EEG response to game-craving according to personal preference for games

Watch and yearn? Effects of watching gambling livestreams on cravings

Background

Gambling content on streaming platforms has gained popularity. Given their intense, cue-laden nature, watching gambling streams may trigger cravings among viewers. At the same time, people who gamble may be motivated to watch gambling streams in an attempt to regulate their cravings.

Methods

We tested these ideas across two preregistered online studies, recruiting i) people who gamble to compare a subgroup of gambling stream viewers with non-viewers (Study 1; nviewers = 221, nnon-viewers = 642), and ii) a group of gambling stream viewers (Study 2; nviewers = 271).

Results

Gambling stream viewers were younger, tended to identify as men, and displayed higher levels of problem gambling and gambling cravings compared to non-viewers. Problem gambling severity was correlated positively with both the motivation to use gambling streams to regulate cravings and with cravings elicited by watching gambling streams.

Discussion

Our findings indicate that while viewers with higher levels of problem gambling may use gambling streams to regulate their cravings, doing so might evoke cravings.

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.

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.

Cue-reactivity to distal cues in individuals at risk for gaming disorder

BACKGROUND

Gaming disorder (GD) is a disorder due to addictive behaviors (ICD-11). Cue-reactivity and craving are relevant mechanisms in the development and maintenance of addictive behaviors. When confronted with cues showing in-game content (proximal cues) individuals with higher symptom severity show increased cue-reactivity. Based on conditioning and addiction theories on incentive sensitization, cue-reactivity responses may generalize to more distal cues, e.g. when individuals at risk of developing a GD are confronted with a starting page of an online game. In cue-reactivity paradigms so far, only proximal gaming cues have been used.

METHODS

We investigated the effect of distal gaming cues compared to gaming-unrelated control cues on cue-reactivity and craving in 88 individuals with non-problematic use of online games (nPGU) and 69 individuals at risk for GD (rGD). The distal cues showed the use of an electronic device (e.g., desktop PC or smartphone) whose screen showed starting pages of either games (target cues), shopping- or pornography sites (control cues) from a first-person perspective.

FINDINGS

We found significantly higher urge and arousal ratings as well as longer viewing times for gaming-related compared to gaming-unrelated control cues in rGD compared to nPGU. Valence ratings did not differ between groups.

INTERPRETATION

The results demonstrate that already distal gaming-specific cues lead to cue-reactivity and craving in rGD. This finding indicates that based on conditioning processes, cue-reactivity and craving develop during the course of GD and generalize to cues that are only moderately related to the specific gaming activity.

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

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