Differential resting-state EEG patterns associated with comorbid depression in Internet addiction

Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity

BACKGROUND

Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

METHODS

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

RESULTS

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

CONCLUSIONS

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

Individualized Spectral Features in First-Episode and Drug-Naïve Major Depressive Disorder: Insights From Periodic and Aperiodic Electroencephalography Analysis

BACKGROUND

The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences.

METHODS

In this study, we aimed to elucidate the pathological changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD. EEG data in the eyes-closed resting state were continuously recorded from 97 first-episode and drug-naïve patients with MDD and 90 healthy control participants. Both periodic oscillations and aperiodic components were obtained via the fitting oscillations and one-over f (FOOOF) algorithm and then used to compute individualized spectral features.

RESULTS

Patients with MDD presented higher canonical alpha and beta band power but lower aperiodic-adjusted alpha and beta power. Furthermore, we found that alpha power was strongly correlated with the age of patients but not with disease symptoms. The aperiodic intercept was lower in the parieto-occipital region and was positively correlated with Hamilton Depression Rating Scale scores after accounting for age and sex. In the asymmetry analysis, alpha activity appeared asymmetrical only in the healthy control group, whereas aperiodic activity was symmetrical in both groups.

CONCLUSIONS

The findings of this study provide insights into the role of abnormal neural spiking activity and impaired neuroplasticity in MDD progression and suggest that the aperiodic intercept in resting-state EEG may be a potential biomarker of MDD.

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.

Impact of Mobile Neurofeedback on Internet Addiction and Neurocognitive Function in Neurotypical Children: Double-Blind, Sham-Controlled Randomized Clinical Trial

Objective

The purpose of this study was to evaluate the positive impact of mobile neurofeedback (MNF) in neurotypical children compared to sham mobile neurofeedback.

Methods

Neurotypical children aged 10-15 participated in the study. All subjects were assessed using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version Korean Version (K-SADS-PL-K) and confirmed to have no psychiatric symptoms. The participants were randomly assigned to the MNF active (N=31) or sham control (N=30) groups. The MNF program was administered using a mobile app for 30 min/day, 3 days/week, for 3 months. All participants and their parents completed self-report scales and participants complete neurocognitive function assessments including the continuous performance test, Stroop, children's color trails test-1 and 2, and intelligence test at baseline and after the 3-month MNF program.

Results

This study involved 61 participants (mean [SD] age, 11.24 [1.84] years; 30 male participants [49.2%]). To verify the difference between the MNF group and the sham group, 2(MNF-Sham) X 2(Pre-Post) repeated measures ANOVA was performed. The main effect of the K-scale (Korea Internet addiction scale) between-group factor (MNF vs Sham) was not significant, but the main effect of the within-group factor (Pre vs Post) was significant (F=7.595, p=0.008). The interaction effect of between-group factors and within-group factors was also significant (F=5.979, p=0.017). In other self-reported scales of children and parents and neurocognitive function assessments, there was no significant difference between the two groups.

Conclusion

Active mobile neurofeedback significantly improved children's K-scale score compared to the sham group. Therefore, mobile neurofeedback could be an easy-to-access therapeutic option for children at risk of Internet addiction. On the other hand, there was no significant difference in other scales and neurocognitive function. A 3-month intervention may not have been long enough to cause change, so longer interventions are needed for confirmation.

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.

Problematic social media use is associated with depression and similar to behavioral addictions: Physiological and behavioral evidence

While many studies have examined the relationship between problematic social media use (PSMU) and mental health disorders, little is known about reward responsiveness mechanisms that might be driving this relationship and the neurophysiological characteristics of PSMU. We surveyed 96 undergraduate students at a private liberal arts college in upstate NY. PSMU was assessed using the Social Media Disorder Scale. Fourteen Individuals endorsing in five or more and three or less categories on the Social Media Disorder Scale were offered and underwent resting state QEEG. Mental health was assessed with the Center for Epidemiological Studies Depression Scale Short Form, Social Interaction Anxiety Scale, Penn State Worry Questionnaire, the 10-item Perceived Stress Scale, and a locally developed measure of Substance Use Disorder. Reward and motivational systems were studied using the Brief Sensation Seeking Scale, Behavioral Inhibition/Behavioral Activation Scale, and Temporal Experience of Pleasure Scale. SMDS scores were associated with poorer mental health on all measures except substance use. SMDS scores were positively associated with the behavioral inhibition scale, and the anticipatory pleasure scale. QEEG results revealed a negative association of high PSMU and right central and frontal lobeta, right central beta, and a positive association with frontal alpha asymmetry. The study replicates findings that PSMU is associated with mental health issues. Further the pattern of reward response is different compared with other addictive behaviors. QEEG results are consistent with previous work in substance use and depression.

SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition

Introduction

EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.

Methods

In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.

Results

To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.

Discussion

Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.

Electrophysiological correlates of dispositional mindfulness: A quantitative and complexity EEG study

While growing evidence supports that dispositional mindfulness relates to psychological health and cognitive enhancement, to date there have been only a few attempts to characterize its neural underpinnings. In the present study, we aimed at exploring the electrophysiological (EEG) signature of dispositional mindfulness using quantitative and complexity measures of EEG during resting state and while performing a learning task. Hundred twenty participants were assessed with the Five Facet Mindfulness Questionnaire and underwent 5 min eyes-closed resting state and 5 min at task EEG recording. We hypothesized that high mindfulness individuals would show patterns of brain activity related to (a) lower involvement of the default mode network (DMN) at rest (reduced frontal gamma power) and (b) a state of 'task readiness' reflected in a more similar pattern from rest to task (reduced overall q-EEG power at rest but not at task), as compared to their low mindfulness counterparts. Dispositional mindfulness was significantly linked to reduced frontal gamma power at rest and lower overall power during rest but not at task. In addition, we found a trend towards higher entropy during task performance in mindful individuals, which has recently been reported during mindfulness meditation. Altogether, our results add to those from expert meditators to show that high (dispositional) mindfulness seems to have a specific electrophysiological pattern characteristic of less involvement of the DMN and mind-wandering processes.

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.

The Impact of Mental Health Predictors of Internet Addiction among Pre-Service Teachers in Ghana

This study examined the prevalence of addictive Internet behavior and its links with mental health among pre-service teachers in Ghana. A descriptive, correlational design was employed with 405 pre-service teachers from colleges of education and a public university in Ghana participating in this study. The sample completed a sociodemographic survey about loneliness, life satisfaction, depression, self-esteem, and the Internet addiction scales (abridged form). The results revealed that there was a significant relationship between pre-service teachers' Internet addiction, depression, life satisfaction, and loneliness; however, depression was the least influential factor in addictive Internet use. Additionally, there was a statistically significant nexus between self-esteem, loneliness, depression, and life satisfaction. In addition, all the above-mentioned variables were discovered to explain 56.3% of the absolute variance in addiction to the Internet. Among the variables linked with Internet addiction and its dimensions, loneliness appeared to be the most significant. Institutional coping programs with Internet addiction should be established within the scope of the university administration, supporting pre-service teachers' mental health. Finally, the development of awareness campaigns on the menaces associated with Internet usage and mental health through extracurricular programs is recommended.

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.

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.

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.

Profiling of pornography addiction among children using EEG signals: A systematic literature review

Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.

Cybervictimization, Depression, and Adolescent Internet Addiction: The Moderating Effect of Prosocial Peer Affiliation

Although vast research has shown that cybervictimization is a significant risk factor of adolescent’s internet addiction (IA), little is known about the mediating and moderating mechanisms behind this relationship. The current study examined whether depression mediated the relationship between cybervictimization and adolescent’s IA, and whether the direct and indirect effect was moderated by prosocial peer affiliation (PPA). A sample of 1,006 adolescents (Mean_age = 13.16; SD = 0.67) anonymously completed the questionnaires. The results revealed that the positive association between cybervictimization and adolescent’s IA was mediated by depression. Moderated mediation analysis further showed that PPA moderated the association between cybervictimization and adolescent’s IA. However, this indirect effect was stronger for adolescents with high PPA than for those with low PPA, which means that the protective effects of PPA are limited. These findings highlight the mediating and moderating mechanisms between cybervictimization and adolescent’s IA, and provide guidance for the prevention and intervention in adolescent’s IA.

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.

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.

[Neurophysiological and clinico-biological features of internet addiction]

AIM

To analyse neurophysiological and some physiological characteristics of people with Internet addiction.

MATERIAL AND METHODS

Two groups of subjects were studied: with Internet-addiction lasted no more than two years and the control group. Spectral-correlation parameters of EEG, functional asymmetry of EEG parameters, and heart rate variability were recorded. The comparison was performed in three states: eyes-closed, eyes-open conditions and after a 15-minute Internet session.

RESULTS AND CONCLUSION

The shift in the balance of the regulation of the heart rate towards the predominance of the sympathetic nervous system is accompanied by a functional state of increased activation, anxiety as indicated by the parameters of the electric activity of the brain and the shift in the functional asymmetry of the brain in the spectral power of the fast EEG rhythms in the right hemisphere.

Diminished Frontal Theta Activity During Gaming in Young Adults With Internet Gaming Disorder

Cognitive control is essential for flexible, top-down, goal-directed behavior. Individuals with Internet gaming disorder (IGD) are characterized by impaired prefrontal cortex function and cognitive control. This results in an increase in stimulus-driven habitual behavior, particularly related to pathological gaming. In the present study, we investigated the electroencephalographic (EEG) activity in individuals with IGD. Twenty-four individuals with IGD and 35 healthy control (HC) subjects were recruited. We analyzed their EEG activity while the subjects played their favorite game (30–40 min duration). We compared the band power between the two groups. During gaming, the left frontal theta, alpha, and beta band activities were lower in subjects with IGD than in HCs. Moreover, the left frontal theta power negatively correlated with IGD severity. These results indicate that left frontal theta power could be used as a neurophysiological biomarker for the detection of diminished cognitive control patterns in individuals with IGD.

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.

Prevalence and correlates of comorbid depression in a nonclinical online sample with DSM-5 internet gaming disorder

BACKGROUND

We investigated the prevalence and correlates of comorbid depression among patients with internet gaming disorder using the Internet Gaming Disorder scale (IGD-9) and the Patient Health Questionnaire-9 (PHQ-9) among nonclinical online survey respondents.

METHODS

Korean adolescents and adults from 14 to 39 years of age were selected. We compared internet gaming use patterns and sociodemographic and clinical variables between patients with internet gaming disorder who had depression and those without depression.

RESULTS

In 2016, 7200 people participated in an online survey. Respondents with internet gaming disorder that was comorbid with depression were older, more often female, had greater Internet Addiction Test total scores, Alcohol Use Disorder Identification Test total scores, Generalized Anxiety Disorder Scale-7 total scores, Fagerstrom Test for Nicotine Dependence total scores, and higher Dickman Dysfunctional Impulsivity Instrument dysfunctional subscale scores than those without depression. The binary logistic regression analysis revealed that female gender, problematic alcohol use, anxiety, and a past history of psychiatric counseling or treatment due to internet gaming use were significant predictors for comorbid depression among participants with internet gaming disorder.

CONCLUSION

Depression was a common comorbidity of internet gaming disorder. Internet gaming disorder with comorbid depression was related to more serious psychiatric phenomenology and a greater psychiatric burden.

An endophenotype approach to the genetics of alcohol dependence: a genome wide association study of fast beta EEG in families of African ancestry

Fast beta (20-28 Hz) electroencephalogram (EEG) oscillatory activity may be a useful endophenotype for studying the genetics of disorders characterized by neural hyperexcitability, including substance use disorders (SUDs). However, the genetic underpinnings of fast beta EEG have not previously been studied in a population of African-American ancestry (AA). In a sample of 2382 AA individuals from 482 families drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed a genome-wide association study (GWAS) on resting-state fast beta EEG power. To further characterize our genetic findings, we examined the functional and clinical/behavioral significance of GWAS variants. Ten correlated single-nucleotide polymorphisms (SNPs) (r2>0.9) located in an intergenic region on chromosome 3q26 were associated with fast beta EEG power at P<5 × 10-8. The most significantly associated SNP, rs11720469 (β: -0.124; P<4.5 × 10-9), is also an expression quantitative trait locus for BCHE (butyrylcholinesterase), expressed in thalamus tissue. Four of the genome-wide SNPs were also associated with Diagnostic and Statistical Manual of Mental Disorders Alcohol Dependence in COGA AA families, and two (rs13093097, rs7428372) were replicated in an independent AA sample (Gelernter et al.). Analyses in the AA adolescent/young adult (offspring from COGA families) subsample indicated association of rs11720469 with heavy episodic drinking (frequency of consuming 5+ drinks within 24 h). Converging findings presented in this study provide support for the role of genetic variants within 3q26 in neural and behavioral disinhibition. These novel genetic findings highlight the importance of including AA populations in genetics research on SUDs and the utility of the endophenotype approach in enhancing our understanding of mechanisms underlying addiction susceptibility.

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.

Childhood Trauma Associated with Enhanced High Frequency Band Powers and Induced Subjective Inattention of Adults

Childhood trauma can lead to various psychological and cognitive symptoms. It has been demonstrated that high frequency electroencephalogram (EEG) powers could be closely correlated with inattention. In this study, we explored the relationship between high frequency EEG powers, inattention, symptoms of adult attention deficit hyperactivity disorder (ADHD), and childhood traumatic experiences. A total of 157 healthy Korean adult volunteers were included and divided into two groups using the Childhood Trauma Questionnaire (CTQ) score. The subjective inattention scores, ADHD scale, and anxiety and depression symptom were evaluated. EEG was recorded and quantitative band powers were analyzed. The results were as follows: (1) the high CTQ group showed significantly increased delta, beta1, beta2, beta3 and gamma, and significantly decreased low alpha power compared to the low CTQ group; (2) the high CTQ group had higher inattention score compared to the low CTQ group; (3) the high CTQ group had higher adult ADHD scores; (4) CTQ scores showed significant positive correlations with inattention scores, and adult ADHD scores; (5) unexpectedly, the inattention scores showed significant positive correlations with beta powers and a negative correlation with low alpha power; and (6) the moderated mediation model was confirmed: the depression fully mediated the path from state anxiety to inattention, and the CTQ significantly moderated the pathway between anxiety and depression. Our results show the possibility that childhood adversity may cause subjective inattention and adult ADHD symptoms. Depressive symptoms fully mediated the path from anxiety to inattention, especially in those who report severe childhood traumatic experiences.

A genome wide association study of fast beta EEG in families of European ancestry

BACKGROUND

Differences in fast beta (20-28Hz) electroencephalogram (EEG) oscillatory activity distinguish some individuals with psychiatric and substance use disorders, suggesting that it may be a useful endophenotype for studying the genetics of disorders characterized by neural hyper-excitability. Despite the high heritability estimates provided by twin and family studies, there have been relatively few genetic studies of beta EEG, and to date only one genetic association finding has replicated (i.e., GABRA2).

METHOD

In a sample of 1564 individuals from 117 families of European Ancestry (EA) drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed a Genome-Wide Association Study (GWAS) on resting-state fronto-central fast beta EEG power, adjusting regression models for family relatedness, age, sex, and ancestry. To further characterize genetic findings, we examined the functional and behavioral significance of GWAS findings.

RESULTS

Three intronic variants located within DSE (dermatan sulfate epimerase) on 6q22 were associated with fast beta EEG at a genome wide significant level (p<5×10-8). The most significant SNP was rs2252790 (p<2.6×10-8; MAF=0.36; β=0.135). rs2252790 is an eQTL for ROS1 expressed most robustly in the temporal cortex (p=1.2×10-6) and for DSE/TSPYL4 expressed most robustly in the hippocampus (p=7.3×10-4; β=0.29). Previous studies have indicated that DSE is involved in a network of genes integral to membrane organization; gene-based tests indicated that several variants within this network (i.e., DSE, ZEB2, RND3, MCTP1, and CTBP2) were also associated with beta EEG (empirical p<0.05), and of these genes, ZEB2 and CTBP2 were associated with DSM-V Alcohol Use Disorder (AUD; empirical p<0.05).'

DISCUSSION

In this sample of EA families enriched for AUDs, fast beta EEG is associated with variants within DSE on 6q22; the most significant SNP influences the mRNA expression of DSE and ROS1 in hippocampus and temporal cortex, brain regions important for beta EEG activity. Gene-based tests suggest evidence of association with related genes, ZEB2, RND3, MCTP1, CTBP2, and beta EEG. Converging data from GWAS, gene expression, and gene-networks presented in this study provide support for the role of genetic variants within DSE and related genes in neural hyperexcitability, and has highlighted two potential candidate genes for AUD and/or related neurological conditions: ZEB2 and CTBP2. However, results must be replicated in large, independent samples.

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

The identification of the predictive factors and biological markers associated with treatment-related changes in the symptoms of Internet gaming disorder (IGD) may provide a better understanding of the pathophysiology underlying this condition. Thus, the present study aimed to identify neurophysiological markers associated with symptom changes in IGD patients and to identify factors that may predict symptom improvements following outpatient treatment with pharmacotherapy. The present study included 20 IGD patients (mean age: 22.71 ± 5.47 years) and 29 healthy control subjects (mean age: 23.97 ± 4.36 years); all IGD patients completed a 6-month outpatient management program that included pharmacotherapy with selective serotonin reuptake inhibitors. Resting-state electroencephalography scans were acquired prior to and after treatment, and the primary treatment outcome was changes in scores on Young's Internet Addiction Test (IAT) from pre- to posttreatment. IGD patients showed increased resting-state electroencephalography activity in the delta and theta bands at baseline, but the increased delta band activity was normalized after 6 months of treatment and was significantly correlated with improvements in IGD symptoms. Additionally, higher absolute theta activity at baseline predicted a greater possibility of improvement in addiction symptoms following treatment, even after adjusting for the effects of depressive or anxiety symptoms. The present findings demonstrated that increased slow-wave activity represented a state neurophysiological marker in IGD patients and suggested that increased theta activity at baseline may be a favorable prognostic marker for this population.

Cell-Phone Addiction: A Review

We present a review of the studies that have been published about addiction to cell phones. We analyze the concept of cell-phone addiction as well as its prevalence, study methodologies, psychological features, and associated psychiatric comorbidities. Research in this field has generally evolved from a global view of the cell phone as a device to its analysis via applications and contents. The diversity of criteria and methodological approaches that have been used is notable, as is a certain lack of conceptual delimitation that has resulted in a broad spread of prevalent data. There is a consensus about the existence of cell-phone addiction, but the delimitation and criteria used by various researchers vary. Cell-phone addiction shows a distinct user profile that differentiates it from Internet addiction. Without evidence pointing to the influence of cultural level and socioeconomic status, the pattern of abuse is greatest among young people, primarily females. Intercultural and geographical differences have not been sufficiently studied. The problematic use of cell phones has been associated with personality variables, such as extraversion, neuroticism, self-esteem, impulsivity, self-identity, and self-image. Similarly, sleep disturbance, anxiety, stress, and, to a lesser extent, depression, which are also associated with Internet abuse, have been associated with problematic cell-phone use. In addition, the present review reveals the coexistence relationship between problematic cell-phone use and substance use such as tobacco and alcohol.

Behavioral addictions in addiction medicine: from mechanisms to practical considerations

Recent progress has been made in our understanding of nonsubstance or "behavioral" addictions, although these conditions and their most appropriate classification remain debated and the knowledge basis for understanding the pathophysiology of and treatments for these conditions includes important gaps. Recent developments include the classification of gambling disorder as a "Substance-Related and Addictive Disorder" in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and proposed diagnostic criteria for Internet Gaming Disorder in Section 3 of DSM-5. This chapter reviews current neuroscientific understandings of behavioral addictions and the potential of neurobiological data to assist in the development of improved policy, prevention, and treatment efforts.

Neuroscience of Internet Pornography Addiction: A Review and Update

Many recognize that several behaviors potentially affecting the reward circuitry in human brains lead to a loss of control and other symptoms of addiction in at least some individuals. Regarding Internet addiction, neuroscientific research supports the assumption that underlying neural processes are similar to substance addiction. The American Psychiatric Association (APA) has recognized one such Internet related behavior, Internet gaming, as a potential addictive disorder warranting further study, in the 2013 revision of their Diagnostic and Statistical Manual. Other Internet related behaviors, e.g., Internet pornography use, were not covered. Within this review, we give a summary of the concepts proposed underlying addiction and give an overview about neuroscientific studies on Internet addiction and Internet gaming disorder. Moreover, we reviewed available neuroscientific literature on Internet pornography addiction and connect the results to the addiction model. The review leads to the conclusion that Internet pornography addiction fits into the addiction framework and shares similar basic mechanisms with substance addiction. Together with studies on Internet addiction and Internet Gaming Disorder we see strong evidence for considering addictive Internet behaviors as behavioral addiction. Future research needs to address whether or not there are specific differences between substance and behavioral addiction.

Neurophysiological features of Internet gaming disorder and alcohol use disorder: a resting-state EEG study

Despite that Internet gaming disorder (IGD) shares clinical, neuropsychological and personality characteristics with alcohol use disorder (AUD), little is known about the resting-state quantitative electroencephalography (QEEG) patterns associated with IGD and AUD. Therefore, this study compared the QEEG patterns in patients with IGD with those in patients with AUD to identify unique neurophysiological characteristics that can be used as biomarkers of IGD. A total of 76 subjects (34 with IGD, 17 with AUD and 25 healthy controls) participated in this study. Resting-state, eyes-closed QEEGs were recorded, and the absolute and relative power of brains were analyzed. The generalized estimating equation showed that the IGD group had lower absolute beta power than AUD (estimate = 5.319, P < 0.01) and the healthy control group (estimate = 2.612, P = 0.01). The AUD group showed higher absolute delta power than IGD (estimate = 7.516, P < 0.01) and the healthy control group (estimate = 7.179, P < 0.01). We found no significant correlations between the severity of IGD and QEEG activities in patients with IGD. The current findings suggest that lower absolute beta power can be used as a potential trait marker of IGD. Higher absolute power in the delta band may be a susceptibility marker for AUD. This study clarifies the unique characteristics of IGD as a behavioral addiction, which is distinct from AUD, by providing neurophysiological evidence.

Internet Addiction in Young People

In our technology-savvy population, mental health professionals are seeing an increasing trend of excessive Internet use or Internet addiction. Researchers in China, Taiwan and Korea have done extensive research in the field of Internet addiction. Screening instruments are available to identify the presence of Internet addiction and its extent. Internet addiction is frequently associated with mental illnesses such as anxiety, depression, conduct disorder and attention deficit hyperactivity disorder (ADHD). Treatment modalities include individual and group therapies, cognitive behavioural therapy (CBT), family therapy and psychotropic medications. A significant proportion of Singapore adolescents engaging in excessive Internet use are also diagnosed to have concomitant Internet addiction. Despite the presence of a variety of treatment options, future research in this area is needed to address its growing trend and to minimise its negative psychological and social impact on the individuals and their families. Key words: Aetiology, Pathophysiology, Treatment