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Dietary patterns in childhood have been associated with child neurodevelopment and cognitive performance, while the underlying neurobiological pathway is unclear. We aimed to examine associations of dietary patterns in infancy and mid-childhood with pre-adolescent brain morphology, and whether diet-related differences in brain morphology mediate the relation with cognition. We included 1888 and 2326 children with dietary data at age one or eight years, respectively, and structural neuroimaging at age 10 years in the Generation R Study. Measures of brain morphology were obtained using magnetic resonance imaging. Dietary intake was assessed using food-frequency questionnaires, from which we derived diet quality scores based on dietary guidelines and dietary patterns using principal component analyses. Full scale IQ was estimated using the Wechsler Intelligence Scale for Children-Fifth Edition at age 13 years. Children with higher adherence to a dietary pattern labeled as ‘Snack, processed foods and sugar’ at age one year had smaller cerebral white matter volume at age 10 (B = -4.3, 95%CI -6.9, -1.7). At age eight years, higher adherence to a ‘Whole grains, soft fats and dairy’ pattern was associated with a larger total brain (B = 8.9, 95%CI 4.5, 13.3), and larger cerebral gray matter volumes at age 10 (B = 5.2, 95%CI 2.9, 7.5). Children with higher diet quality and better adherence to a ‘Whole grains, soft fats and dairy’ dietary pattern at age eight showed greater brain gyrification and larger surface area, clustered primarily in the dorsolateral prefrontal cortex. These observed differences in brain morphology mediated associations between dietary patterns and IQ. In conclusion, dietary patterns in early- and mid-childhood are associated with differences in brain morphology which may explain the relation between dietary patterns and neurodevelopment in children.

Background: Psychotic disorders have been widely associated with structural brain abnormalities. However, it is unclear whether brain structure predicts psychotic experiences in youth from the general population, owing to an overall paucity of studies and predominantly cross-sectional designs. Here, the authors investigated longitudinal associations between brain morphology and hallucinations from childhood to early adolescence. Methods: This study was embedded in the population-based Generation R Study. Children underwent structural neuroimaging at age 10 years (N = 2042); a subsample received a second scan at age 14 years (n = 964). Hallucinations were assessed at ages 10 and 14 years and studied as a binary variable. Cross-lagged panel models and generalized linear mixed-effects models were fitted to examine longitudinal associations between brain morphology and hallucinations. Results: Smaller total gray and white matter volumes and total cortical surface area at baseline were associated with a higher occurrence of hallucinations between ages 10 and 14 years. The regions associated with hallucinations were widespread, including the frontal, parietal, temporal, and occipital lobes, as well as the insula and cingulate cortex. Analyses of subcortical structures revealed that smaller baseline hippocampal volumes were longitudinally associated with hallucinations, although this association was no longer significant following adjustment for intracranial volume. No evidence for reverse temporality was observed (i.e., hallucinations predicting brain differences). Conclusions: The findings from this longitudinal study suggest that global structural brain differences are associated with the development of hallucinations. These results extend findings from clinical populations and provide evidence for a neurodevelopmental vulnerability across the psychosis continuum.

Background Executive functions (EFs) are important partly because they are associated with risk for psychopathology and substance use problems. Because EFs have been linked to white matter microstructure, we tested the prediction that fractional anisotropy (FA) and mean diffusivity (MD) in white matter tracts are associated with EFs and dimensions of psychopathology in children younger than the age of widespread psychoactive substance use. Methods Parent symptom ratings, EF test scores, and diffusion tensor parameters from 8588 9- to 10-year-olds in the ABCD Study (Adolescent Brain Cognitive Development Study) were used. Results A latent factor derived from EF test scores was significantly associated with specific conduct problems and attention-deficit/hyperactivity disorder problems, with dimensions defined in a bifactor model. Furthermore, EFs were associated with FA and MD in 16 of 17 bilateral white matter tracts (range: β = .05; SE = .17; through β = −.31; SE = .06). Neither FA nor MD was directly associated with psychopathology, but there were significant indirect associations via EFs of both FA (range: β = .01; SE = .01; through β = −.09; SE = .02) and MD (range: β = .01; SE = .01; through β = .09; SE = .02) with both specific conduct problems and attention-deficit/hyperactivity disorder in all tracts except the forceps minor. Conclusions EFs in children are inversely associated with diffusion tensor imaging measures in nearly all tracts throughout the brain. Furthermore, variance in diffusion tensor measures that is shared with EFs is indirectly shared with attention-deficit/hyperactivity disorder and conduct problems.

Autism spectrum disorders (ASD) are associated with widespread brain alterations. Previous research in our group linked autistic traits with altered gyrification, but without pronounced differences in cortical thickness. Herein, we aim to replicate and extend these findings using a larger and older sample. Additionally, we examined whether (a) brain correlates of autistic traits were associated with polygenic risk scores (PRS) for ASD, and (b) autistic traits are related with brain morphological changes over time in a subset of children with longitudinal data available. The sample included 2400 children from the Generation R cohort. Autistic traits were measured using the Social Responsiveness Scale (SRS) at age 6 years. Gyrification, cortical thickness, surface area, and global morphological measures were obtained from high-resolution structural MRI scans at ages 9-to-12 years. We performed multiple linear regression analyses on a vertex-wise level. Corresponding regions of interest were tested for association with PRS. Results showed that autistic traits were related to (a) lower gyrification in the lateral occipital and the superior and inferior parietal lobes, (b) lower cortical thickness in the superior frontal region, and (c) lower surface area in inferior temporal and rostral middle frontal regions. PRS for ASD and longitudinal analyses showed significant associations that did not survive correction for multiple testing. Our findings support stability in the relationship between higher autistic symptoms and lower gyrification and smaller surface areas in school-aged children. These relationships remained when excluding ASD cases, providing neurobiological evidence for the extension of autistic traits into the general population. Lay Summary: We found that school-aged children with higher levels of autistic traits had smaller total brain volume, cerebellum, cortical thickness, and surface area. Further, we also found differences in the folding patterns of the brain (gyrification). Overall, genetic susceptibility for autism spectrum disorders was not related to these brain regions suggesting that other factors could be involved in their origin. These results remained significant when excluding children with a diagnosis of ASD, providing support for the extension of the relationship between autistic traits and brain findings into the general population.

OBJECTIVES: Dietary patterns (DPs) in childhood have been associated with neurodevelopmental disorders and cognitive performance in children. However, little is known about the underlying neurobiological mechanisms. We aimed to examine associations of DPs in early and mid-childhood with pre-adolescent brain morphology in a large population-based cohort. METHODS: We studied children's DPs at ages 1 and 8 and their associations with volumetric (total brain, hippocampus and amygdala) and surface-based brain measures (cortical thickness, surface area and gyrification) at age 10 years in a prospective population-based cohort in the Netherlands. High-resolution T(1)-weighted magnetic resonance images were collected in 3210 children. Brain measures were extracted using FreeSurfer. Dietary intake was assessed using age-specific food-frequency questionnaires. We derived DPs as diet quality scores based on dietary guidelines and identified Western and Prudent DPs using principal component analyses. We used multiple linear regression and surface-based analyses adjusting for multiple testing (Benjamini-Hochberg method) to examine associations with brain measures. RESULTS: Higher adherence to a Western DP at age 8 years was associated with a smaller total brain volume (B = –7.84, 95% CI –13.06, –2.61), whereas higher adherence to a Prudent DP at age 8 years was associated with a larger total brain volume (B = 8.45, 95% CI 4.25, 12.65). In addition, children with higher diet quality and better adherence to a Prudent DP showed greater gyrification and larger surface area, mostly clustered in the prefrontal cortex. At age 1 year, no statistically significant associations between DPs and global or surface-based brain measures were observed. No volumetric differences were found in the hippocampus or amygdala at both ages. CONCLUSIONS: We are the first to show in a large population-based setting that dietary patterns in mid-childhood are associated with brain structural alterations, with a prudent diet being linked to a more optimal development. These findings suggest that a healthy diet is important for children's brain development, which may explain part of the previously demonstrated relation between dietary patterns and psychosocial well-being and cognitive performance in children. FUNDING SOURCES: This project did not receive any funding.

Brain morphology is altered in both anorexia nervosa and obesity. However, it is yet unclear if the relationship between Body Mass Index-Standard Deviation Score (BMI-SDS) and brain morphology exists across the BMI-SDS spectrum, or is present only in the extremes. The study involved 3160 9-to-11 year-old children (50.3% female) who participate in Generation R, a population-based study. Structural MRI scans were obtained from all children and FreeSurfer was used to quantify both global and surface-based measures of gyrification and cortical thickness. Body length and weight were measured to calculate BMI. Dutch growth curves were used to calculate BMI-SDS. BMI-SDS was analyzed continuously and in two categories (median split). The relationship between BMI-SDS (range − 3.82 to 3.31) and gyrification showed an inverted-U shape curve in children with both lower and higher BMI-SDS values having lower gyrification in widespread areas of the brain. BMI-SDS had a positive linear association with cortical thickness in multiple brain regions. This study provides evidence for an association between BMI-SDS and brain morphology in a large sample of children from the general population and suggests that a normal BMI during childhood is important for brain development. Future studies could determine whether lifestyle modifications optimize BMI-SDS result in return to more typical patterns of brain morphology. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-020-02209-0.

Increasingly, policies are being introduced to reward and recognise open research practices, while the adoption of such practices into research routines is being facilitated by many grassroots initiatives. However, despite this widespread endorsement and support, as well as various efforts led by early career researchers, open research is yet to be widely adopted. For open research to become the norm, initiatives should engage academics from all career stages, particularly senior academics (namely senior lecturers, readers, professors) given their routine involvement in determining the quality of research. Senior academics, however, face unique challenges in implementing policy changes and supporting grassroots initiatives. Given that—like all researchers—senior academics are motivated by self-interest, this paper lays out three feasible steps that senior academics can take to improve the quality and productivity of their research, that also serve to engender open research. These steps include changing (a) hiring criteria, (b) how scholarly outputs are credited, and (c) how we fund and publish in line with open research principles. The guidance we provide is accompanied by material for further reading.

Psychopathology and cognitive development are closely related. Assessing the relationship between multiple domains of psychopathology and cognitive performance can elucidate which cognitive tasks are related to specific domains of psychopathology. This can help build theory and improve clinical decision-making in the future. In this study, we included 13,841 children and adolescents drawn from two large population-based samples (Generation R and ABCD studies). We assessed the cross-sectional relationship between three psychopathology domains (internalizing, externalizing, dysregulation profile (DP)) and four cognitive domains (vocabulary, fluid reasoning, working memory, and processing speed) and the full-scale intelligence quotient. Lastly, differential associations between symptoms of psychopathology and cognitive performance by sex were assessed. Results indicated that internalizing symptoms were related to worse performance in working memory and processing speed, but better performance in the verbal domain. Externalizing and DP symptoms were related to poorer global cognitive performance. Notably, those in the DP subgroup had a 5.0 point lower IQ than those without behavioral problems. Cognitive performance was more heavily affected in boys than in girls given comparable levels of psychopathology. Taken together, we provide evidence for globally worse cognitive performance in children and adolescents with externalizing and DP symptoms, with those in the DP subgroup being most heavily affected.

Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community., Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. However, data sharing initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. This article discusses the opportunities, challenges, benefits for sharing neuroimaging data, including the missed opportunities in not providing participants greater say in opportunities for data sharing initiatives.

Background: Gender diversity in young adolescents is understudied outside of referral clinics. We investigated gender diversity in an urban, ethnically diverse sample of adolescents from the general population and examined predictors and associated mental health outcomes. Methods: The study was embedded in Generation R, a population-based cohort of children born between 2002 and 2006 in Rotterdam, the Netherlands (n = 5727). At ages 9–11 and 13–15 years, adolescents and/or their parents responded to two questions addressing children’s contentedness with their assigned gender, whether they (a) ‘wished to be the opposite sex’ and (b) ‘would rather be treated as someone from the opposite sex’. We defined ‘gender-variant experience’ when either the parent or child responded with ‘somewhat or sometimes true’ or ‘very or often true’. Mental health was assessed at 13–15 years, using the Achenbach System of Empirically Based Assessment. Results: Less than 1% of the parents reported that their child had gender-variant experience, with poor stability between 9–11 and 13–15 years. In contrast, 4% of children reported gender-variant experience at 13–15 years. Adolescents who were assigned female at birth reported more gender-variant experience than those assigned male. Parents with low/medium educational levels reported more gender-variant experience in their children than those with higher education. There were positive associations between gender-variant experience and symptoms of anxiety, depression, somatic complaints, rule-breaking, and aggressive behavior as well as attention, social, and thought problems. Similar associations were observed for autistic traits, independent of other mental difficulties. These associations did not differ by assigned sex at birth. Conclusions: Within this population-based study, adolescents assigned females were more likely to have gender-variant experience than males. Our data suggest that parents may not be aware of gender diversity feelings in their adolescents. Associations between gender diversity and mental health symptoms were present in adolescents.

The field of psychiatry increasingly highlights the importance of studying not only the influence of the brain on behavior, but also the long-term influences that the persistence of specific behaviors can have on the brain. A severe behavioral phenotype that puts children at risk for later psychopathology is the Child Behavior Checklist-Dysregulation Profile (CBCL-DP).

Background Sleep problems, altered sleep patterns and mental health difficulties often co-occur in the pediatric population. Different assessment methods for sleep exist, however, many studies only use one measure of sleep or focus on one specific mental health problem. In this population-based study, we assessed different aspects of sleep and mother-reported mental health to provide a broad overview of the associations between reported and actigraphic sleep characteristics and mental health. Methods This cross-sectional study included 788 children 10-11-year-old children (52.5% girls) and 344 13–14-year-old children (55.2% girls). Mothers and children reported on the sleep of the child and wrist actigraphy was used to assess the child’s sleep patterns and 24 h activity rhythm. Mental health was assessed via mother-report and covered internalizing, externalizing and a combined phenotype of internalizing and externalizing symptoms, the dysregulation profile. Results Higher reported sleep problems were related to more symptoms of mental health problems in 10–11- and 13–14-year-old adolescents, with standardized ß-estimates ranging between 0.11 and 0.35. There was no association between actigraphy-estimated sleep and most mental health problems, but earlier sleep onset was associated with more internalizing problems (ß = − 0.09, SE = 0.03, p-value = 0.002), and higher intra-daily variability of the 24 h activity rhythm was associated with more dysregulation profile symptoms at age 10–11 (ß = 0.11, SE = 0.04, p-value = 0.002). Discussion Reported sleep problems across informants were related to all domains of mental health problems, providing evidence that sleep can be an important topic to discuss for clinicians seeing children with mental health problems. Actigraphy-estimated sleep characteristics were not associated with most mental health problems. The discrepancy between reported and actigraphic sleep measures strengthens the idea that these two measures tap into distinct constructs of sleep.

Magnetic resonance imaging (MRI) procedures often evoke anxiety in children. Further, anxious children may be less likely to participate in MRI research, leading to a possible selection bias, and may be more likely to move during image acquisition, resulting in lower image quality and possible information bias. Therefore, state anxiety is problematic for functional and structural MRI studies. Children with behavioral problems, such as internalizing and externalizing behaviors, may be more likely to experience state anxiety prior to and during MRI scanning. Therefore, our first aim was to investigate the relationship between internalizing/externalizing behavior and children’s MRI-related state anxiety. Our second aim was to investigate the relationship between internalizing and externalizing behavior and MRI research participation. Our final aim was to investigate the effect of internalizing and externalizing behavior as well as MRI-related anxiety on image quality in children. We included 1,241 six- to ten-year-old children who underwent a mock MRI. Afterwards, if not too anxious, these children were scanned using a 3-Tesla GE Discovery MRI system (n= 1,070). Internalizing and externalizing behaviors were assessed with the Child Behavior Checklist. State anxiety was assessed with a visual analogue scale. Internalizing behaviors were positively associated with child state anxiety, as reported by child, parent, and researcher. For state anxiety reported by the parent and researcher, this relationship was independent of externalizing behaviors. Externalizing behaviors were related to state anxiety reported by the researcher, but this difference was not independent of internalizing behaviors, pointing towards a relationship via the shared variance with internalizing behaviors. Further, children with more internalizing and externalizing behaviors were less likely to participate in the actual MRI scanning procedure. Lastly, internalizing and externalizing behaviors, as well as MRI-related state anxiety were associated with worse image quality. These results underscore the potential for biases and methodological issues caused by MRI-related state anxiety in children.

Some children can handle big problems without getting upset, while other children quickly become frustrated or irritable. If we want to understand why some children become more frustrated or irritable than other children, we need to study the brain, because the brain controls behavior. To understand irritability in children, we need to learn about three things. First, we need to know how children recognize the emotions of other people, because if children are not able to understand the emotions of other children, they might get frustrated. Second, we need to learn about how it feels to get a reward, because children who expect a reward, but do not get one, can become frustrated. Finally, we can ask how we can teach children to cope better. Learning about how children’s brains work can help us to understand why some children become frustrated or irritable more quickly than other children.

SummaryHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. Here, we identified common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal MRI data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genesGPR139, DACH1andAPOEare associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene-set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and ageing.

Think about this: Linke et al.1 are publishing the "first" ever study using diffusion tensor imaging (DTI) in children with disruptive mood dysregulation disorder (DMDD). Child and adolescent psychiatrists know that these children are not new and are not uncommonly seen in both inpatient and outpatient mental health settings. So, why does the first paper examining the microstructural differences in white matter tracts in children with DMDD emerge in 2019? If you search PubMed using the key words "autism" and "diffusion tensor imaging" (as children with autistic symptoms have likely been around for as long as children with symptoms of dysregulation), you end up with 305 papers. So, what's the story? Was aberrant white matter microstructure never considered as a putative hypothesis for DMDD? Or did DMDD fall under a different name, and, as Shakespeare once wrote, "a rose by any other name would smell as sweet"? Or, in our loose psychiatric-based translation from Old English, "a pediatric bipolar child by any other name is just as dysregulated?" Indeed, DMDD has had a circuitous and somewhat shady history, with its roots in the name "pediatric bipolar disorder."2 Because a number of long-term studies showed that few children with chronic irritable mood problems progressed to develop adult bipolar affective disorder (BPAD),3 DMDD emerged in the DSM-5.4.