta and theta power reflect right-dominant brain activation in addiction. The positive correlations between beta power and the severity of gambling disorder may be associated with hyperexcitability and increased cravings. These findings contribute to a better understanding of brain-based electrophysiological changes and BDNF levels in patients with pathological gambling.

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.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.