Measures of behavioral function predict duration of video game play: utilization of the Video Game Functional Assessment - Revised

Background: Internet gaming disorder (IGD) was introduced in the DSM-5 as a way of identifying and diagnosing problematic video game play. However, the use of the diagnosis is constrained, as it shares criteria with other addictive orders (e.g., pathological gambling). Aims: Further work is required to better understand IGD. One potential avenue of investigation is IGD’s relationship to the primary reinforcing behavioral functions. This study explores the relationship between duration of video game play and the reinforcing behavioral functions that may motivate or maintain video gaming. Methods: A total of 499 video game players began the online survey, with complete data from 453 participants (85% white and 28% female), were analyzed. Individuals were placed into five groups based on self-reported hours of video gaming per week, and completed the Video Game Functional Assessment – Revised (VGFA-R). Results: The results demonstrated the escape and social attention function were significant in predicting duration of video game play, whereas sensory and tangible were not significant. Conclusion: Future implications of the VGFA-R and behaviorally based research are discussed

Catastrophizing mediates the relationship between the personal belief in a just world and pain outcomes among chronic pain support group attendees

Health-related research suggests the belief in a just world can act as a personal resource that protects against the adverse effects of pain and illness. However, currently, little is known about how this belief, particularly in relation to one’s own life, might influence pain. Consistent with the suggestions of previous research, the present study undertook a secondary data analysis to investigate pain catastrophizing as a mediator of the relationship between the personal just world belief and chronic pain outcomes in a sample of chronic pain support group attendees. Partially supporting the hypotheses, catastrophizing was negatively correlated with the personal just world belief and mediated the relationship between this belief and pain and disability, but not distress. Suggestions for future research and intervention development are made

Runs of homozygosity implicate autozygosity as a schizophrenia risk factor

Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ~17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations

Timescales of Massive Human Entrainment

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates. By time locking these data sources, we quantify the impact of the unfolding debate on human attention. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version revised according to peer reviewers' comments: more detailed explanation of the methods, and grounding of the hypothese

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Major depressive disorder and current psychological distress moderate the effect of polygenic risk for obesity on body mass index

We are grateful to the families who took part in GS:SFHS, the GPs and Scottish School of Primary Care for their help in recruiting them, and the whole GS team, which includes academic researchers, clinic staff, laboratory technicians, clerical workers, IT staff, statisticians and research managers. This work is supported by the Wellcome Trust through a Strategic Award, reference 104036/Z/14/Z. The Chief Scientist Office of the Scottish Government and the Scottish Funding Council provided core support for Generation Scotland. GS:SFHS was funded by a grant from the Scottish Government Health Department, Chief Scientist Office, number CZD/16/6. We acknowledge with gratitude the financial support received for this work from the Dr Mortimer and Theresa Sackler Foundation. PT, DJP, IJD and AMM are members of The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Council and Medical Research Council is gratefully acknowledged. DJM is an NRS Career Fellow, funded by the CSO. Supplementary Information accompanies the paper on the Translational Psychiatry websitePeer reviewedPublisher PD

Do regional brain volumes and major depressive disorder share genetic architecture?:A study of Generation Scotland (<i>n</i>=19,762), UK Biobank (<i>n</i>=24,048) and the English Longitudinal Study of Ageing (<i>n</i>=5,766)

Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV