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

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.

Alterations of gray matter asymmetry in internet gaming disorder

Structural asymmetry is a subtle but pervasive property of the human brain, which has been found altered in various psychiatric and neurocognitive disorders. However, little is known regarding potential alterations of structural asymmetry underlying internet gaming disorder (IGD). Therefore, this study aimed to investigate the structural features of gray matter asymmetry in IGD. High-resolution structural magnetic resonance imaging data were collected from 104 individuals with IGD and 104 recreational game users (RGUs). We applied a whole-brain voxel-based asymmetry (VBA) approach to determine the asymmetrical aberrations of gray matter in relation to IGD. Furthermore, the local abnormalities of structural asymmetry were employed as features to examine the effect of classification using a support vector machine (SVM). The results indicated that individuals with IGD as compared to RGUs showed asymmetrical alterations of gray matter in the medial prefrontal cortex (mPFC), orbitofrontal cortex, precuneus, middle temporal gyrus, superior parietal lobule and inferior temporal gyrus, regions implicated in hedonic motivation, self-reflection, information integration and visuospatial attention processing. Moreover, these atypical asymmetrical features can distinguish IGD subjects from RGUs with high accuracy. These results suggested that disrupted structural asymmetry of motivational reward, visuospatial and default mode circuits might be potential biomarkers for identifying pathological gaming dependence. These findings extended our understanding of structural underpinnings of IGD and provided new insights for developing effective interventions to alleviate compulsive gaming usage.

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.

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.

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.

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.

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.

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.