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Research questionAre unmet needs for psychosocial counselling, peer support and friends/family support in parents directly and/or indirectly related to the mental health of parents and their donor-children?DesignA cross-sectional sample of 214 parents participated in this quantitative study via an online questionnaire. The sample comprised mothers and fathers in a heterosexual relationship (n = 85), mothers in a lesbian relationship (n = 67) and single mothers (n = 62). Parents were recruited via three Dutch fertility clinics and four network organizations. Unmet support needs were measured with an adapted version of the Unmet Needs for Parenting Support questionnaire, changing the original items into items about donor conception. The items were derived from a qualitative study and checked by experts in donor conception. The parents' mental health was measured with the Adult Self Report and the donor-children's mental health with the Child Behaviour Checklist. A multigroup mediation analysis was conducted to explore relationships between parents’ unmet support needs and their child's mental health, with the parents’ mental health as a possible mediator.ResultsThere were no direct relations between parents’ unmet support needs and the mental health of donor-children. Unmet needs for psychosocial counselling, peer support and friends/family support for parents and children's mental health were indirectly related through the mental health of the parents: 0.074 (CI 95% = 0.013–0.136; P = 0.017), 0.085 (CI 95% = 0.018–0.151; P = 0.036) and 0.063 (CI 95% = 0.019–0.106; P = 0.013), respectively.ConclusionsWe recommend that fertility clinics, network organizations and authorities for infertility counsellors make their support available to parents for extended periods after their treatment. Further qualitative studies are necessary to assess how to relieve unmet support needs during donor sperm treatment.

Selective mutism (SM) is an anxiety disorder in children/adolescents, characterized by the absence of speaking in specific social situations, mostly at school. The selective mutism questionnaire (SMQ) is a parent report, internationally used to assess SM symptomatology and treatment outcomes. Since no assessment instrument for SM was available in the Netherlands, our aim was to investigate the psychometric properties of the Dutch translation of the SMQ, through reliability, confirmatory factor, and ROC analyses conducted on data obtained in 303 children (ages 3-17 years; clinical SM group n = 106, control group n = 197). The SMQ turned out to be highly reliable (α = 0.96 in the combined sample; 0.83 within the clinical group) and followed the expected factor structure. We conclude that the Dutch version of the SMQ is a reliable and valid tool both as a screening and clinical instrument to assess SM in Dutch speaking children.

Objective Irritable mood, a common and impairing symptom in psychopathology, has been proposed to underlie the developmental link between oppositional problems in youth and depression in adulthood. We examined the neural correlates of adolescent irritability in IMAGEN, a sample of 2,024 14-year-old adolescents from 5 European countries. Method The Development and Well-Being Assessment (DAWBA) was used to assess attention-deficit/hyperactivity disorder, major depressive disorder, oppositional defiant disorder, and generalized anxiety disorder. Three items from the DAWBA, selected as close matches to the Affective Reactivity Index, were used to assess irritability. Structural magnetic resonance imaging was examined using whole-brain voxel-based morphometry analysis, and functional magnetic resonance imaging was examined during a stop signal task of inhibitory control. Imaging data were included in structural equation models to examine the direct and indirect associations between irritable mood and comorbid DSM diagnoses. Results Whole-brain voxelwise analysis showed that adolescent irritable mood was associated with less gray matter volume and less neural activation underlying inhibitory control in frontal and temporal cortical areas (cluster-correction at p Conclusion This study identifies adolescent irritability as an independent construct and points to a neurobiological correlate to irritability that is an important contributing feature to many psychopathological disorders.

In a study of undergraduate L2 students participating in a Content and Language Integrated Learning historical reasoning course, we examined students’ changing performance on historical reasoning a...

IntroductionThis paper outlines the study protocol for the Dutch Tackle Your Tics study in youth with tic disorders. Tourette syndrome and chronic tic disorders are prevalent neurodevelopmental disorders, placing considerable burden on youth and their families. Behavioural treatment is the first-line, evidence-based intervention for tic disorders, but tic reduction and availability remain relatively low. Patient associations stress the need for more accessible high-quality treatments, also focusing on improving quality of life. Therefore, the brief, intensive group-based treatment Tackle Your Tics was developed.Methods and analysisTackle Your Tics is a 4-day intensive and comprehensive group-based intervention for children and adolescents (9–17 years) with Tourette syndrome or a chronic tic disorder. The programme encompasses exposure and response prevention treatment and additional supporting components (coping strategies, relaxation exercises and parent support). To study the effectiveness of Tackle Your Tics and identify predictors/moderators at baseline, a single-blinded randomised controlled trial (n=104) is conducted, comparing Tackle Your Tics (n=52) with a waiting list condition lasting 3 months (n=52). Assessments are performed at similar time points for both groups: at baseline, after 4 weeks, and at 3 and 6 months of follow-up, on tic severity, quality of life and other psychosocial variables.Ethics and disseminationEthics approval has been obtained from the medical ethical committee of the Amsterdam Medical Centre (METC nr NL66340.018.18, v3 June 2020). Findings will be presented on national and international conferences, peer-reviewed scientific journals, patient organisation meetings and public media. Patient representatives are fully integrated as part of the research team. If Tackle Your Tics proves to be effective, it can expand evidence-based treatment possibilities for children and adolescents with tic disorders. Identifying the psychosocial predictors/moderators for the effectiveness of this intervention can provide personalised treatment advice in the future.Trial registration numberNL8052.

Selective mutism (SM) is an anxiety disorder in children/adolescents, characterized by the absence of speaking in specific social situations, mostly at school. The selective mutism questionnaire (SMQ) is a parent report, internationally used to assess SM symptomatology and treatment outcomes. Since no assessment instrument for SM was available in the Netherlands, our aim was to investigate the psychometric properties of the Dutch translation of the SMQ, through reliability, confirmatory factor, and ROC analyses conducted on data obtained in 303 children (ages 3–17 years; clinical SM group n = 106, control group n = 197). The SMQ turned out to be highly reliable (α = 0.96 in the combined sample; 0.83 within the clinical group) and followed the expected factor structure. We conclude that the Dutch version of the SMQ is a reliable and valid tool both as a screening and clinical instrument to assess SM in Dutch speaking children.

Background: Meta-analysis (Glass, 1976) is a commonly used statistical technique to aggregate sample effect sizes of different independent primary studies in order to draw inferences concerning population effects. To extend the range of research questions that can be answered, new meta-analytic models have been developed, such as meta-analytic structural equation modeling (MASEM) (Becker, 1992, 1995; Cheung, 2014, 2015a; Cheung & Chang, 2005; Jak, 2015; Viswesvaran & Ones, 1995). In primary studies, an effect size may represent the strength and direction of the association between any two variables of interest. Such an effect size can be expressed in different ways, for example as Pearson product-moment correlation, Cohens’ d, biserial correlation, and point-biserial correlation. How an effect size is expressed depends on the nature of the variables (e.g., continuous or dichotomous), but also on the way the variables are measured or analyzed. If one of the two continuous variables is artificially dichotomized, one may express the effect size as a point-biserial correlation. However, this typically provides a negatively biased estimate of the true underlying Pearson product-moment correlation (e.g., Cohen, 1983; MacCallum, Zhang, Preacher, & Rucker, 2002). The biserial correlation on the other hand should generally provide an unbiased estimate (Soper, 1914; Tate, 1955). Bias in the effect size of any primary study may affect meta-analytic results in the same direction (Jacobs & Viechtbauer, 2017). Therefore, we may expect that the use of the point-biserial correlation for the relationship between an artificially dichotomized and continuous variable also biases MASEM-parameters. In the current study we will evaluate how using point-biserial correlations versus biserial correlations from primary studies may affects path coefficients, their standard errors, and model fit in MASEM. Based on the results, we expect to be able to inform researchers about which of the two investigated effect sizes is the most appropriate to use in MASEM-applications and under which conditions. Aim: Our aim is to investigate the effects of using (1) the point-biserial correlation and (2) the biserial correlation for the relationship between an artificially dichotomized variable and a continuous variable on MASEM-parameters and model fit. Specifically, our interest lies in path coefficients, standard errors of these coefficients, and model fit. Method: We simulated meta-analytic data according to a full mediation (hence overidentified) population model (see Figure 1), with a continuous predictor variable X, continuous mediator M, and a continuous variable Y as outcome. Depending on the condition, the predictor variable X is artificially dichotomized in all or a given percentage of the primary studies. We chose this population model because in educational research the median number of variables in a ‘typical’ meta-analysis is three (de Jonge & Jak, 2018) and because mediation is a popular research topic. Figure 1. Population model with fixed parameter values. Under this population model, random meta-analytic datasets were generated under different conditions. We systematically varied the following: (1) the size of the (standardized) path coefficient between X and M (.16, .23, .33), (2) the percentage of primary studies in which X was artificially dichotomized (25%, 75%, 100%), and (3) the cut-off point at which X was artificially dichotomized (at the median value, so a proportion of .05, or when groups become unbalance, at a proportion of .01). These choices were mainly based on typical situations in educational research. The size of the path coefficient, reflect the minimum, mean/median, and maximum pooled Pearson product-moment correlations in a ‘typical’ meta-analysis in educational research (de Jonge & Jak, 2018). The 75% primary studies that artificially dichotomize the variable X, is based on a comparable example of a meta-analysis in educational research (Jansen, Elffers, & Jak, 2019). We used between-study variances of .01. The number of primary studies in a meta-analysis was fixed at the median number of a ‘typical’ meta-analysis, which is 44 (de Jonge & Jak, 2018). Because we use a random-effects MASEM-method, the assumption is thus that the population comprises 44 subpopulations from which the 44 samples are drawn, and that the weighted mean of the subpopulation parameters equals the population parameter. Given a specific condition and the fixed number of 44 primary studies, we randomly sampled the within primary study sample sizes from a positively skewed distribution as used in Hafdahl (2007) with a mean of 421.75, yielding ‘typical’ sample sizes (de Jonge & Jak, 2018) for every iteration. We imposed 39% missing correlations (Sheng, Kong, Cortina, & Hou, 2016) by (pseudo) randomly deleting either variable M or Y from 26 of the 44 studies. In each condition, we generated 2000 meta-analytic datasets drawn from the 44 subpopulations, which we analyzed using (1) the point-biserial and (2) the biserial correlation as effect size between the artificially dichotomized predictor X and continuous mediator M. The full mediation model was fitted using random-effects two stage structural equation modeling (TSSEM) (Cheung, 2014) within the R-package ‘metaSEM’ (Cheung, 2015b). As recommended (Becker, 2009; Hafdahl, 2007), we used the weighted mean correlation across the included primary studies to estimate the sampling variances and covariances of the correlation coefficients in the primary studies. Next, over the converged simulated datasets, we (1) estimated the relative percentage bias in both path coefficients (less than 5% bias was considered negligible; Hoogland & Boomsma, 1998), (2) calculated the relative percentage bias of the standard errors of these path coefficients (less than 10% bias was considered acceptable; Hoogland & Boomsma, 1998), (3) calculated the rejection rates of the chi-square statistic of the model of Stage 2 (df = 1,  = .05) and tested whether the rejection rate significantly differed from the nominal -level with the proportion test, and (4) compared the theoretical chi-square distribution (df = 1) with the empirical chi-square distribution (by means of QQplots and the Kolmogorov-Smirnov test). Main Results: When the point-biserial correlation for the relation between an artificially dichotomized predictor and a continuous mediator was used, the path coefficient of this relationship in the population (βMX) seems systematically underestimated. When the biserial correlation was used instead of the point-biserial correlation, this path coefficient could be considered unbiased in each condition. The estimated path coefficient between the two continuous variables (βYM) could also be considered unbiased in all conditions, no matter if the biserial or point-biserial correlation was used. The relative percentage bias in the standard errors of all path coefficients could be considered as not substantial according to the criteria that were applied. However, we noticed that the relative percentage bias in the standard error of the path coefficient between the predictor and mediator (βMX) seems systematically negatively biased when the biserial correlation was used. We also found that the relative percentage bias in the standard error of the path coefficient between the continuous variables Y and M (βYM) seems systematically negative, regardless if the point-biserial or biserial correlation was used. In most conditions, the rejection rate of the chi-square test of model fit at Stage 2 of the random-effects TSSEM was slightly above the nominal -level, no matter if the point-biserial or biserial correlation was used. The results of the Kolmogorov-Smirnov test and QQplots show that when the biserial correlation was used, there was a statistically significant difference between the empirical chi-square distribution and the theoretical chi-square distribution in five of the 18 conditions. When the point-biserial correlation was used, there was a significant difference in the same five conditions plus in three other conditions. There seems to be no clear pattern in which conditions the distributions differed significantly or not. Expected Conclusions and Implications: We advise researchers who want to apply MASEM and want to investigate mediation to convert the effect size between any dichotomized predictor and continuous variable to a biserial correlation, not to a point-biserial correlation. References: Becker, B. J. (1995). Corrections to “using results from replicated studies to estimate linear models”. Journal of Educational and Behavioral Statistics, 20, 100–102. doi:10.2307/1165390 Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. V. Hedges, & J.C. Valentine (Eds.) The handbook of research synthesis and meta-analysis (2nd ed., pp. 377–395). New York: Russell Sage Foundation. Becker, B. J. (1992). Using results from replicated studies to estimate linear models. Journal of Educational Statistics, 17, 341–362. doi:10.2307/1165128 Cheung, M. W.-L. (2014). Fixed-and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46, 29–40. doi:10.3758/s13428-013-0361-y Cheung, M. W.-L. (2015a). Meta-analysis: A structural equation modeling approach. Chichester, United Kingdom: John Wiley & Sons. Cheung, M. W.-L. (2015b). metaSEM: An R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5, [1521]. https://doi.org/10.3389/fpsyg.2014.01521 Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: a two-stage approach. Psychological methods, 10, 40-64. doi:10.1037/1082-989X.10.1.40 Cohen, J. (1983). The cost of dichotomization. Applied psychological measurement, 7, 249-253. https://doi.org/10.1177/014662168300700301 de Jonge, H., & Jak, S. (2018, June). A Meta-Meta-Analysis: Identifying Typical Conditions of Meta-Analyses in Educational Research. Paper presented at the conference Research Synthesis 2018 of Leibniz Institute for Psychology Information, Trier, Germany. http://dx.doi.org/10.23668/psycharchives.853 Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. The Educational Researcher, 10, 3–8. doi:10.3102/0013189X005010003 Hafdahl, A. R. (2007). Combining correlation matrices: Simulation analysis of improved fixed-effects methods. Journal of Educational and Behavioral Statistics, 32, 180–205. doi:10.3102/1076998606298041 Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26, 329–367. doi:10.1177/0049124198026003003 Jacobs, P., & Viechtbauer, W. (2017). Estimation of the biserial correlation and its sampling variance for use in meta‐analysis. Research synthesis methods, 8, 161-180. doi:10.1002/jrsm.1218 Jak, S. (2015). Meta-analytic structural equation modelling. Springer International Publishing. Jansen, D., Elffers, L., & Jak, S. (2019). The functions of shadow education in school careers: a systematic review. Manuscript submitted for publication. MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological methods, 7, 19–40. doi:10.1037//1082-989X.7.1.19 Tate, R. F. (1955). The theory of correlation between two continuous variables when one is dichotomized. Biometrika, 42, 205–216. doi:10.2307/2333437 Sheng, Z., Kong, W., Cortina, J. M., & Hou, S. (2016). Analyzing matrices of meta‐analytic correlations: current practices and recommendations. Research synthesis methods, 7, 187-208. doi:10.1002/jrsm.1206 Soper, H. E. (1914). On the probable error of the bi-serial expression for the correlation coefficient. Biometrika, 10, 384–390. doi:10.2307/2331789 Viswesvaran, C., & Ones, D. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865–885. doi:10.1111/j.1744-6570.1995.tb01784.x

Parental socioeconomic status (SES) is a strong predictor of children’s educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child’s PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children’s EA.

Background Studying the neural consequences of tobacco smoking during adolescence, including those associated with early light use, may help expose the mechanisms that underlie the transition from initial use to nicotine dependence in adulthood. However, only a few studies in adolescents exist, and they include small samples. In addition, the neural mechanism, if one exists, that links nicotinic receptor genes to smoking behavior in adolescents is still unknown. Methods Structural and diffusion tensor magnetic resonance imaging data were acquired from a large sample of 14-year-old adolescents who completed an extensive battery of neuropsychological, clinical, personality, and drug-use assessments. Additional assessments were conducted at 16 years of age. Results Exposure to smoking in adolescents, even at low doses, is linked to volume changes in the ventromedial prefrontal cortex and to altered neuronal connectivity in the corpus callosum. The longitudinal analyses strongly suggest that these effects are not preexisting conditions in those who progress to smoking. There was a genetic contribution wherein the volume reduction effects were magnified in smokers who were carriers of the high-risk genotype of the alpha 5 nicotinic receptor subunit gene, rs16969968. Conclusions These findings give insight into a mechanism involving genes, brain structure, and connectivity underlying why some adolescents find nicotine especially addictive.

As societies become increasingly diverse, mental health professionals need instruments for assessing emotional, behavioral, and social problems in terms of constructs that are supported within and across societies. Building on decades of research findings, multisample alignment confirmatory factor analyses tested an empirically based 8-syndrome model on parent ratings across 30 societies and youth self-ratings across 19 societies. The Child Behavior Checklist for Ages 6–18 and Youth Self-Report for Ages 11–18 were used to measure syndromes descriptively designated as Anxious/Depressed, Withdrawn/Depressed, Somatic Complaints, Social Problems, Thought Problems, Attention Problems, Rule-Breaking Behavior, and Aggressive Behavior. For both parent ratings (N = 61,703) and self-ratings (N = 29,486), results supported aggregation of problem items into 8 first-order syndromes for all societies (configural invariance), plus the invariance of item loadings (metric invariance) across the majority of societies. Supported across many societies in both parent and self-ratings, the 8 syndromes offer a parsimonious phenotypic taxonomy with clearly operationalized assessment criteria. Mental health professionals in many societies can use the 8 syndromes to assess children and youths for clinical, training, and scientific purposes. © 2018, Copyright © Society of Clinical Child and Adolescent Psychology.

Tourette syndrome (TS) and other chronic tic disorders (CTD) are prevalent neurodevelopmental disorders, which can have a huge burden on families and society. Behavioral treatment is a first-line intervention for tic disorders. Despite demonstrated efficacy, tic reduction and utilization rates of behavioral treatment remain relatively low. Patient associations point to an urgent need for easy-to-undergo treatments that focus both on tic reduction and improvement of quality of life. To enhance treatment outcome and overcome treatment barriers, this pilot study’s aim was to investigate the feasibility and preliminary results of a brief, intensive group-based treatment. Tackle your Tics is a 4-day intensive and comprehensive group-based program for children and adolescents (9–17 years) with a tic disorder, consisting of exposure and response prevention (ERP) treatment and additional supporting components, such as coping strategies, relaxing activities and parent support. Assessments were performed pre- and post-treatment and at 2 months follow-up, to test outcomes on tic severity and quality of life, and explore premonitory urges, emotional and behavioral functioning and treatment satisfaction (N = 14, of whom 13 completed the treatment). Parents and children rated this treatment positive on a treatment satisfaction questionnaire. On tic severity (Yale Global Tic Severity Scale) and quality of life (Gilles de la Tourette Syndrome Quality of Life Scale for children and adolescents), improvements between pre-treatment and follow-up were found. Intensive ERP in group format is promising as a feasible treatment to improve both tic severity as well as quality of life. Larger controlled trials are needed to establish its effectiveness.

Human intelligence is a puzzling concept. There is no consensus on the definition of intelligence, not even within specific fields that study intelligence, such as psychometrics. Within the latter, there are, for instance, major disputes over whether intelligence is one thing (a general ability) or multiple things. In our view, it is important to have a clear conception of what intelligence is. In this chapter we adhere to such a conception and present a formal model of psychometric intelligence. This model illuminates mechanisms that can explain some important and well replicated phenomena in the study of intelligence.

Aims To investigate how use of alcohol, illicit drugs and tobacco come from substance specific pathways and from pathways general to all three substances through adolescents’ development Design Analysis of population-based survey. Adolescent twins reported alcohol use (AU), tobacco use (TU), and illicit drug use (IDU) at 3 waves (2006, 2008, 2010). Restructuring data by age allowed for variance decomposition into age and substance specific and common genetic and environmental variance components. Setting Norway. Participants Seven national twin birth cohorts 1988 to 1994, totaling 2340 individuals, 1483 pairs (558 monozygotic; 925 dizygotic, same and opposite sex). Measurements 6-point Likert scores of AU, TU and IDU on items from the Monitoring the Future Study. Findings Substance use was found to be highly heritable; a2=.73 (CI .61-.94) for AU, a2=.36 (CI .18-.52); d2=.49 (CI95 = .29-.62) for IDU and a2=.46 (CI .23-.54); d2=.05 (CI95 = .00-.07) for TU over the whole adolescence period. General substance use (GSU) was also highly heritable at each age and averaged a2=.57 (CI95= 48-.66). There was a high genetic carry-over from earlier age to later age. Genetic effects on GSU at age 12-14 were still detectable 4 years later. New substance (general and specific) genetic effects also appeared. IDU demonstrated significant nonadditive genetic effects (age 12-14). Shared environment had a small impact on AU only. There was almost no nonshared environmental carry-over from age to age, the effect probably partly due to reliability deficiency. Common genetic effects across substance and substance-specific genetic effects were observed at each age period. Conclusions Among Norwegian adolescents, there appear to be strong genetic effects on both substance specific and comorbid use of alcohol, illicit drugs and tobacco. Among Norwegian adolescents, individual differences in alcohol use can be partially explained by family background.

Why are some children more socially anxious than others? One theory holds that socially anxious children are poor mindreaders, which hampers their social interactions; another that socially anxious children are advanced mindreaders leading to heightened self-consciousness in social situations. To test these theories simultaneously, this study (N = 105, ages 8–12) assessed children’s mindreading (accuracy in detecting mental states from the eye region), self-consciousness (indexed as physiological blushing during public performance), and social anxiety levels. Results support both theories, showing a quadratic relation between mindreading and social anxiety. Low mindreading was related to clinical levels of social anxiety. High mindreading was related to subclinical levels of social anxiety through blushing. Our findings suggest two social-cognitive pathways to heightened social anxiety.

Studies of adults provide evidence that spatial reasoning is non-unitary in nature, consisting of separate object transformation and viewer transformation abilities. This research examined the presence of this dissociation in children. Participants between 8 and 12 years of age, divided over three age groups (i.e., 65 children from 7.5 to 9 years old, 75 children from 9 to 10.5 years old, and 77 children from 10.5 to 12 years old) performed a battery of object and viewer transformation tasks. Analysis of variance showed that performance improved with age on the individual object and viewer transformation tasks, with the largest effects between 10.5 and 12 years of age. Multi-group confirmatory factor analyses to test the dissociation of object and viewer transformation ability over the different age groups revealed that in children under 10.5 years of age object and viewer transformation ability could not be differentiated. A dissociation between object and viewer transformation ability was shown between 10.5 and 12 years of age. This period of specialization of spatial abilities may be a particularly interesting time window for identifying spatial talents and providing spatial training and intervention.

Background.The influence of genetic factors on major depressive disorder is lower than on other psychiatric disorders. Heritability estimates mainly derive from cross-sectional studies, and knowledge on the longitudinal aetiology of symptoms of anxiety and depression (SxAnxDep) across the lifespan is limited. We aimed to assess phenotypic, genetic and environmental stability in SxAnxDep between ages 3 and 63 years.Method.We used a cohort-sequential design combining data from 49 524 twins followed from birth to age ⩾20 years, and from adolescence into adulthood. SxAnxDep were assessed repeatedly with a maximum of eight assessments over a 25-year period. Data were ordered in 30 age groups and analysed with longitudinal genetic models.Results.Over age, there was a significant increase during adolescence in mean scores with sex differences (women>men) emerging. Heritability was high in childhood and decreased to 30–40% during adulthood. This decrease in heritability was due to an increase in environmental variance. Phenotypic stability was moderate in children (correlations across ages ~0.5) and high in adolescents (r = 0.6), young adults (r = 0.7), and adults (r = 0.8). Longitudinal stability was mostly attributable to genetic factors. During childhood and adolescence there was also significant genetic innovation, which was absent in adults. Environmental effects contributed to short-term stability.Conclusions.The substantial stability in SxAnxDep is mainly due to genetic effects. The importance of environmental effects increases with age and explains the relatively low heritability of depression in adults. The environmental effects are transient, but the contribution to stability increases with age.

We show by simulations that the main premise underlying Gignac's (2016) rejection of the mutualism model of general intelligence is incorrect. We discuss our results in the context of the debate on g-factor theory and its alternatives, especially with regard to the claim that g-factor theory is much simpler than mutualism theory.

Cronbach’s (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores - the empirical basis for the well-known g-factor - as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

The Dickens and Flynn model of the Flynn effect (generational increases in mean IQ) assigns an important role to genotype-environment covariance (GE-cov). We quantify GE-cov in a longitudinal simplex model by modeling it as phenotype to environment (Ph->E) transmission in twin data. The model fits as well as the standard genetic simplex model, which assumes uncorrelated genetic and environmental influences. We use the results to explore numerically the possible role of GE-cov in amplifying increases in environmental means. Given the estimated Ph->E transmission parameters, GE-cov resulted in an amplification (in std units) of a factor 1.57 (full scale IQ) to 1.7 (performance IQ). The results lend credence to the role of GE-cov in the Flynn effect.

To investigate how use of alcohol, illicit drugs and tobacco come from substance-specific pathways and from pathways general to all three substances through adolescent development.Analysis of population-based survey. Adolescent twins reported alcohol use (AU), tobacco use (TU) and illicit drug use (IDU) in three waves (2006, 2008, 2010). Restructuring data by age allowed for variance decomposition into age- and substance-specific and common genetic and environmental variance components.Norway.Seven national twin birth cohorts from 1988 to 1994, totalling 1483 pairs (558 monozygotic; 925 dizygotic, same and opposite sex).Six-point Likert scores of AU, TU and IDU on items from the Monitoring the Future Study.Substance use was found to be highly heritable; aAmong Norwegian adolescents, there appear to be strong genetic effects on both substance-specific and comorbid use of alcohol, illicit drugs and tobacco; individual differences in alcohol use can be explained partially by family background.

The Netherlands Twin Register (NTR) began in 1987 with data collection in twins and their families, including families with newborn twins and triplets. Twenty-five years later, the NTR has collected at least one survey for 70,784 children, born after 1985. For the majority of twins, longitudinal data collection has been done by age-specific surveys. Shortly after giving birth, mothers receive a first survey with items on pregnancy and birth. At age 2, a survey on growth and achievement of milestones is sent. At ages 3, 7, 9/10, and 12 parents and teachers receive a series of surveys that are targeted at the development of emotional and behavior problems. From age 14 years onward, adolescent twins and their siblings report on their behavior problems, health, and lifestyle. When the twins are 18 years and older, parents are also invited to take part in survey studies. In sub-groups of different ages, in-depth phenotyping was done for IQ, electroencephalography , MRI, growth, hormones, neuropsychological assessments, and cardiovascular measures. DNA and biological samples have also been collected and large numbers of twin pairs and parents have been genotyped for zygosity by either micro-satellites or sets of short nucleotide polymorphisms and repeat polymorphisms in candidate genes. Subject recruitment and data collection is still ongoing and the longitudinal database is growing. Data collection by record linkage in the Netherlands is beginning and we expect these combined longitudinal data to provide increased insights into the genetic etiology of development of mental and physical health in children and adolescents. Copyright © The Authors 2012.

The large body of literature on the association between blood pressure (BP) and cognitive functioning has yielded mixed results, possibly due to the presence of non-linear effects across age, or because BP affects specific brain areas differently, impacting more on some cognitive skills than on others. If a robust association was detected among BP and specific cognitive tasks, the causal nature of reported associations between BP and cognition could be investigated in twin data, which allow a test of alternative explanations, including genetic pleiotropy. The present study first examines the association between BP and cognition in a sample of 1,140 participants with an age range between 10 and 86 years. Linear and quadratic effects of systolic BP (SBP) and diastolic BP (DBP) on cognitive functioning were examined for 17 tests across five functions. Associations were corrected for effects of sex and linear and quadratic effects of age. Second, to test a causal model, data from 123 monozygotic (MZ) twin pairs were analyzed to test whether cognitive functioning of the twins with the higher BP was different from that of the co-twins with lower BP. Associations between BP and cognitive functioning were absent for the majority of the cognitive tests, with the exception of a lower speed of emotion identification and verbal reasoning in subjects with high diastolic BP. In the MZ twin pair analyses, no effects of BP on cognition were found. We conclude that in the population at large, BP level is not associated with cognitive functioning in a clinically meaningful way.

BACKGROUND: Individual differences in impulsivity and early adversity are known to be strong predictors of adolescent antisocial behavior. However, the neurobiological bases of impulsivity and their relation to antisocial behavior and adversity are poorly understood.METHODS: Impulsivity was estimated with a temporal discounting task. Voxel-based morphometry was used to determine the brain structural correlates of temporal discounting in a large cohort (n = 1830) of 14- to 15-year-old children. Mediation analysis was then used to determine whether the volumes of brain regions associated with temporal discounting mediate the relation between adverse life events (e.g., family conflict, serious accidents) and antisocial behaviors (e.g., precocious sexual activity, bullying, illicit substance use).RESULTS: Greater temporal discounting (more impulsivity) was associated with 1) lower volume in frontomedial cortex and bilateral insula and 2) greater volume in a subcortical region encompassing the ventral striatum, hypothalamus and anterior thalamus. The volume ratio between these cortical and subcortical regions was found to partially mediate the relation between adverse life events and antisocial behavior.CONCLUSIONS: Temporal discounting is related to regions of the brain involved in reward processing and interoception. The results support a developmental imbalance model of impulsivity and are consistent with the idea that negative environmental factors can alter the developing brain in ways that promote antisocial behavior.

Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction.

Kristof Kovacs and Andrew Conway (this issue) offer a new theory for the positive manifold of intelligence (PM) and thus for the presence of a statistical general factor of intelligence. This aim i...

We side with Lee on the importance and potential use of graphical causal modelling in personality research but raise three issues crucial to its validity. First, causal relations obtained at the inter-individual level should not be confused with intra-individual causal relations. Second, it is difficult to explicate all assumptions about which variables are measured, their causal relations and the possibility of co-occurring events when applying graphical modelling to personality data. Third, multiple testing complicates assessing (in)dependencies. Conclusions and inferences should always be drawn with appropriate attention to the underlying assumptions.

Why is Gärtner's paper so interesting? It is for a number of reasons, but the most interesting is not the finding that phenotypic differences among animals still exist after standardization of genotype and environment (described in the first part of Gärtner's paper). These differences are expected to remain to a certain extent, because complete experimental control is impossible. More interesting is how large these differences are. Particularly interesting are the results of Gärtner's experiments in which he attempts to alter the amount of phenotypic variance by varying the amount of variance in environmental conditions and genetic influences (described in the second part). These results suggested the presence of an additional source of phenotypic variance besides the genotype and environment. Long before Gärtner's paper had been published, various researchers had speculated about the existence of such non-genetic, non-environmental (‘third’) source of variance. These included pioneering geneticists such as Sewall Wright (see the first path diagram ever), Sir Kenneth Mather and Jinks (p. 6) and Douglas Falconer and Mackay (p. 135). As we argue below, whereas this third source of variance was demonstrated in experimental organisms, it is also relevant to the interpretation of human quantitative (behaviour) genetic results (see also previous research).

Objective The Computerized Neurocognitive Battery (CNB) enables efficient neurocognitive assessment. The authors aimed to (a) estimate validity and reliability of the battery's Dutch translation, (b) investigate effects of age across cognitive domains, and (c) estimate heritability of the CNB tests. Method A population-representative sample of 1,140 participants (aged 10-86), mainly twin-families, was tested on the CNB, providing measures of speed and accuracy in 14 cognitive domains. In a subsample (246 subjects aged 14-22), IQ data (Wechsler Intelligence Scale for Adults; WAIS) were available. Validity and reliability were assessed by Cronbach's alpha, comparisons of scores between Dutch and U.S. samples, and investigation of how a CNB-based common factor compared to a WAIS-based general factor of intelligence (g). Linear and nonlinear age dependencies covering the life span were modeled through regression. Heritability was estimated from twin data and from entire pedigree data. Results Internal consistency of all tests was moderate to high (median = 0.86). Effects of gender, age, and education on cognitive performance closely resembled U.S. Samples The CNB-based common factor was completely captured by the WAIS-based g. Some domains, like nonverbal reasoning accuracy, peaked in young adulthood and showed steady decline. Other domains, like language reasoning accuracy, peaked in middle adulthood and were spared decline. CNB-test heritabilities were moderate (median h2 = 31%). Heritability of the CNB common factor was 70%, similar to the WAIS-based g-factor. Conclusion The CNB can be used to assess specific neurocognitive performance, as well as to obtain a reliable proxy of general intelligence. Effects of aging and heritability differed across cognitive domains.

We present a review of empirical evidence that suggests that a substantial portion of phenotypic variance is due to nonlinear (epigenetic) processes during ontogenesis. The role of such processes as a source of phenotypic variance in human behaviour genetic studies is not fully appreciated. In addition to our review, we present simulation studies of nonlinear epigenetic variance using a computational model of neuronal network development. In each simulation study, time series for monozygotic and dizygotic twins were generated and analysed using conventional behaviour genetic modelling. In the results of these analyses, the nonlinear epigenetic variance was subsumed under the non-shared environmental component. As is commonly found in behaviour genetic studies, observed heritabilities and unique environmentabilities increased with time, whereas common environmentabilities decreased. The fact that the phenotypic effects of nonlinear epigenetic processes appear as unsystematic variance in conventional twin analyses complicates the identification and quantification of the ultimate genetic and environmental causes of individual differences. We believe that nonlinear dynamical system theories provide a challenging perspective on the development of individual differences, which may enrich behaviour genetic studies.

An automatic bias to threat is often invoked to account for colour-naming interference in emotional Stroop. Recent findings by McKenna and Sharma [(2004). Reversing the emotional Stroop effect reveals that it is not what it seems: The role of fast and slow components. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 382-392], however, cast doubt on the fast and non-conscious nature of emotional Stroop. Interference by threat words only occurred with colour naming in the trial subsequent to the threat trial (i.e., a "slow" effect), but not immediately (i.e., a "fast" effect, as would be predicted by the bias hypothesis). In a meta-analysis of 70 published emotional Stroop studies the largest effects occurred when presentation of threat words was blocked, suggesting a strong contribution by slow interference. We did not find evidence; moreover, for interference in suboptimal (less conscious) presentation conditions and the only significant effects were observed in optimal (fully conscious) conditions with high-anxious non-clinical participants and patients. The emotional Stroop effect seems to rely more on a slow disengagement process than on a fast, automatic, bias.

Cannabis use in adolescence may be characterized by differences in the neural basis of affective processing. In this study, we used an fMRI affective face processing task to compare a large group (n = 70) of 14-year olds with a history of cannabis use to a group (n = 70) of never-using controls matched on numerous characteristics including IQ, SES, alcohol and cigarette use. The task contained short movies displaying angry and neutral faces. Results indicated that cannabis users had greater reactivity in the bilateral amygdalae to angry faces than neutral faces, an effect that was not observed in their abstinent peers. In contrast, activity levels in the cannabis users in cortical areas including the right temporal-parietal junction and bilateral dorsolateral prefrontal cortex did not discriminate between the two face conditions, but did differ in controls. Results did not change after excluding subjects with any psychiatric symptomology. Given the high density of cannabinoid receptors in the amygdala, our findings suggest cannabis use in early adolescence is associated with hypersensitivity to signals of threat. Hypersensitivity to negative affect in adolescence may place the subject at-risk for mood disorders in adulthood.

Objective: Clinically, attention-deficit/hyperactivity disorder (ADHD) is characterized by hyperactivity, impulsivity, and inattention and is among the most common childhood disorders. These same traits that define ADHD are variable in the general population, and the clinical diagnosis may represent the extreme end of a continuous distribution of inattentive and hyperactive behaviors. This hypothesis can be tested by assessing the predictive value of polygenic risk scores derived from a discovery sample of ADHD patients in a target sample from the general population with continuous scores of inattention and hyperactivity. In addition, the genetic overlap between ADHD and continuous ADHD scores can be tested across rater and age. Method: The Psychiatric Genomics Consortium has performed the largest genome-wide analysis (GWA) study of ADHD so far, including 5,621 clinical patients and 13,589 controls. The effects sizes of single nucleotide polymorphisms (SNPs) estimated in this meta-analysis were used to obtain individual polygenic risk scores in an independent population-based cohort of 2,437 children from the Netherlands Twin Register. The variance explained in Attention Problems (AP) scale scores by the polygenic risk scores was estimated by linear mixed modeling. Results: The ADHD polygenic risk scores significantly predicted both parent and teacher ratings of AP in preschool- and school-aged children. Conclusion: These results indicate genetic overlap between a diagnosis of ADHD and AP scale scores across raters and age groups and provides evidence for a dimensional model of ADHD. Future GWA studies on ADHD can likely benefit from the inclusion of population-based cohorts and the analysis of continuous scores. J. Am. Acad. Child Adolesc. Psychiatry, 2014;53(10):1123–1129. Key Words: ADHD, attention problems, polygenic scores, genetics, dimensional models

Assessment of genetic influences on behavior depends on context, informants, and study design: We show (analytically) that, conditional on study design, informant specific genetic variance is included in the genetic variance component or in the environmental variance component. To aid the explanation, we present an illustrative empirical analysis of data from the Netherlands Twin Register. Subjects included 1,571 monozygotic and 2,672 dizygotic 12-year-old twin pairs whose attention problems (AP) were rated by their parents, teachers, and themselves. Heritability estimates (h 2) of AP were about ~0.75 for same informant ratings (mother, father, and same teacher ratings) and ~0.54 for different informants’ ratings (different parents’, different teachers’, and two twins’ self-ratings). Awareness of assessment effects is relevant to research into psychiatric disorders. Differences in assessment can account for age effects, such as a drop in heritability of ADHD symptoms. In genome-wide association studies, effects of rating specific genetic influences will be undetectable.

To further knowledge concerning the nature and nurture of intelligence, we scrutinized how heritability coefficients vary across specific cognitive abilities both theoretically and empirically. Data from 23 twin studies (combined N = 7,852) showed that (a) in adult samples, culture-loaded subtests tend to demonstrate greater heritability coefficients than do culture-reduced subtests; and (b) in samples of both adults and children, a subtest’s proportion of variance shared with general intelligence is a function of its cultural load. These findings require an explanation because they do not follow from mainstream theories of intelligence. The findings are consistent with our hypothesis that heritability coefficients differ across cognitive abilities as a result of differences in the contribution of genotype-environment covariance. The counterintuitive finding that the most heritable abilities are the most culture-dependent abilities sheds a new light on the long-standing nature-nurture debate of intelligence.Keywords: intelligence, behavior genetics, cognitive ability, environmental effects, individual differences

Objective: To review findings on attention-deficit/hyperactivity disorder and attention problems (AP) in children, adolescents, and adults, as established in the database of the Netherlands Twin Register and increase the understanding of stability in AP across the lifespan as a function of genetic and environmental influences. Method: A longitudinal model was fitted on Netherlands Twin Register AP scores from 44,607 child (18-year-old) twins. Results: Mean AP showed a downward trend with age. Age-to-age correlations ranged from 0.33 (50-≥60 years old) to 0.73 (10-12 years old). Stability in individual differences in AP was due to genetic and environmental factors, and change was due primarily to environmental factors. Nonadditive genetic influences were present from childhood to adulthood. Total genetic variance decreased slightly throughout aging, whereas environmental variance increased substantially with the switch from maternal to self-ratings at 12 years of age. As a result, heritability coefficients decreased from 0.70 to 0.74 in childhood (maternal ratings) to 0.51 to 0.56 in adolescence (self-ratings), and 0.40 to 0.54 in adulthood (self-ratings). In childhood, male subjects scored higher than female subjects. After the rater switch at 12 years of age, female subjects tended to score higher than male subjects. Conclusions: Stability of AP is the result of genetic and environmental stability. The decrease in estimated heritability at 12 years of age is due to an increase in occasion-specific environmental variance and likely reflects a methodologic effect. Because environmental influences have lasting effects on AP, their early detection is crucial. © 2013 American Academy of Child and Adolescent Psychiatry.

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

On the one hand, the factors Gf and Gc in the Cattell–Horn–Carroll (CHC) model of intelligence are hypothesized to represent individual differences in unique psychological or biological capacities. On the other hand, they are interpreted as representing the theoretical variables fluid and crystallized intelligence in investment theory. With respect to Gc, this leads to a theoretical conflict because in investment theory crystallized intelligence is not a capacity but purely a statistical entity. We contend that if CHC factor Gc represents a capacity, it cannot represent crystallized intelligence, and if Gc represents crystallized intelligence, it does not represent a capacity. In addition, from our discussion of Gc, we conclude that in investment theory the factors Gf and g represent one and the same capacity. In support of our theoretical conclusions, we implemented the CHC model in a confirmatory factor analysis of a HCA (Human Cognitive Abilities project) data set. As expected, Gf and g were statistically indistinguishable. Gc was effectively absent, because it was statistically equivalent to verbal comprehension. Factors Gc and g could be removed from the model without any reduction in model fit. We argue that in the CHC taxonomy the factors Gc and g are redundant as explanatory variables.