Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder.

Background: An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Method: Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. Results: The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. Discussion and Conclusions: The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Bridging the Gap Between Self-Report and Behavioral Laboratory Measures: A Real-Time Driving Task With Inverse Reinforcement Learning.

A major challenge in assessing psychological constructs such as impulsivity is the weak correlation between self-report and behavioral task measures that are supposed to assess the same construct. To address this issue, we developed a real-time driving task called the "highway task," in which participants often exhibit impulsive behaviors mirroring real-life impulsive traits captured by self-report questionnaires. Here, we show that a self-report measure of impulsivity is highly correlated with performance in the highway task but not with traditional behavioral task measures of impulsivity (47 adults aged 18-33 years). By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The results indicated that impulsive participants attribute high subjective rewards to irrational or risky situations. Overall, our results suggest that using real-time tasks combined with IRL can help reconcile the discrepancy between self-report and behavioral task measures of psychological constructs.

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).

Early life stress modulates the genetic influence on brain structure and cognitive function in children.

The enduring influence of early life stress (ELS) on brain and cognitive development has been widely acknowledged, yet the precise mechanisms underlying this association remain elusive. We hypothesize that ELS might disrupt the genome-wide influence on brain morphology and connectivity development, consequently exerting a detrimental impact on children's cognitive ability. We analyzed the multimodal data of DNA genotypes, brain imaging (structural and diffusion MRI), and neurocognitive battery (NIH Toolbox) of 4276 children (ages 9-10 years, European ancestry) from the Adolescent Brain Cognitive Development (ABCD) study. The genome-wide influence on cognitive function was estimated using the polygenic score (GPS). By using brain morphometry and tractography, we identified the brain correlates of the cognition GPSs. Statistical analyses revealed relationships for the gene-brain-cognition pathway. The brain structural variance significantly mediated the genetic influence on cognition (indirect effect = 0.016, PFDR < 0.001). Of note, this gene-brain relationship was significantly modulated by abuse, resulting in diminished cognitive capacity (Index of Moderated Mediation = -0.007; 95 % CI = -0.012 âˆ¼ -0.002). Our results support a novel gene-brain-cognition model likely elucidating the long-lasting negative impact of ELS on children's cognitive development.

Acute Stress Enhances Memory and Preference for Smoking-Related Associations in Smokers.

INTRODUCTION: Nicotine dependence follows a chronic course that is characterized by repeated relapse, often driven by acute stress and rewarding memories of smoking retrieved from related contexts. These two triggers can also interact, with stress influencing retrieval of contextual memories. However, the roles of these processes in nicotine dependence remain unknown. AIMS AND METHODS: We investigated how acute stress biases memory for smoking-associated contexts among smokers (N = 65) using a novel laboratory paradigm. On day 1, participants formed associations between visual stimuli of items (either neutral or related to smoking) and places (background scenes). On day 2 (24 hours later), participants were exposed to an acute laboratory-based stressor (socially evaluated cold pressor test; N = 32) or a matched control condition (N = 33) prior to being tested on their memory recognition and preferences for each item and place. We distinguished the accuracy of memory into specific (ie, precisely correct) or gist (ie, lure items with similar content) categories. RESULTS: Results demonstrated that the stressor significantly induced physiological and subjective perceived stress responses, and that stressed smokers exhibited a memory bias in favor of smoking-related items. In addition, the stressed group displayed greater preference for both smoking-related items and places that had been paired with the smoking-related items. We also found suggestive evidence that stronger smoking-related memory biases were associated with more severe nicotine dependence (ie, years of smoking). CONCLUSIONS: These results highlight the role of stress in biasing smokers toward remembering contexts associated with smoking, and amplifying their preference for these contexts. IMPLICATIONS: The current study elucidates the role of acute stress in promoting memory biases favoring smoking-related associations among smokers. The results suggest that the retrieval of smoking-biased associative memory could be a crucial factor in stress-related nicotine seeking. This may lead to a potential intervention targeting the extinction of smoking-related context memories as a preventive strategy for stress-induced relapse.

Caffeinated Soda Intake in Children Is Associated with Neurobehavioral Risk Factors for Substance Misuse.

BACKGROUND AND OBJECTIVES: Use of psychotropic substances in childhood has been associated with both impulsivity and other manifestations of poor executive function as well as escalation over time to use of progressively stronger substances. However, how this relationship may start in earlier childhood has not been well explored. Here, we investigated the neurobehavioral correlates of daily caffeinated soda consumption in preadolescent children and examined whether caffeinated soda intake is associated with a higher risk of subsequent alcohol initiation. METHODS: Using Adolescent Brain Cognitive Development study data (N = 2,092), we first investigated cross-sectional relationships between frequent caffeinated soda intake and well-known risk factors of substance misuse: impaired working memory, high impulsivity, and aberrant reward processing. We then examined whether caffeinated soda intake at baseline predicts more alcohol sipping at 12 months follow-up using a machine learning algorithm. RESULTS: Daily consumption of caffeinated soda was cross-sectionally associated with neurobehavioral risk factors for substance misuse such as higher impulsivity scores and lower working memory performance. Furthermore, caffeinated soda intake predicted a 2.04 times greater likelihood of alcohol sipping after 12 months, even after controlling for rates of baseline alcohol sipping rates. CONCLUSIONS: These findings suggest that previous linkages between caffeine and substance use in adolescence also extend to younger initiation, and may stem from core neurocognitive features thought conducive to substance initiation.

The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning.

Research suggests that a fast, capacity-limited working memory (WM) system and a slow, incremental reinforcement learning (RL) system jointly contribute to instrumental learning. Thus, situations that strain WM resources alter instrumental learning: under WM loads, learning becomes slow and incremental, the reliance on computationally efficient learning increases, and action selection becomes more random. It is also suggested that Pavlovian learning influences people's behavior during instrumental learning by providing hard-wired instinctive responses including approach to reward predictors and avoidance of punishment predictors. However, it remains unknown how constraints on WM resources affect instrumental learning under Pavlovian influence. Thus, we conducted a functional magnetic resonance imaging (fMRI) study (N = 49) in which participants completed an instrumental learning task with Pavlovian-instrumental conflict (the orthogonalized go/no-go task) both with and without extra WM load. Behavioral and computational modeling analyses revealed that WM load reduced the learning rate and increased random choice, without affecting Pavlovian bias. Model-based fMRI analysis revealed that WM load strengthened RPE signaling in the striatum. Moreover, under WM load, the striatum showed weakened connectivity with the ventromedial and dorsolateral prefrontal cortex when computing reward expectations. These results suggest that the limitation of cognitive resources by WM load promotes slow and incremental learning through the weakened cooperation between WM and RL; such limitation also makes action selection more random, but it does not directly affect the balance between instrumental and Pavlovian systems.

Correction: Causal role of the dorsolateral prefrontal cortex in modulating the balance between Pavlovian and instrumental systems in the punishment domain.

[This corrects the article DOI: 10.1371/journal.pone.0286632.].

Biopsychosocial factors of gaming disorder: a systematic review employing screening tools with well-defined psychometric properties.

Background and aims: Considering the growing number of gamers worldwide and increasing public concerns regarding the negative consequences of problematic gaming, the aim of the present systematic review was to provide a comprehensive overview of gaming disorder (GD) by identifying empirical studies that investigate biological, psychological, and social factors of GD using screening tools with well-defined psychometric properties. Materials and methods: A systematic literature search was conducted through PsycINFO, PubMed, RISS, and KISS, and papers published up to January 2022 were included. Studies were screened based on the GD diagnostic tool usage, and only five scales with well-established psychometric properties were included. A total of 93 studies were included in the synthesis, and the results were classified into three groups based on biological, psychological, and social factors. Results: Biological factors (n = 8) included reward, self-concept, brain structure, and functional connectivity. Psychological factors (n = 67) included psychiatric symptoms, psychological health, emotion regulation, personality traits, and other dimensions. Social factors (n = 29) included family, social interaction, culture, school, and social support. Discussion: When the excess amount of assessment tools with varying psychometric properties were controlled for, mixed results were observed with regards to impulsivity, social relations, and family-related factors, and some domains suffered from a lack of study results to confirm any relevant patterns. Conclusion: More longitudinal and neurobiological studies, consensus on a diagnostic tool with well-defined psychometric properties, and an in-depth understanding of gaming-related factors should be established to settle the debate regarding psychometric weaknesses of the current diagnostic system and for GD to gain greater legitimacy in the field of behavioral addiction.

Causal role of the dorsolateral prefrontal cortex in modulating the balance between Pavlovian and instrumental systems in the punishment domain.

Previous literature suggests that a balance between Pavlovian and instrumental decision-making systems is critical for optimal decision-making. Pavlovian bias (i.e., approach toward reward-predictive stimuli and avoid punishment-predictive stimuli) often contrasts with the instrumental response. Although recent neuroimaging studies have identified brain regions that may be related to Pavlovian bias, including the dorsolateral prefrontal cortex (dlPFC), it is unclear whether a causal relationship exists. Therefore, we investigated whether upregulation of the dlPFC using transcranial current direct stimulation (tDCS) would reduce Pavlovian bias. In this double-blind study, participants were assigned to the anodal or the sham group; they received stimulation over the right dlPFC for 3 successive days. On the last day, participants performed a reinforcement learning task known as the orthogonalized go/no-go task; this was used to assess each participant's degree of Pavlovian bias in reward and punishment domains. We used computational modeling and hierarchical Bayesian analysis to estimate model parameters reflecting latent cognitive processes, including Pavlovian bias, go bias, and choice randomness. Several computational models were compared; the model with separate Pavlovian bias parameters for reward and punishment domains demonstrated the best model fit. When using a behavioral index of Pavlovian bias, the anodal group showed significantly lower Pavlovian bias in the punishment domain, but not in the reward domain, compared with the sham group. In addition, computational modeling showed that Pavlovian bias parameter in the punishment domain was lower in the anodal group than in the sham group, which is consistent with the behavioral findings. The anodal group also showed a lower go bias and choice randomness, compared with the sham group. These findings suggest that anodal tDCS may lead to behavioral suppression or change in Pavlovian bias in the punishment domain, which will help to improve comprehension of the causal neural mechanism.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting.

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Psychological well-being and salivary markers of inflammation: The moderating effect of age.

Increasing evidence suggests a significant impact of higher psychological well-being (PWB) on health outcomes; however, such associations have been studied exclusively in middle-aged to older adults. This study examined the aging effect on PWB measures as well as the moderating effect of age on the link between PWB and inflammation, using salivary markers by comparing the younger adults (n = 127; Mage = 22.98 years) versus older adults (n = 75; Mage = 75.60 years). Older adults showed significantly lower levels of PWB, particularly regarding purpose in life and personal growth. Moreover, higher purpose in life was associated with lower salivary IL-1ß and IL-6 (b = 0.83, p < .001; b = 0.81, p < .01) only in the older adult group but not in younger adults. These findings highlight the potential buffering effect of the sense of living well on physiological pathways in later life.

Coordinated anatomical and functional variability in the human brain during adolescence.

Adolescence represents a time of unparalleled brain development. In particular, developmental changes in morphometric and cytoarchitectural features are accompanied by maturation in the functional connectivity (FC). Here, we examined how three facets of the brain, including myelination, cortical thickness (CT), and resting-state FC, interact in children between the ages of 10 and 15. We investigated the pattern of coordination in these measures by computing correlation matrices for each measure as well as meta-correlations among them both at the regional and network levels. The results revealed consistently higher meta-correlations among myelin, CT, and FC in the sensory-motor cortical areas than in the association cortical areas. We also found that these meta-correlations were stable and little affected by age-related changes in each measure. In addition, regional variations in the meta-correlations were consistent with the previously identified gradient in the FC and therefore reflected the hierarchy of cortical information processing, and this relationship persists in the adult brain. These results demonstrate that heterogeneity in FC among multiple cortical areas are closely coordinated with the development of cortical myelination and thickness during adolescence.

Psychological treatments for excessive gaming: a systematic review and meta-analysis.

Despite widespread public interest in problematic gaming interventions, questions regarding the empirical status of treatment efficacy persist. We conducted pairwise and network meta-analyses based on 17 psychological intervention studies on excessive gaming (n = 745 participants). The pairwise meta-analysis showed that psychological interventions reduce excessive gaming more than the inactive control (standardized mean difference [SMD] = 1.70, 95% confidence interval [CI] 1.27 to 2.12) and active control (SMD = 0.88, 95% CI 0.21 to 1.56). The network meta-analysis showed that a combined treatment of Cognitive Behavioral Therapy (CBT) and Mindfulness was the most effective intervention in reducing excessive gaming, followed by a combined CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment. Due to the limited number of included studies and resulting identified methodological concerns, the current results should be interpreted as preliminary to help support future research focused on excessive gaming interventions. Recommendations for improving the methodological rigor are also discussed.

A Randomized, Double-Blind, Sham-Controlled Trial of Transcranial Direct Current Stimulation for the Treatment of Persistent Postural-Perceptual Dizziness (PPPD).

Background: Persistent postural-perceptual dizziness (PPPD) is a functional vestibular disorder that causes chronic dizziness interfering with daily activities. Transcranial direct current stimulation (tDCS) has reportedly improved dizziness in patients with phobic postural vertigo in an open-label trial. However, no randomized, double-blind, sham-controlled study has been conducted on its therapeutic efficacy in PPPD. Objective: This study was conducted to investigate the efficacy and safety of tDCS as an add-on treatment to pharmacotherapy in patients with PPPD. In addition, functional neuroimaging was used to identify the neural mechanisms underlying the effects of tDCS. Materials and Methods: In a randomized, double-blind, sham-controlled trial, 24 patients diagnosed with PPPD were randomized to receive active (2 mA, 20 min) or sham tDCS to the left dorsolateral prefrontal cortex (DLPFC), administered in 15 sessions over 3 weeks. The clinical measures that assess the severity of dizziness, depression, and anxiety were collected at baseline, immediate follow-up, 1-month follow-up, and 3-month follow-up. Adverse events were also observed. The effect of tDCS on regional cerebral blood flow (rCBF) was evaluated with single photon emission tomography before and after tDCS sessions. Results: For the primary outcome measure of the Dizziness Handicap Inventory (DHI) score, a significant main effect of time was found, but neither the treatment-by-time interaction effect nor the main effect of treatment was significant. For the Hamilton Depression Rating Scale (HDRS) score, there was a statistical significance for the treatment-by-time interaction effect and the main effect of time, but not for the main effect of treatment. However, the treatment-by-time interaction effect and the main effect of time on HDRS score appear to be due to one data point, an increase in depressive symptoms reported by the sham group at the 3-month follow-up. For the Activities-specific Balance Confidence (ABC) Scale and the Hamilton Anxiety Rating Scale scores, there were no significant main effects of time, treatment, and treatment-by-time interaction. In a comparison with the changes in rCBF between the groups, a significant treatment-by-time interaction effect was found in the right superior temporal and left hippocampus, controlling for age and sex. Conclusion: Active tDCS was not found to be significantly more efficacious than sham tDCS on dizziness symptoms in patients with PPPD. It is conceivable that tDCS targeting the DLPFC may not be an optimal treatment option for reducing dizziness symptoms in PPPD. Our findings encourage further investigation on the effects of tDCS in PPPD, which considers different stimulation protocols in terms of stimulation site or the number of sessions. Clinical Trial Registration: cris.nih.go.kr, identifier: KCT0005068.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (pfdr < .001, ηp2  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (pfdr < .001, ηp2 < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Association of Genome-Wide Polygenic Scores for Multiple Psychiatric and Common Traits in Preadolescent Youths at Risk of Suicide.

Importance: Suicide is the second leading cause of death among youths worldwide, but no available means exist to identify the risk of suicide in this population. Objective: To assess whether genome-wide polygenic scores for psychiatric and common traits are associated with the risk of suicide among preadolescent children and to investigate whether and to what extent the interaction between early life stress (a major environmental risk factor) and polygenic factors is associated with suicidal thoughts and behaviors among youths. Design, Setting, and Participants: This cohort study analyzed the genotype-phenotype data of 11 869 preadolescent children aged 9 to 10 years from the Adolescent Brain and Cognitive Development study. Data were collected from September 1, 2016, to October 21, 2018, and analyzed from August 1, 2020, to January 3, 2021. Using machine learning approaches, genome-wide polygenic scores of 24 complex traits were estimated to investigate their phenome-wide associations and utility for assessing risk of suicidal thoughts and behaviors (suicidal ideation [active, passive, and overall] and suicide attempt). Main Outcomes and Measures: Genome-wide polygenic scores were used to measure 24 traits, including psychiatric disorders, cognitive capacity, and personality and psychological characteristics. The Child Behavior Checklist was used to measure early life stress, and the Family Environment Scale was used to assess family environment. Suicidal ideation and suicide attempts were derived from the computerized version of the Kiddie Schedule for Affective Disorders and Schizophrenia. Results: Among 11 869 preadolescent children in the US, complete data for phenotypic outcomes, genotypes, and covariates were available for 7140 participants in the multiethnic cohort (mean [SD] age, 9.9 [0.6] years; 3588 girls [50.3%]), including 925 participants with suicidal ideation and 63 participants with suicide attempts. Among those 7140 participants, 729 had African ancestry (self-reported race or ethnicity: 569 Black, 71 Hispanic, and 89 other), 276 had admixed American ancestry (self-reported race or ethnicity: 265 Hispanic, 3 White, and 8 other), 150 had East Asian ancestry (self-reported race or ethnicity: 67 Asian, 18 Hispanic, and 65 other), 5718 had European ancestry (self-reported race or ethnicity: 7 Asian, 39 Black, 1142 Hispanic, 3934 White, and 596 other), and 267 had other ancestries (self-reported race or ethnicity: 70 Asian, 13 Black, 126 Hispanic, 48 White, and 10 other). Three genome-wide polygenic scores were significantly associated (false discovery rate P < .05) with suicidal thoughts and behaviors among all participants: attention-deficit/hyperactivity disorder (odds ratio [OR], 1.12; 95% CI, 1.05-1.21; P = .001), schizophrenia (OR, 1.50; 95% CI, 1.17-1.93; P = .002), and general happiness (OR, 0.89; 95% CI, 0.83-0.96; P = .002). In the analysis including only children with European ancestry, 3 additional genome-wide polygenic scores with false discovery rate significance were associated with suicidal thoughts and behaviors: autism spectrum disorder (OR, 1.18; 95% CI, 1.06-1.31; P = .002), major depressive disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .003), and posttraumatic stress disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .004). A significant interaction between genome-wide polygenic scores and environment was found, with genetic risk factors for autism spectrum disorder and the level of early life stress associated with increases in the risk of overall suicidal ideation and overall suicidal thoughts and behaviors (OR, 1.20; 95% CI, 1.07-1.35; P = .002). A machine learning model using multitrait genome-wide polygenic scores and additional self-reported questionnaire data (Child Behavior Checklist and Family Environment Scale) produced a moderately accurate estimate of overall suicidal thoughts and behaviors (area under the receiver operating characteristic curve [AUROC], 0.77; 95% CI, 0.73-0.81; accuracy, 0.67) and suicidal ideation (AUROC, 0.76; 95% CI, 0.72-0.80; accuracy, 0.66) among children with European ancestry only. Among all children in the multiethnic cohort, the integrated model also outperformed the baseline model in estimating the risk of overall suicidal thoughts and behaviors (AUROC, 0.71; 95% CI, 0.67-0.75; accuracy, 0.68) and suicidal ideation (AUROC, 0.75; 95% CI, 0.71-0.78; accuracy, 0.67). Conclusions and Relevance: In this cohort study of preadolescent youths in the US, higher genome-wide polygenic scores for psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, and schizophrenia, were significantly associated with a greater risk of suicidal ideation and suicide attempt. The findings and quantitative models from this study may help to identify children with a high risk of suicide, potentially assisting with early screening, intervention, and prevention.

Prevalence of gaming disorder: A meta-analysis.

BACKGROUND: Gaming disorder (GD) has been listed in the International Classification of Diseases 11th Revision. Studies on GD prevalence have been highly heterogeneous, and there are significant gaps in prevalence estimates. Few studies have examined what methodological and demographic factors could explain this phenomenon. Therefore, this meta-analytic study quantifies globally reported GD prevalence rates and explores their various moderating variables. METHODS: Prevalence estimates were extracted from 61 studies conducted before December 3, 2020, which included 227,665 participants across 29 countries. Subgroup and moderator analyses were used to investigate the potential causes of heterogeneity, including region, sample size, year of data collection, age group, study design, sampling method, survey format, sample type, risk of bias, terminology, assessment tool, and male proportion. RESULTS: The overall pooled prevalence of GD was 3.3% (95% confidence interval: 2.6-4.0) (8.5% in males and 3.5% in females). By selecting only 28 representative sample studies, the prevalence estimate was reduced to 2.4% (95% CI 1.7-3.2), and the adjusted prevalence estimate using the trim-and-fill method was 1.4% (95% CI 0.9-1.9). High heterogeneity in GD prevalence rates was influenced by various moderators, such as participant variables (e.g., region, sample size, and age) and study methodology (e.g., study design, sampling method, sample type, terminology, and instrument). The moderator analyses revealed that the sample size, mean age, and study quality were negatively associated with GD prevalence. CONCLUSIONS: This study confirms that GD prevalence studies were highly heterogeneous based on participant demographics and research methodologies. Various confounding variables, such as sampling methods, sample types, assessment tools, age, region, and cultural factors have significantly influenced the GD prevalence rates. Prevalence estimates are likely to vary depending on study quality. Further epidemiological studies should be conducted using rigorous methodological standards to more accurately estimate GD prevalence.

Reliability, and Convergent and Discriminant Validity of Gaming Disorder Scales: A Meta-Analysis.

Background: An association between gaming disorder (GD) and the symptoms of common mental disorders is unraveled yet. In this preregistered study, we quantitatively synthesized reliability, convergent and discriminant validity of GD scales to examine association between GD and other constructs. Methods: Five representative GD instruments (GAS-7, AICA, IGDT-10, Lemmens IGD-9, and IGDS9-SF) were chosen based on recommendations by the previous systematic review study to conduct correlation meta-analyses and reliability generalization. A systematic literature search was conducted through Pubmed, Proquest, Embase, and Google Scholar to identify studies that reported information on either reliability or correlation with related variables. 2,124 studies were full-text assessed as of October 2020, and 184 were quantitatively synthesized. Conventional Hedges two-level meta-analytic method was utilized. Results: The result of reliability generalization reported a mean coefficient alpha of 0.86 (95% CI = 0.85-0.87) and a mean test-retest estimate of 0.86 (95% CI = 0.81-0.89). Estimated effect sizes of correlation between GD and the variables were as follows: 0.33 with depression (k = 45; number of effect sizes), 0.29 with anxiety (k = 37), 0.30 with aggression (k = 19), -0.22 with quality of life (k = 18), 0.29 with loneliness (k = 18), 0.56 with internet addiction (k = 20), and 0.40 with game playtime (k = 53), respectively. The result of moderator analyses, funnel and forest plots, and publication bias analyses were also presented. Discussion and Conclusion: All five GD instruments have good internal consistency and test-retest reliability. Relatively few studies reported the test-retest reliability. The result of correlation meta-analysis revealed that GD scores were only moderately associated with game playtime. Common psychological problems such as depression and anxiety were found to have a slightly smaller association with GD than the gaming behavior. GD scores were strongly correlated with internet addiction. Further studies should adopt a rigorous methodological procedure to unravel the bidirectional relationship between GD and other psychopathologies. Limitations: The current study did not include gray literature. The representativeness of the five tools included in the current study could be questioned. High heterogeneity is another limitation of the study. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42020219781].

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

BACKGROUND: The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE: This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. METHODS: We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. RESULTS: A higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011). CONCLUSIONS: Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.