A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD.

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.

Triangulating causality between childhood obesity and neurobehavior: Behavioral genetic and longitudinal evidence.

Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. We filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9-10. First, correlations between the age- and sex-specific 95th BMI percentile (%BMIp95) and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Behavioral genetic Direction of Causation modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. %BMIp95 correlated with impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Greater %BMIp95 was also associated with reduced cortical thickness in frontal and temporal brain areas but with increased thickness in parietal and occipital areas. Similar although weaker patterns emerged for cortical surface area and volume. Behavioral genetic modeling suggested causal effects of %BMIp95 on eating behavior (ß = 0.26), cognition (ß = 0.05), cortical thickness (ß = 0.15), and cortical surface area (ß = 0.07). Personality/psychopathology (ß = 0.09) and eating behavior (ß = 0.16) appeared to influence %BMIp95. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. Results supported causal effects of obesity on brain functioning and morphology. The present study highlights the importance of physical health for brain development and may inform interventions aimed at preventing or reducing pediatric obesity. RESEARCH HIGHLIGHTS: A continuous measure related to obesity, %BMIp95, has correlations with various measures of brain functioning and structure Behavioral genetic and longitudinal modeling suggest causal links from personality, psychopathology, and eating behavior to %BMIp95 Results also indicate directional links from %BMIp95 to eating behavior, cognition, cortical thickness, and cortical surface area Obesity may play a role for healthy brain development during childhood.

Body mass predicts personality development across 18 years in middle to older adulthood.

OBJECTIVE: Various personality traits have longitudinal relations with body mass index (BMI), a measure of body weight and a risk factor for numerous health concerns. We tested these associations' compatibility with causality in either direction. METHOD: Using three waves of the Wisconsin Longitudinal Study (N = 12,235, Mage  = 53.33 at baseline), we tested how accurately the Five-Factor Model personality domains and their items could collectively predict BMI and change in it with elastic net models. With multilevel models, we tested (a) bidirectional and (b) within-person associations between BMI and personality traits. RESULTS: The five domains were able to predict concurrent (r = 0.08), but not future BMI. Twenty-nine personality items predicted concurrent and future BMI at r = 0.21 and r = 0.16 to 0.25, respectively. Neither the domains nor items could collectively predict change in BMI. Similarly, no individual trait predicted change in BMI, but BMI predicted changes in Conscientiousness, Agreeableness, and several items (|b*| = 0.03 to 0.08). BMI had within-person correlations with these same traits; time-invariant third factors like genetics or childhood environments therefore could not (fully) account for their relations. CONCLUSIONS: Body weight may contribute to adults' personality development, but the reverse appears less likely.

Educational attainment polygenic scores, socioeconomic factors, and cortical structure in children and adolescents.

Genome-wide polygenic scores for educational attainment (PGS-EA) and socioeconomic factors, which are correlated with each other, have been consistently associated with academic achievement and general cognitive ability in children and adolescents. Yet, the independent associations of PGS-EA and socioeconomic factors with specific underlying factors at the neural and neurocognitive levels are not well understood. The main goals of this study were to examine the unique contributions of PGS-EA and parental education to cortical structure and neurocognitive skills in children and adolescents, and the associations among PGS-EA, cortical structure, and neurocognitive skills. Participants were typically developing 3- to 21-year-olds (53% male; N = 391). High-resolution, T1-weighted magnetic resonance imaging data were acquired, and cortical thickness (CT) and surface area (SA) were measured. PGS-EA were computed based on the EA3 genome-wide association study of educational attainment. Participants completed executive function, vocabulary, and episodic memory tasks. Higher PGS-EA and parental education were independently and significantly associated with greater total SA and vocabulary. Higher PGS-EA was significantly associated with greater SA in the left medial orbitofrontal gyrus and inferior frontal gyrus, which was associated with higher executive function. Higher parental education was significantly associated with greater SA in the left parahippocampal gyrus after accounting for PGS-EA and total brain volume. These findings suggest that education-linked genetics may influence SA in frontal regions, leading to variability in executive function. Associations of parental education with cortical structure in children and adolescents remained significant after controlling for PGS-EA, a source of genetic confounding.

Relationship between impulsivity, uncontrolled eating and body mass index: a hierarchical model.

BACKGROUND: Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, body mass index (BMI), and longitudinal weight changes. In addition, we analyzed the associations between general impulsivity domains and cortical thickness to elucidate brain vulnerability factors related to weight gain. METHODS: Students (N = 2318) in their first year of university-a risky period for weight gain-completed questionnaire measures of impulsivity and eating behavior at the beginning of the school year. We also collected their weight at the end of the term (N = 1177). Impulsivity was divided into three factors: stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. Seventy-one participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness. RESULTS: Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus. CONCLUSION: The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

Two genetic analyses to elucidate causality between body mass index and personality.

BACKGROUND/OBJECTIVES: Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality. SUBJECTS/METHODS: We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation. RESULTS: In Mendelian randomization, the allele score for BMI predicted domain-general (ß = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (ß = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (ß = -0.05; 95% CI: -0.08, -0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (ß = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (ß = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25). CONCLUSIONS: Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

Personality traits relate to chronotype at both the phenotypic and genetic level.

INTRODUCTION: Diurnal preferences have been linked to personality but often with mixed results. The present study examines the relationships between sleep timing (chronotype), diurnal preferences, and the Five-Factor Model of personality traits at the phenotypic and genetic level. METHODS: Self- and informant-reports of the NEO Personality Inventory-3, self-reports of the Munich Chronotype Questionnaire, and DNA samples were available for 2,515 Estonian adults (Mage  = 45.76 years; 59% females). Genetic correlations were obtained through summary statistics of genome-wide association studies. RESULTS: Results showed that higher Conscientiousness and lower Openness to Experience were significant predictors of earlier chronotype. At the level of facets, we found that more straightforward (A2) and excitement-seeking (E5), yet less self-disciplined (C5) people were more likely to have later chronotypes. The nuance-level Polypersonality score was correlated with chronotype at r = .28 (p < .001). Conscientiousness and Openness were genetically related with diurnal preferences. The polygenic score for morningness-eveningness significantly predicted the Polypersonality score. CONCLUSION: Phenotypic measures of chronotype and personality showed significant associations at all three of levels of the personality hierarchy. Our findings indicate that the relationship between personality and morningness-eveningness is partly due to genetic factors. Future studies are necessary to further refine the relationship.

Neurobehavioral correlates of obesity are largely heritable.

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the "right brain hypothesis" for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality-BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor's genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25-0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77-89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.

Distinct influence of parental occupation on cortical thickness and surface area in children and adolescents: Relation to self-esteem.

Studies of socioeconomic disparities have largely focused on correlating brain measures with either composite measure of socioeconomic status (SES), or its components-family income or parental education, giving little attention to the component of parental occupation. Emerging evidence suggests that parental occupation may be an important and neglected indicator of childhood and adolescent SES compared to absolute measures of material resources or academic attainment because, while related, it may more precisely capture position in social hierarchy and related health outcomes. On the other hand, although cortical thickness and surface area are brain measures with distinct genetic and developmental origins, large-scale neuroimaging studies investigating regional differences in interaction of the composite measure of SES or its components with cortical thickness and surface area are missing. We set out to fill this gap, focusing specifically on the role of parental occupation on cortical thickness and surface area by analyzing magnetic resonance imaging scans from 704 healthy individuals (age = 3-21 years). We observed spatially distributed patterns of (parental occupation × age2 ) interaction with cortical thickness (localized at the left caudal middle frontal, the left inferior parietal and the right superior parietal) and surface area (localized at the left orbitofrontal cortex), indicating independent sources of variability. Further, with decreased cortical thickness, children from families with lower parental occupation exhibited lower self-esteem. Our findings demonstrate distinct influence of parental occupation on cortical thickness and surface area in children and adolescents, potentially reflecting different neurobiological mechanisms by which parental occupation may impact brain development.

Uncontrolled eating: a unifying heritable trait linked with obesity, overeating, personality and the brain.

Many eating-related psychological constructs have been proposed to explain obesity and overeating. However, these constructs, including food addiction, disinhibition, hedonic hunger, emotional eating, binge eating and the like all have similar definitions, emphasizing loss of control over intake. As questionnaires measuring the constructs correlate strongly (r > 0.5) with each other, we propose that these constructs should be reconsidered to be part of a single broad phenotype: uncontrolled eating. Such an approach enables reviewing and meta-analysing evidence obtained with each individual questionnaire. Here, we describe robust associations between uncontrolled eating, body mass index (BMI), food intake, personality traits and brain systems. Reviewing cross-sectional and longitudinal data, we show that uncontrolled eating is phenotypically and genetically intertwined with BMI and food intake. We also review evidence on how three psychological constructs are linked with uncontrolled eating: lower cognitive control, higher negative affect and a curvilinear association with reward sensitivity. Uncontrolled eating mediates all three constructs' associations with BMI and food intake. Finally, we review and meta-analyse brain systems possibly subserving uncontrolled eating: namely, (i) the dopamine mesolimbic circuit associated with reward sensitivity, (ii) frontal cognitive networks sustaining dietary self-control and (iii) the hypothalamus-pituitary-adrenal axis, amygdala and hippocampus supporting stress reactivity. While there are limits to the explanatory and predictive power of the uncontrolled eating phenotype, we conclude that treating different eating-related constructs as a single concept, uncontrolled eating, enables drawing robust conclusions on the relationship between food intake and BMI, psychological variables and brain structure and function.

Hunger enhances automatic processing of food and non-food stimuli: A visual mismatch negativity study.

Automatic detection of important and unexpected stimuli in the visual environment is crucial for survival. We sought to explore whether visual food stimuli are detected already in the pre-attentive stimulus processing phase, and whether hunger enhances such automatic detection. To attain these goals, we adapted an electroencephalography paradigm - visual mismatch negativity (vMMN). vMMN is a useful paradigm to study the processing of salient emotional stimuli which has not yet been applied in the context of eating behaviours. In our study, 18 right-handed women (25.2 ±â€¯7.4 years) underwent two experimental sessions: hunger and fed conditions. Participants had to focus on a 2-back working memory task in the center of the computer screen, while stimuli depicting high fat savoury (HFSA) and high fat sweet (HFSW) foods were presented as deviants in a stream of neutral standard stimuli in the four corners of the screen. Automatic detection of foods was observed within 80-360 ms after stimulus onset, although some foods were better detected than the others. In HFSA, hunger enhanced the processing of all deviant stimuli in the early (100-160 ms) and mid-latency (160-220 ms) time windows. As the modulating effect of hunger was not food-specific, hunger may enhance automatic detection of changes in the visual environment, regardless of the type of input. Nevertheless, hamburger in HFSA was better detected than other stimuli, indicated by larger peak amplitudes, which supports the food-specificity of hunger modulation. In HFSW, the modulating effect of hunger was not observed, possibly due to suboptimally chosen stimuli within this block. In conclusion, we believe that after careful stimulus selection vMMN has a great potential to be a reliable measure of early food-cue processing.

Educational Attainment and Personality Are Genetically Intertwined.

Heritable variance in psychological traits may reflect genetic and biological processes that are not necessarily specific to these particular traits but pertain to a broader range of phenotypes. We tested the possibility that the personality domains of the five-factor model and their 30 facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of more than 3,000 adult Estonians, education polygenic scores (EPSs), which are interpretable as estimates of molecular-genetic propensity for education, were correlated with various personality traits, particularly from the neuroticism and openness domains. The correlations of personality traits with phenotypic educational attainment closely mirrored their correlations with EPS. Moreover, EPS predicted an aggregate personality trait tailored to capture the maximum amount of variance in educational attainment almost as strongly as it predicted the attainment itself. We discuss possible interpretations and implications of these findings.

Diet misreporting can be corrected: confirmation of the association between energy intake and fat-free mass in adolescents.

Subjective energy intake (sEI) is often misreported, providing unreliable estimates of energy consumed. Therefore, relating sEI data to health outcomes is difficult. Recently, Börnhorst et al. compared various methods to correct sEI-based energy intake estimates. They criticised approaches that categorise participants as under-reporters, plausible reporters and over-reporters based on the sEI:total energy expenditure (TEE) ratio, and thereafter use these categories as statistical covariates or exclusion criteria. Instead, they recommended using external predictors of sEI misreporting as statistical covariates. We sought to confirm and extend these findings. Using a sample of 190 adolescent boys (mean age=14), we demonstrated that dual-energy X-ray absorptiometry-measured fat-free mass is strongly associated with objective energy intake data (onsite weighted breakfast), but the association with sEI (previous 3-d dietary interview) is weak. Comparing sEI with TEE revealed that sEI was mostly under-reported (74 %). Interestingly, statistically controlling for dietary reporting groups or restricting samples to plausible reporters created a stronger-than-expected association between fat-free mass and sEI. However, the association was an artifact caused by selection bias - that is, data re-sampling and simulations showed that these methods overestimated the effect size because fat-free mass was related to sEI both directly and indirectly via TEE. A more realistic association between sEI and fat-free mass was obtained when the model included common predictors of misreporting (e.g. BMI, restraint). To conclude, restricting sEI data only to plausible reporters can cause selection bias and inflated associations in later analyses. Therefore, we further support statistically correcting sEI data in nutritional analyses. The script for running simulations is provided.

Eating traits questionnaires as a continuum of a single concept. Uncontrolled eating.

Research on eating behaviour has identified several potentially relevant eating-related traits captured by different questionnaires. Often, these questionnaires predict Body Mass Index (BMI), but the relationship between them has not been explicitly studied. We studied the unity and diversity of questionnaires capturing five common eating-related traits: Power of Food, Eating Impulsivity, emotional eating, Disinhibition, and binge eating in women from Estonia (n = 740) and Canada (n = 456). Using bifactor analysis, we showed that a) these questionnaires are largely explained by a single factor, and b) relative to this shared factor, only some questionnaires offered additional variance in predicting BMI. Hence, these questionnaires seemed to characterise a common factor, which we label Uncontrolled Eating. Item Response Theory techniques were then applied to demonstrate that c) within this common factor, the questionnaires could be placed on a continuum of Uncontrolled Eating. That is, Eating Impulsivity focused on the milder degree, Power of Food Scale, emotional eating scales, and Disinhibition on intermediate degrees, and the Binge Eating Scale on the most severe degrees of Uncontrolled Eating. In sum, evidence from two samples showed that questionnaires capturing five common BMI-related traits largely reflected the same underlying latent trait - Uncontrolled Eating. In Estonia, some questionnaires focused on different severities of this common construct, supporting a continuum model of Uncontrolled Eating. These findings provide a starting point for developing better questionnaires of the neurobehavioural correlates of obesity, and provide a unifying perspective from which to view the existing literature. R scripts and data used for the analysis are provided.

A bottom-up approach dramatically increases the predictability of body mass from personality traits.

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

Most people's life satisfaction matches their personality traits: True correlations in multitrait, multirater, multisample data.

Despite numerous meta-analyses, the true extent to which life satisfaction reflects personality traits has remained unclear due to overreliance on a single method to assess both and insufficient attention to construct overlaps. Using data from three samples tested in different languages (Estonian, N = 20,886; Russian, N = 768; English, N = 600), we combined self- and informant-reports to estimate personality domains' and nuances' true correlations (rtrue) with general life satisfaction (LS) and satisfactions with eight life domains (DSs), while controlling for single-method and occasion-specific biases and random error, and avoiding direct construct overlaps. The associations replicated well across samples. The Big Five domains and nuances allowed predicting LS with accuracies up to rtrue ≈ .80-.90 in independent (sub)samples. Emotional stability, extraversion, and conscientiousness correlated rtrue ≈ .30-.50 with LS, while its correlations with openness and agreeableness were small. At the nuances level, low LS was most strongly associated with feeling misunderstood, unexcited, indecisive, envious, bored, used, unable, and unrewarded (rtrue ≈ .40-.70). Supporting LS's construct validity, DSs had similar personality correlates among themselves and with LS, and an aggregated DS correlated rtrue ≈ .90 with LS. LS's approximately 10-year stability was rtrue = .70 and its longitudinal associations with personality traits mirrored cross-sectional ones. We conclude that without common measurement limitations, most people's life satisfaction is highly consistent with their personality traits, even across many years. So, satisfaction is usually shaped by these same relatively stable factors that shape personality traits more broadly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Self- and informant-reported personality traits and vaccination against COVID-19.

As COVID-19 vaccines' accessibility has grown, so has the role of personal choice in vaccination, and not everybody is willing to vaccinate. Exploring personality traits' associations with vaccination could highlight some person-level drivers of, and barriers to, vaccination. We used self- and informant-ratings of the Five-Factor Model domains and their subtraits (a) measured approximately at the time of vaccination with the 100 Nuances of Personality (100NP) item pool (N = 56,575) and (b) measured on average ten years before the pandemic with the NEO Personality Inventory-3 (NEO-PI-3; N = 3,168). We tested individual domains' and either items' (in the 100NP sample) or facets' (in the NEO-PI-3 sample) associations with vaccination, as well as their collective ability to predict vaccination using elastic net models trained and tested in independent sample partitions. Although the NEO-PI-3 domains and facets did not predict vaccination ten years later, the domains correlated with vaccination in the 100NP sample, with vaccinated people scoring slightly higher on neuroticism and agreeableness and lower on openness, controlling for age, sex, and education. Collectively, the five domains predicted vaccination with an accuracy of r = .08. Associations were stronger at the item level. Vaccinated people were, on average, more science-minded, politically liberal, respectful of rules and authority, and anxious but less spiritual, religious, and self-assured. The 100NP items collectively predicted vaccination with r = .31 accuracy. We conclude that unvaccinated people may be a psychologically heterogeneous group and highlight some potential areas for action in vaccination campaigns.

Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents.

BACKGROUND: Depression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume. METHODS: Participants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions. RESULTS: Children and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes. LIMITATIONS: Participants in these analyses were all of European ancestry. CONCLUSIONS: Polygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies. DECLARATIONS OF INTEREST: none. DECLARATIONS OF INTEREST: None.

Neuroanatomical correlates of genetic risk for obesity in children.

Obesity has a strong genetic component, with up to 20% of variance in body mass index (BMI) being accounted for by common polygenic variation. Most genetic polymorphisms associated with BMI are related to genes expressed in the central nervous system. At the same time, higher BMI is associated with neurocognitive changes. However, the direct link between genetics of obesity and neurobehavioral mechanisms related to weight gain is missing. Here, we use a large sample of participants (n > 4000) from the Adolescent Brain Cognitive Development cohort to investigate how genetic risk for obesity, expressed as polygenic risk score for BMI (BMI-PRS), is related to brain and behavioral measures in adolescents. In a series of analyses, we show that BMI-PRS is related to lower cortical volume and thickness in the frontal and temporal areas, relative to age-expected values. Relatedly, using structural equation modeling, we find that lower overall cortical volume is associated with higher impulsivity, which in turn is related to an increase in BMI 1 year later. In sum, our study shows that obesity might partially stem from genetic risk as expressed in brain changes in the frontal and temporal brain areas, and changes in impulsivity.