The ABCD study: understanding the development of risk for mental and physical health outcomes

Longitudinal Associations Between Perceived Discrimination and Suicidality in Youth

Research among adults reveals robust associations between discrimination and suicidality. However, the relationship between discrimination and suicidality is understudied in youth. Participants in the Adolescent Brain Cognitive Development study (n = 10  312) completed a measure of discrimination based on multiple attributes. The Kiddie Schedule for Affective Disorders and Schizophrenia was administered 1 year later to assess depressive disorders and suicidality (ideation and behavior). Logistic regressions, adjusting for age, sex, race/ethnicity, family income, lifetime depressive disorders, and body composition were conducted. Adjusting for covariates, discrimination based on weight (OR: 2.19), race/ethnicity/color (OR: 3.21), and sexual orientation (OR: 3.83) were associated with greater odds of reporting suicidality 1 year later (ps < 0.025). Nationality-based discrimination was not significantly associated with suicidality. Compared with those reporting no discrimination, youths reporting discrimination based on 2 or more attributes had nearly 5 times greater odds of recent suicidality (OR: 4.72; P < .001). The current study highlights the deleterious impacts of discrimination on mental health among youths reporting multiple forms of discrimination.

Associations Between Polygenic Scores for Cognitive and Non-cognitive Factors of Educational Attainment and Measures of Behavior, Psychopathology, and Neuroimaging in the Adolescent Brain Cognitive Development Study

Background

Both cognitive and non-cognitive (e.g., traits like curiosity) factors are critical for social and emotional functioning and independently predict educational attainment. These factors are heritable and genetically correlated with a range of health-relevant traits and behaviors in adulthood (e.g., risk-taking, psychopathology). However, whether these associations are present during adolescence, and to what extent these relationships diverge, could have implications for adolescent health and well-being.

Methods

Using data from 5,517 youth of European ancestry from the ongoing Adolescent Brain Cognitive DevelopmentSM Study, we examined associations between polygenic scores (PGS) for cognitive and non-cognitive factors and outcomes related to cognition, socioeconomic status, risk tolerance and decision-making, substance initiation, psychopathology, and brain structure.

Results

Cognitive and non-cognitive PGSs were both positively associated with cognitive performance and family income, and negatively associated with ADHD and severity of psychotic-like experiences. The cognitive PGS was also associated with greater risk-taking, delayed discounting, and anorexia, as well as lower likelihood of nicotine initiation. The cognitive PGS was further associated with cognition scores and anorexia in within-sibling analyses, suggesting these results do not solely reflect the effects of assortative mating or passive gene-environment correlations. The cognitive PGS showed significantly stronger associations with cortical volumes than the non-cognitive PGS and was associated with right hemisphere caudal anterior cingulate and pars-orbitalis in within-sibling analyses, while the non-cognitive PGS showed stronger associations with white matter fractional anisotropy and a significant within-sibling association for right superior corticostriate-frontal cortex.

Conclusions

Our findings suggest that PGSs for cognitive and non-cognitive factors show similar associations with cognition and socioeconomic status as well as other psychosocial outcomes, but distinct associations with regional neural phenotypes in this adolescent sample.

Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with high prevalence, heritability, and heterogeneity. Children with a positive family history of ADHD have a heightened risk of ADHD emergence, persistence, and executive function deficits, with the neural mechanisms having been under investigated. The objective of this study was to investigate working memory-related functional brain activation patterns in children with ADHD (with vs. without positive family histories (ADHD-F vs. ADHD-NF)) and matched typically developing children (TDC). Voxel-based and region of interest analyses were conducted on two-back task-based fMRI data of 362 subjects, including 186, 96, and 80 children in groups of TDC, ADHD-NF, and ADHD-F, respectively. Relative to TDC, both ADHD groups had significantly reduced activation in the left inferior frontal gyrus (IFG). And the ADHD-F group demonstrated a significant positive association of left IFG activation with task reaction time, a negative association of the right IFG with ADHD symptomatology, and a negative association of the IFG activation laterality index with the inattention symptom score. These results suggest that working memory-related functional alterations in bilateral IFGs may play distinct roles in ADHD-F, with the functional underdevelopment of the left IFG significantly informing the onset of ADHD symptoms. Our findings have the potential to assist in tailored diagnoses and targeted interventions in children with ADHD-F.

Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.

Effects of family income on brain functional connectivity in US children: associations with cognition

Higher family income (FI) is associated with larger cortical gray matter volume and improved cognitive performance in children. However, little is known about the effects of FI on brain functional and structural connectivity. This cross-sectional study investigates the effects of FI on brain connectivity and cognitive performance in 9- to 11-years old children (n = 8739) from the Adolescent Brain Cognitive Development (ABCD) study. Lower FI was associated with decreased global functional connectivity density (gFCD) in the default-mode network (DMN), inferior and superior parietal cortices and in posterior cerebellum, and increased gFCD in motor, auditory, and extrastriate visual areas, and in subcortical regions both for girls and boys. Findings demonstrated high reproducibility in Discovery and Reproducibility samples. Cognitive performance partially mediated the association between FI and DMN connectivity, whereas DMN connectivity did not mediate the association between FI and cognitive performance. In contrast, there was no significant association between FI and structural connectivity. Findings suggest that poor cognitive performance, which likely reflects multiple factors (genetic, nutritional, the level and quality of parental interactions, and educational exposure [1]), contributes to reduced DMN functional connectivity in children from low-income families. Follow-up studies are needed to help clarify if this leads to reductions in structural connectivity as these children age.

Transmit Radiant Individuality to Offspring (TRIO) study: investigating intergenerational transmission effects on brain development

Intergenerational transmission is a crucial aspect of human development. Although prior studies have demonstrated the continuity of psychopathology and maladaptive upbringing environments between parents and offspring, the underlying neurobiological mechanisms remain unclear. We have begun a novel neuroimaging research project, the Transmit Radiant Individuality to Offspring (TRIO) study, which focuses on biological parent-offspring trios. The participants of the TRIO study were Japanese parent-offspring trios consisting of offspring aged 10-40 and their biological mother and father. Structural and functional brain images of all participants were acquired using magnetic resonance imaging (MRI). Saliva samples were collected for DNA analysis. We obtained psychosocial information, such as intelligence, mental health problems, personality traits, and experiences during the developmental period from each parent and offspring in the same manner as much as possible. By April 2023, we completed data acquisition from 174 trios consisting of fathers, mothers, and offspring. The target sample size was 310 trios. However, we plan to conduct genetic and epigenetic analyses, and the sample size is expected to be expanded further while developing this project into a multi-site collaborative study in the future. The TRIO study can challenge the elucidation of the mechanism of intergenerational transmission effects on human development by collecting diverse information from parents and offspring at the molecular, neural, and behavioral levels. Our study provides interdisciplinary insights into how individuals' lives are involved in the construction of the lives of their descendants in the subsequent generation.

Reconfiguration of brain-wide neural activity after early life adversity

Early life adversity (ELA) predisposes individuals to both physical and mental disorders lifelong. How ELA affects brain function leading to this vulnerability is under intense investigation. Research has begun to shed light on ELA effects on localized brain regions within defined circuits. However, investigations into brain-wide neural activity that includes multiple localized regions, determines relationships of activity between regions and identifies shifts of activity in response to experiential conditions is necessary. Here, we performed longitudinal manganese-enhanced magnetic resonance imaging (MEMRI) to image the brain in normally reared or ELA-exposed adults. Images were captured in the freely moving home cage condition, and short- and long-term after naturalistic threat. Images were analyzed with new computational methods, including automated segmentation and fractional activation or difference volumes. We found that neural activity was increased after ELA compared to normal rearing in multiple brain regions, some of which are involved in defensive and/or reward circuitry. Widely distributed patterns of neural activity, "brain states", and their dynamics after threat were altered with ELA. Upon acute threat, ELA-mice retained heightened neural activity within many of these regions, and new hyperactive responses emerged in monoaminergic centers of the mid- and hindbrain. Nine days after acute threat, heightened neural activity remained within locus coeruleus and increased within posterior amygdala, ventral hippocampus, and dorso- and ventromedial hypothalamus, while reduced activity emerged within medial prefrontal cortical regions (prelimbic, infralimbic, anterior cingulate). These results reveal that functional imbalances arise between multiple brain-systems which are dependent upon context and cumulative experiences after ELA.

Specificity of associations between parental psychopathology and offspring brain structure

Multiple forms of parental psychopathology have been associated with differences in subcortical brain volume. However, few studies have considered the role of comorbidity. Here, we examine if alterations in child subcortical brain structure are specific to parental depression, anxiety, mania, or alcohol/substance use parental psychopathology, common across these disorders, or altered by a history of multiple disorders. We examined 6581 children aged 9 to 10 years old from the ABCD study with no history of mental disorders. We found several significant interactions such that the effects of a parental history of depression, anxiety, and substance use problems on amygdala and striatal volumes were moderated by comorbid parental history of another disorder. Interactions tended to suggest smaller volumes in the presence of a comorbid disorder. However, effect sizes were small, and no associations remained significant after correcting for multiple comparisons. Results suggest that associations between familial risk for psychopathology and offspring brain structure in 9-10-year-olds are modest, and relationships that do exist tend to implicate the amygdala and striatal regions and are moderated by a comorbid parental psychopathology history. Several methodological factors, including controlling for intracranial volume and other forms of parental psychopathology and excluding child psychopathology, likely contribute to inconsistencies in the literature.

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence

Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

Estimating the Total Variance Explained by Whole-Brain Imaging for Zero-inflated Outcomes

Zero-inflated outcomes are very common in behavioral data, particularly for responses to psychological questionnaires. Modeling these challenging distributions is further exacerbated by the absence of established statistical models capable of characterizing total signals attributed to whole-brain imaging features, making the accurate assessment of brain-behavior relationships particularly formidable. Given this critical need, we have developed a novel variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes . Our zero-inflated variance (ZIV) estimator robustly estimates the fraction of variance explained (FVE) and the proportion of non-null effects from large-scale imaging data. In simulations, ZIV outperformed other linear prediction algorithms. Applying ZIV to data from one of the largest neuroimaging studies, the Adolescent Brain Cognitive Development SM (ABCD) Study, we found that whole-brain imaging features have a larger FVE for externalizing compared to internalizing behavior. We also demonstrate that the ZIV estimator, especially applied to focal sub-scales, can localize key neurocircuitry associated with human behavior.

Ancestral, Pregnancy, and Negative Early-Life Risks Shape Children's Brain (Dis)similarity to Schizophrenia

BACKGROUND

Familial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children.

METHODS

We used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child's cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control differences.

RESULTS

In children with a family history of SSD, the regional vulnerability index captured significantly more variance in ancestral history than traditional whole-brain and regional brain measurements. In children with and without family history of SSD, the regional vulnerability index also captured more variance associated with negative pre/perinatal environment and early-life experiences than traditional brain measurements.

CONCLUSIONS

In summary, in a cohort in which most children will not develop SSD, familial, pre/perinatal, and early developmental risks can alter brain patterns in the direction observed in adult patients with SSD. Individual similarity to adult SSD patterns may provide an early biomarker of the effects of genetic and developmental risks on the brain prior to psychotic or prodromal symptom onset.

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

The longitudinal role of family conflict and neural reward sensitivity in youth's internalizing symptoms

Adolescence is often associated with an increase in psychopathology. Although previous studies have examined how family environments and neural reward sensitivity separately play a role in youth's emotional development, it remains unknown how they interact with each other in predicting youth's internalizing symptoms. Therefore, the current research took a biopsychosocial approach to examine this question using two-wave longitudinal data of 9353 preadolescents (mean age = 9.93 years at T1; 51% boys) from the Adolescent Brain Cognitive Development study. Using mixed-effects models, results showed that higher family conflict predicted youth's increased internalizing symptoms 1 year later, whereas greater ventral striatum (VS) activity during reward receipt predicted reduced internalizing symptoms over time. Importantly, there was an interaction effect between family conflict and VS activity. For youth who showed greater VS activation during reward receipt, high family conflict was more likely to predict increased internalizing symptoms. In contrast, youth with low VS activation during reward receipt showed high levels of internalizing symptoms regardless of family conflict. The findings suggest that youth's neural reward sensitivity is a marker of susceptibility to adverse family environments and highlight the importance of cultivating supportive family environments where youth experience less general conflict within the family.

Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth

BACKGROUND

Integrating multiple neuroimaging modalities to identify clusters of individuals and then associating these clusters with psychopathology is a promising approach for understanding neurobiological mechanisms that underlie psychopathology and the extent to which these features are associated with clinical symptoms.

METHODS

We leveraged neuroimaging data from T1-weighted, diffusion-weighted, and resting-state functional magnetic resonance images from the Adolescent Brain Cognitive Development (ABCD) Study (N = 8035) and used similarity network fusion and spectral clustering to identify subgroups of participants. We examined neuroimaging measures as a function of clustering profiles using 1, 2, or 3 imaging modalities (i.e., data combinations), calculated the stability of the clustering assignment in each respective data combination, and compared the consistency of clusters across different data combinations. We then compared the extent to which clusters were associated with overall psychopathology at the baseline assessment and at 2 yearly follow-up visits.

RESULTS

Each data combination resulted in optimal clusters ranging from 2 to 4 subgroups for each data combination. Clusters were stable across subsampling of the ABCD Study cohort. Widespread structural measures (surface area, fractional anisotropy, and mean diffusivity) were important features contributing to clustering across different data combinations. Five of the seven data combinations were associated with overall psychopathology, both at baseline and over time (d = 0.08-0.41). Generally, lower global cortical volume and surface area, widespread reduced fractional anisotropy, and increased radial diffusivity were associated with increased overall psychopathology.

CONCLUSIONS

Profiles constructed from neuroimaging data combinations are associated with concurrent and future psychopathology trajectories.

Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings

Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings. In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach-which we refer to as "cognitive microscopy"-that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.

Association of Racial Discrimination With Adiposity in Children and Adolescents

Importance

Childhood obesity is a major public health issue and is disproportionately prevalent among children from minority racial and ethnic groups. Personally mediated racism (commonly referred to as racial discrimination) is a known stressor that has been linked to higher body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) in adults, but little is known about the association of racial discrimination and childhood and adolescent adiposity.

Objective

To assess the prospective association between self-reported experiences of racial discrimination and adiposity (BMI and waist circumference) in a large sample of children and adolescents in the Adolescent Brain Cognitive Development (ABCD) study.

Design, Setting, and Participants

This cohort study used complete data from the ABCD study (2017 to 2019), involving a total of 6463 participants. The ABCD study recruited a diverse sample of youths from across the US, with rural, urban, and mountain regions. Data were analyzed from January 12 to May 17, 2023.

Exposure

The child-reported Perceived Discrimination Scale was used to quantify racial discrimination, reflecting participants' perceptions of being treated unfairly by others or unaccepted by society based on their race or ethnicity.

Main Outcomes and Measures

Weight, height, and waist circumference were measured by trained research assistants. BMI z scores were computed by applying the US Centers for Disease Control and Prevention's age and sex-specific reference standards for children and adolescents. Waist circumference (inches) was quantified as the mean of 3 consecutive measures. Measurements were taken from time 1 (ie, 2017 to 2019) and time 2 (ie, 2018 to 2020).

Results

Of the 6463 respondents with complete data, 3090 (47.8%) were female, and the mean (SD) age was 9.95 (0.62) years. Greater racial discrimination exposure at time 1 was associated with higher BMI z score in both unadjusted (β, 0.05; 95% CI, 0.02-0.08) and adjusted regression models (β, 0.04; 95% CI, 0.01-0.08). Discrimination at time 1 was associated with higher waist circumference in unadjusted (β, 0.35; 95% CI, 0.15-0.54) and adjusted (β, 0.24; 95% CI, 0.04-0.44) models.

Conclusions and Relevance

In this cohort study of children and adolescents, racial discrimination was positively associated with adiposity, quantified by BMI z score and waist circumference. Interventions to reduce exposure to racial discrimination in early life may help reduce the risk of excess weight gain across throughout life.

Pathways link environmental and genetic factors with structural brain networks and psychopathology in youth

Adolescence is a period of significant brain development and maturation, and it is a time when many mental health problems first emerge. This study aimed to explore a comprehensive map that describes possible pathways from genetic and environmental risks to structural brain organization and psychopathology in adolescents. We included 32 environmental items on developmental adversity, maternal substance use, parental psychopathology, socioeconomic status (SES), school and family environment; 10 child psychopathological scales; polygenic risk scores (PRS) for 10 psychiatric disorders, total problems, and cognitive ability; and structural brain networks in the Adolescent Brain Cognitive Development study (ABCD, n = 9168). Structural equation modeling found two pathways linking SES, brain, and psychopathology. Lower SES was found to be associated with lower structural connectivity in the posterior default mode network and greater salience structural connectivity, and with more severe psychosis and internalizing in youth (p < 0.001). Prematurity and birth weight were associated with early-developed sensorimotor and subcortical networks (p < 0.001). Increased parental psychopathology, decreased SES and school engagement was related to elevated family conflict, psychosis, and externalizing behaviors in youth (p < 0.001). Increased maternal substance use predicted increased developmental adversity, internalizing, and psychosis (p < 0.001). But, polygenic risks for psychiatric disorders had moderate effects on brain structural connectivity and psychopathology in youth. These findings suggest that a range of genetic and environmental factors can influence brain structural organization and psychopathology during adolescence, and that addressing these risk factors may be important for promoting positive mental health outcomes in young people.

The prospective relationship between weight-based discrimination and eating pathology among youth

Among adults and adolescents, weight-based discrimination is associated with disordered eating. However, these relationships remain understudied in children. Given that weight-based discrimination is commonly reported among youth, and that childhood is a crucial developmental period for the onset of disordered eating, the current study assessed prospective associations between weight-based discrimination and eating pathology among participants in the Adolescent Brain Cognitive Development Study. At the one-year visit, children indicated whether they had experienced discrimination due to their weight within the past year. Parents completed a computerized clinical interview to determine the presence of sub-or-full threshold eating disorders (AN, BN, and BED) among their children. At the two-year visit, children completed the same assessment. Height and fasting weight were obtained. Logistic regressions, adjusting for age, sex, race/ethnicity, family income, BMI%ile, and parent-reported presence of the respective eating disorder at one-year, were conducted to assess the associations between weight-based discrimination and eating pathology. Participants were 10,299 children who completed measures at both the one- and two-year visits (M_age at one-year: 10.92 ± 0.64, 47.6 % female, 45.9 % racial/ethnic minority). The presence of weight-based discrimination, reported by 5.6 % (n = 574) of children, was significantly associated with a greater likelihood of reporting AN, BN, and BED one-year later (ORs: 1.94-4.91). Findings suggest that weight-based discrimination may confer additional risk for the onset of disordered eating, above and beyond the contribution of body weight. Intersectional research is needed to examine the role of multiple forms of discrimination in relation to the development of eating pathology.

Shared molecular genetic factors influence subcortical brain morphometry and Parkinson's disease risk

Parkinson's disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson's Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.

Gene-by-Environment Interaction Effects of Social Adversity on Externalizing Behavior in ABCD Youth

This study tested whether multiple domains of social adversity, including neighborhood opportunity/deprivation and life stress, moderate genetic (A), common environmental (C), and unique environmental (E) influences on externalizing behaviors in 760 same-sex twin pairs (332 monozygotic; 428 dizygotic) ages 10-11 from the ABCD Study. Proportion of C influences on externalizing behavior increased at higher neighborhood adversity (lower overall opportunity). A decreased and C and E increased at lower levels of educational opportunity. A increased at lower health-environment and social-economic opportunity levels. For life stress, A decreased and E increased with number of experienced events. Results for educational opportunity and stressful life experiences suggest a bioecological gene-environment interaction pattern such that environmental influences predominate at higher levels of adversity, whereas limited access to healthcare, housing, and employment stability may potentiate genetic liability for externalizing behavior via a diathesis-stress mechanism. More detailed operationalization of social adversity in gene-environment interaction studies is needed.

Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization

Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

Importance

The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people's understanding of and willingness to join and remain engaged in remote health studies.

Objective

To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children.

Design, Setting, and Participants

This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022.

Main Outcomes and Measures

The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined.

Results

The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (-59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (β = -43.00 hours; 95% CI, -55.11 to -30.88 hours; P < .001).

Conclusions and Relevance

In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals' health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children

IMPORTANCE

Early detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children's mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life.

OBJECTIVE

To evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study.

DESIGN, SETTING, AND PARTICIPANTS

In this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm-based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models.

MAIN OUTCOMES AND MEASURES

The average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features.

RESULTS

The final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992).

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an ML method for early detection or screening using digital phenotypes in children's daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders

Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9-10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.

Social goals in girls transitioning to adolescence: associations with psychopathology and brain network connectivity

The motivation to socially connect with peers increases during adolescence in parallel with changes in neurodevelopment. These changes in social motivation create opportunities for experiences that can impact risk for psychopathology, but the specific motivational presentations that confer greater psychopathology risk are not fully understood. To address this issue, we used a latent profile analysis to identify the multidimensional presentations of self-reported social goals in a sample of 220 girls (9-15 years old, M = 11.81, SD = 1.81) that was enriched for internalizing symptoms, and tested the association between social goal profiles and psychopathology. Associations between social goals and brain network connectivity were also examined in a subsample of 138 youth. Preregistered analyses revealed four unique profiles of social goal presentations in these girls. Greater psychopathology was associated with heightened social goals such that higher clinical symptoms were related to a greater desire to attain social competence, avoid negative feedback and gain positive feedback from peers. The profiles endorsing these excessive social goals were characterized by denser connections among social-affective and cognitive control brain regions. These findings thus provide preliminary support for adolescent-onset changes in motivating factors supporting social engagement that may contribute to risk for psychopathology in vulnerable girls.

Hybrid autoencoder with orthogonal latent space for robust population structure inference

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

Effects of prenatal cannabis exposure on developmental trajectory of cognitive ability and brain volumes in the adolescent brain cognitive development (ABCD) study

Although cannabis use during pregnancy is increasing widely, the effects of cannabis on developmental trajectories, such as whether its effects during pregnancy remain the same between time points or gradually increase, are unclear. This study aimed to examine whether cannabis use during pregnancy affects the process of change in cognition and brain volume. Data from two-time points measured longitudinally were analyzed. We used data from the Adolescent Brain and Cognitive Development Study. Participants included 11,876 children aged 9-11 years participated at baseline, and 10,414 participated at 2-year follow-up from 22 sites across the United States. We explored the associations between prenatal cannabis exposure and cognitive abilities and brain volumes developmental trajectories. Among 11,530 children with valid data for prenatal cannabis exposure, 10,833 had no prenatal cannabis use, and 697 had cannabis use during their pregnancy. There was a significant interaction between time points and cannabis use during pregnancy on visuo-perceptual processing ability (b = -0.019, p = .009) and intracranial volumes (b = -6338.309, p = .009). We found that the effects of exposure to cannabis during pregnancy are not uniform at all times and may gradually become more apparent and magnified as development progresses.

Sleep Quality and Duration in Children That Consume Caffeine: Impact of Dose and Genetic Variation in ADORA2A and CYP1A

Caffeine is the most consumed drug in the world, and it is commonly used by children. Despite being considered relatively safe, caffeine can have marked effects on sleep. Studies in adults suggest that genetic variants in the adenosine A2A receptor (ADORA2A, rs5751876) and cytochrome P450 1A (CYP1A, rs2472297, rs762551) loci are correlated with caffeine-associated sleep disturbances and caffeine intake (dose), but these associations have not been assessed in children. We examined the independent and interaction effects of daily caffeine dose and candidate variants in ADORA2A and CYP1A on the sleep quality and duration in 6112 children aged 9-10 years who used caffeine and were enrolled in the Adolescent Brain Cognitive Development (ABCD) study. We found that children with higher daily caffeine doses had lower odds of reporting > 9 h of sleep per night (OR = 0.81, 95% CI = 0.74-0.88, and p = 1.2 × 10^-6). For every mg/kg/day of caffeine consumed, there was a 19% (95% CI = 12-26%) decrease in the odds of children reporting > 9 h of sleep. However, neither ADORA2A nor CYP1A genetic variants were associated with sleep quality, duration, or caffeine dose. Likewise, genotype by caffeine dose interactions were not detected. Our findings suggest that a daily caffeine dose has a clear negative correlation with sleep duration in children, but this association is not moderated by the ADORA2A or CYP1A genetic variation.

Prediction of gender from longitudinal MRI data via deep learning on adolescent data reveals unique patterns associated with brain structure and change over a two-year period

Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs > 200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.

Advances in stress and depression research

PURPOSE OF REVIEW

Stress plays a central role in the onset and course of depression. However, only a subset of people who encounter stressful life events go on to experience a depressive episode. The current review highlights recent advances in understanding when, why, and for whom the stress-depression link occurs, and we identify avenues for future research.

RECENT FINDINGS

In the last 18 months, researchers have taken a more nuanced perspective on the biopsychosocial mechanisms critical to the stress-depression link. For example, examination of specific facets of emotion regulation, including emotion regulation flexibility and interpersonal emotion regulation, has been critical to understanding its role in depression. Similarly, refined investigations of social support allowed researchers to identify distinct - and occasionally opposite - outcomes depending on the context or manner in which the support was provided. Researchers also documented that the stress-depression link was enhanced by dysregulation of several stress-sensitive biological systems, such as the immune system, microbiome, endocrine system, and neuroanatomical substrates.

SUMMARY

Recent studies highlight the importance of adopting a nuanced understanding of mechanisms and moderators that explain the stress-depression link. We also encourage continued engagement in collaborative, open science that uses multiple methods to study the full breadth of human diversity.

Structural abnormalities in adolescents with conduct disorder and high versus low callous unemotional traits

There may be distinct conduct disorder (CD) etiologies and neural morphologies in adolescents with high callous unemotional (CU) traits versus low CU traits. Here, we employed surface-based morphometry methods to investigate morphological differences in adolescents diagnosed with CD [42 with high CU traits (CD-HCU) and 40 with low CU traits (CD-LCU)] and healthy controls (HCs, N = 115) in China. Whole-brain analyses revealed significantly increased cortical surface area (SA) in the left inferior temporal cortex and the right precuneus, but decreased SA in the left superior temporal cortex in the CD-LCU group, compared with the HC group. There were no significant cortical SA differences between the CD-HCU and the HC groups. Compared to the CD-HCU group, the CD-LCU group had a greater cortical thickness (CT) in the left rostral middle frontal cortex. Region-of-interest analyses revealed significant group differences in the right hippocampus, with CD-HCU group having lower right hippocampal volumes than HCs. We did not detect significant group differences in the amygdalar volume, however, the right amygdalar volume was found to be a significant moderator of the correlation between CU traits and the proactive aggression in CD patients. The present results suggested that the manifestations of CD differ between those with high CU traits versus low CU traits, and underscore the importance of sample characteristics in understanding the neural substrates of CD.

Emotion regulation and the salience network: a hypothetical integrative model of fibromyalgia

Fibromyalgia is characterized by widespread pain, fatigue, sleep disturbances and other symptoms, and has a substantial socioeconomic impact. Current biomedical and psychosocial treatments are unsatisfactory for many patients, and treatment progress has been hindered by the lack of a clear understanding of the pathogenesis of fibromyalgia. We present here a model of fibromyalgia that integrates current psychosocial and neurophysiological observations. We propose that an imbalance in emotion regulation, reflected by an overactive 'threat' system and underactive 'soothing' system, might keep the 'salience network' (also known as the midcingulo-insular network) in continuous alert mode, and this hyperactivation, in conjunction with other mechanisms, contributes to fibromyalgia. This proposed integrative model, which we term the Fibromyalgia: Imbalance of Threat and Soothing Systems (FITSS) model, should be viewed as a working hypothesis with limited supporting evidence available. We hope, however, that this model will shed new light on existing psychosocial and biological observations, and inspire future research to address the many gaps in our knowledge about fibromyalgia, ultimately stimulating the development of novel therapeutic interventions.

Genetic risk of AUDs and childhood impulsivity: Examining the role of parenting and family environment

This study examined the independent and interactive effects of genetic risk for alcohol use disorder (AUD), parenting behaviors, and family environment on childhood impulsivity. Data were drawn from White (n = 5,991), Black/African American (n = 1,693), and Hispanic/Latino (n = 2,118) youth who completed the baseline assessment (age 9-10) and had genotypic data available from the Adolescent Brain Cognitive Development Study. Participants completed questionnaires and provided saliva or blood samples for genotyping. Results indicated no significant main effects of AUD genome-wide polygenic scores (AUD-PRS) on childhood impulsivity as measured by the UPPS-P scale across racial/ethnic groups. In general, parental monitoring and parental acceptance were associated with lower impulsivity; family conflict was associated with higher impulsivity. There was an interaction effect between AUD-PRS and family conflict, such that family conflict exacerbated the association between AUD-PRS and positive urgency, only among Black/African American youth. This was the only significant interaction effect detected from a total of 45 tests (five impulsivity dimensions, three subsamples, and three family factors), and thus may be a false positive and needs to be replicated. These findings highlight the important role of parenting behaviors and family conflict in relation to impulsivity among children.

Neural Impacts of Stigma, Racism, and Discrimination

Racism is a chronic stressor fueled by stigma that can result in significant distress and dysfunction as well as negatively affect emotions, behavior, quality of life, and brain health. The effects of stigma and discrimination emerge early in life and have long-term consequences. In this review, we sought to use neuroscience research to describe how stigma, racism, and discrimination can impact brain and mental health. Societal stigmas may be encoded by associative fear learning and pattern completion networks, and experiences of racial discrimination may similarly affect threat-responsive regions and circuits. Race-related differences in brain function and structure supporting threat circuitry are largely attenuated when negative life experiences and discrimination are taken into account. Downstream, chronic activation of the hypothalamic-pituitary-adrenal axis and the sympathetic-adrenal-medullary axis in the context of discrimination and stigma can contribute to physical health disparities in minoritized and marginalized groups. Finally, we discuss models that provide a framework for interventions and societal-level strategies across ecologic systems to build resilience and foster posttraumatic growth.

Developmental Considerations for Understanding Perceptions and Impacts of Identity-Related Differences: Focusing on Adolescence

Biological psychiatry, similar to many other scientific fields, is grappling with the challenge of revising its practices with an eye toward promoting diversity, equity, and inclusivity. One arena in which much of this work will have significant impact is in developmental science generally and the study of adolescence specifically. Adolescence is a critical period during human development during which important social, neural, and cognitive maturation processes take place. It is also a time marked by risky behaviors and the onset of a range of mental disorders. Social and developmental research has provided insight into the cognitive and neural processes by which perceptions of identity-related differences emerge. Clinical research aimed at understanding how individuals from diverse backgrounds navigate the transition period of adolescence is critical for identifying the unique factors underlying risk and resilience in minoritized populations. Taking a developmental perspective, we review processes by which the brain understands group differences and how the developmental timing of this can influence antecedents of psychological distress. We close with a call to action, pointing to important understudied areas within the field of biological psychiatry that are critical for supporting mental health among diverse adolescent populations.

Moderating Effects of Religious Tourism Activities on Environmental Risk, Leisure Satisfaction, Physical and Mental Health and Well-Being among the Elderly in the Context of COVID-19

The purpose of this study is to explore whether religious tourism activities can create a safe leisure environment and improve the well-being of the elderly during the COVID-19 pandemic, with the participants in the Baishatun Mazu pilgrimage in Taiwan as the subjects of this study. A mixed research method was used. First, statistical software and the Pearson product-moment correlation coefficient were used to analyze the data. Then the respondents' opinions were collected. Finally, a multivariate analysis method was used to discuss the results of analysis. The findings showed that the elderly respondents thought that the epidemic prevention information and leisure space planning for the pilgrimage made them feel secure. The elderly believed the scenery, religious atmosphere, and commodities en route could reduce the perception of environmental risks to tourists, relieve pressure on the brain, and increase social opportunities. Therefore, the friendlier the leisure environment around the pilgrimage, the greater the leisure satisfaction among the elderly respondents. The happier the elderly felt, the less they considered the concentration of airborne contaminants, including viruses. The better their physical and mental health was, the less likely they were to want to ask for religious goods.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Symptom-Based Profiling and Multimodal Neuroimaging of a Large Preteenage Population Identifies Distinct Obsessive-Compulsive Disorder-like Subtypes With Neurocognitive Differences

BACKGROUND

Obsessive-compulsive disorder (OCD) is characterized by both internalizing (anxiety) and externalizing (compulsivity) symptoms. Currently, little is known about their interrelationships and their relative contributions to disease heterogeneity. Our goal is to resolve affective and cognitive symptom heterogeneity related to internalized and externalized symptom dimensions by determining subtypes of children with OCD symptoms, and to identify any corresponding neural differences.

METHODS

A total of 1269 children with OCD symptoms screened using the Child Behavior Checklist Obsessive-Compulsive Symptom scale and 3987 matched control subjects were obtained from the Adolescent Brain Cognitive Development (ABCD) Study. Consensus hierarchical clustering was used to cluster children with OCD symptoms into distinct subtypes. Ten neurocognitive task scores and 20 Child Behavior Checklist syndrome scales were used to characterize cognitive/behavioral differences. Gray matter volume, fractional anisotropy of major white matter fiber tracts, and functional connectivity among networks were used in case-control studies.

RESULTS

We identified two subgroups with contrasting patterns in internalized and externalized dimensions. Group 1 showed compulsive thoughts and repeated acts but relatively low anxiety symptoms, whereas group 2 exhibited higher anxiety and perfectionism and relatively low repetitive behavior. Only group 1 had significant cognitive impairments and gray matter volume reductions in the bilateral inferior parietal lobe, precentral gyrus, and precuneus gyrus, and had white matter tract fractional anisotropy reductions in the corticostriatal fasciculus.

CONCLUSIONS

Children with OCD symptoms are heterogeneous at the level of symptom clustering and its underlying neural basis. Two subgroups represent distinct patterns of externalizing and internalizing symptoms, suggesting that anxiety is not its major predisposing factor. These results may have implications for the nosology and treatment of preteenage OCD.

Reward sensitivity and internalizing symptoms during the transition to puberty: An examination of 9-and 10-year-olds in the ABCD Study

Early pubertal timing has been linked to increased risk for internalizing psychopathology in adolescents. Work in older adolescents and adults suggests that heightened reward sensitivity may buffer risk for internalizing symptoms. However, few studies have investigated these associations during the early transition to puberty, a window of vulnerability to mental health risk. In this preregistered study, we investigated the associations among pubertal timing, internalizing symptoms, and reward sensitivity in a large, population-based sample of 11,224 9-10 year-olds from the ABCD Study®. Using split-half analysis, we tested for within-sample replications of hypothesized effects across two age- and sex-matched subsets of the sample. Early pubertal timing was associated with higher internalizing symptoms in female and male participants across samples, with 9-10 year-olds in the mid-pubertal stage at the highest risk for internalizing symptoms. Additionally, early pubertal timing was robustly associated with greater self-reported reward sensitivity in both female and male participants. We observed inconsistent evidence for a moderating role of reward sensitivity across measurement domains (self-report, behavioral, and fMRI data), several of which differed by sex, but none of these interactions replicated across samples. Together, these findings provide unique insights into early indicators of risk for internalizing psychopathology during the transition to puberty in a large, population-based, demographically diverse sample of youth.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

Integrative analysis of genomic and exposomic influences on youth mental health

BACKGROUND

Understanding complex influences on mental health problems in young people is needed to inform early prevention strategies. Both genetic and environmental factors are known to influence youth mental health, but a more comprehensive picture of their interplay, including wide-ranging environmental exposures - that is, the exposome - is needed. We perform an integrative analysis of genomic and exposomic data in relation to internalizing and externalizing symptoms in a cohort of 4,314 unrelated youth from the Adolescent Brain and Cognitive Development (ABCD) Study.

METHODS

Using novel GREML-based approaches, we model the variance in internalizing and externalizing symptoms explained by additive and interactive influences from the genome (G) and modeled exposome (E) consisting of up to 133 variables at the family, peer, school, neighborhood, life event, and broader environmental levels, including genome-by-exposome (G × E) and exposome-by-exposome (E × E) effects.

RESULTS

A best-fitting integrative model with G, E, and G × E components explained 35% and 63% of variance in youth internalizing and externalizing symptoms, respectively. Youth in the top quintile of model-predicted risk accounted for the majority of individuals with clinically elevated symptoms at follow-up (60% for internalizing; 72% for externalizing). Of note, different domains of environmental exposures were most impactful for internalizing (life events) and externalizing (contextual including family, school, and peer-level factors) symptoms. In addition, variance explained by G × E contributions was substantially larger for externalizing (33%) than internalizing (13%) symptoms.

CONCLUSIONS

Advanced statistical genetic methods in a longitudinal cohort of youth can be leveraged to address fundamental questions about the role of 'nature and nurture' in developmental psychopathology.

Adolescent Mental Health and Family Economic Hardships: The Roles of Adverse Childhood Experiences and Family Conflict

Rising and economically disproportionate rates of adverse mental health outcomes among children and youth warrant research investigating the complex pathways stemming from socioeconomic status. While adverse childhood experiences (ACEs) have been considered a possible mechanism linking socioeconomic status (SES) and child and youth psychopathology in previous studies, less is understood about how family environments might condition these pathways. Using data from a longitudinal, multiple-wave study, the present study addresses this gap by examining the direct relationships between family economic status and youth internalizing and externalizing symptoms, if ACEs mediate these relationships, and if conflictual family environments moderate these direct and indirect relationships. The data were obtained from 5510 youth participants [mean age at baseline = 9.52 (SD = 0.50), 47.7% female, 2.1% Asian, 10.3% Black, 17.6% Hispanic, 9.8% Multiracial/Multiethnic, 60.2% White] and their caretakers from the baseline, 1-year, and 2-year follow up waves. Conditional process analysis assessed the direct, indirect, and moderated relationships in separate, equivalent models based on youth- versus caregiver-raters of ACEs and youth psychopathology to capture potential differences based on the rater. The results of both the youth- and caregiver-rated models indicated that lower family economic status directly predicted higher levels of externalizing symptoms, and ACEs indirectly accounted for higher levels of internalizing and externalizing symptoms. Additionally, family conflict moderated some, but not all, of these relationships. The study's findings highlight that lower family economic status and ACEs, directly and indirectly, contribute to early adolescent psychopathology, and conflictual family environments can further intensify these relationships. Implementing empirically supported policies and interventions that target ACEs and family environments may disrupt deleterious pathways between SES and youth psychopathology.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

Longitudinal Assessments of Neurocognitive Performance and Brain Structure Associated With Initiation of Tobacco Use in Children, 2016 to 2021

IMPORTANCE

The landscape of tobacco use is changing. However, information about the association between early-age tobacco use and cognitive performances is limited, especially for emerging tobacco products such as electronic cigarettes (e-cigarettes).

OBJECTIVE

To assess the association between early-age initiation of tobacco use and cognitive performances measured by the National Institutes of Health (NIH) Toolbox Cognitive Battery and to examine whether initiation is associated with differences in brain morphometry.

DESIGN, SETTING, AND PARTICIPANTS

This observational cohort study examined the longitudinal associations of initiation of tobacco use with neurocognition using multivariate linear mixed models. Children aged 9 to 10 years from 21 US sites were enrolled in wave 1 (October 1, 2016, to October 31, 2018 [n = 11 729]) and the 2-year follow-up (August 1, 2018, to January 31, 2021 [n = 10 081]) of the Adolescent Brain Cognitive Development (ABCD) Study.

EXPOSURES

Ever use (vs none) of any tobacco products at wave 1, including e-cigarettes, cigarettes, cigars, smokeless tobacco, hookah, pipes, and nicotine replacement.

MAIN OUTCOMES AND MEASURES

Neurocognition measured by the NIH Toolbox Cognition Battery and morphometric measures of brain structure and region of interest analysis for the cortex from structural magnetic resonance imaging.

RESULTS

Among 11 729 participants at wave 1 (mean [SE] age, 9.9 [0.6] years; 47.9% girls and 52.1% boys; 20.3% Hispanic; 14.9% non-Hispanic Black; and 52.1% non-Hispanic White), 116 children reported ever use of tobacco products. Controlling for confounders, tobacco ever users vs nonusers exhibited lower scores in the Picture Vocabulary Tests at wave 1 (b [SE] = -2.9 [0.6]; P < .001) and 2-year follow-up (b [SE] = -3.0 [0.7]; P < .001). The crystalized cognition composite score was lower among tobacco ever users than nonusers both at wave 1 (b [SE] = -2.4 [0.5]; P < .001) and 2-year follow-up (b [SE] = -2.7 [0.8]; P = .005). In structural magnetic resonance imaging, the whole-brain measures in cortical area and volume were significantly lower among tobacco users than nonusers, including cortical area (b [SE] = -5014.8 [1739.8] mm2; P = .004) at wave 1 and cortical volume at wave 1 (b [SE] = -174 621.0 [5857.7] mm3; P = .003) and follow-up (b [SE] = -21 790.8 [7043.9] mm3; P = .002). Further region of interest analysis revealed smaller cortical area and volume in multiple regions across frontal, parietal, and temporal lobes at both waves.

CONCLUSIONS AND RELEVANCE

In this cohort study, initiating tobacco use in late childhood was associated with inferior cognitive performance and reduced brain structure with sustained effects at 2-year follow-up. These findings suggest that youths vulnerable to e-cigarettes and tobacco products should be treated as a priority population in tobacco prevention.

Brain structural covariation linked to screen media activity and externalizing behaviors in children

Background and Aims

Screen media activity (SMA) may impact neurodevelopment in youth. Cross-sectionally, SMA has been linked to brain structural patterns including cortical thinning in children. However, it remains unclear whether specific brain structural co-variation patterns are related to SMA and other clinically relevant measures such as psychopathology, cognition and sleep in children.

Methods

Adolescent Brain Cognitive Development (ABCD) participants with useable baseline structural imaging (N = 10,691; 5,107 girls) were analyzed. We first used the Joint and Individual Variation Explained (JIVE) approach to identify cortical and subcortical covariation pattern(s) among a set of 221 brain features (i.e., surface area, thickness, or cortical and subcortical gray matter (GM) volumes). Then, the identified structural covariation pattern was used as a predictor in linear mixed-effect models to investigate its associations with SMA, psychopathology, and cognitive and sleep measures.

Results

A thalamus-prefrontal cortex (PFC)-brainstem structural co-variation pattern (circuit) was identified. The pattern suggests brainstem and bilateral thalamus proper GM volumes covary more strongly with GM volume and/or surface area in bilateral superior frontal gyral, rostral middle frontal, inferior parietal, and inferior temporal regions. This covariation pattern highly resembled one previously linked to alcohol use initiation prior to adulthood and was consistent in girls and boys. Subsequent regression analyses showed that this co-variation pattern associated with SMA (β = 0.107, P = 0.002) and externalizing psychopathology (β = 0.117, P = 0.002), respectively.

Discussion and Conclusions

Findings linking SMA-related structural covariation to externalizing psychopathology in youth resonate with prior studies of alcohol-use initiation and suggest a potential neurodevelopmental mechanism underlying addiction vulnerability.

Association of Cyberbullying Experiences and Perpetration With Suicidality in Early Adolescence

IMPORTANCE

Adolescent suicidality (ie, suicidal ideation or attempts) is a major public health concern. Cyberbullying experiences and perpetration have become increasingly prevalent and are associated with mental health burden, but their roles as independent suicidality risk factors remain unclear. Data are needed to clarify their contribution to teen suicidality to inform suicide prevention efforts.

OBJECTIVE

To examine whether cyberbullying experiences and perpetration are distinct stressors divergent from other forms of peer aggression experiences in their association with suicidality in early adolescence.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional analysis used data collected between July 2018 and January 2021 from the Adolescent Brain Cognitive Development (ABCD) study, a large, diverse sample of US children aged 10 to 13 years.

EXPOSURES

Youth reports of cyberbullying experiences or perpetration.

MAIN OUTCOMES AND MEASURES

The main outcome was youth-reported suicidality (past or present, as reported in the ABCD 2-year follow-up assessment). Covariates included demographics, established environmental risk and protective factors for youth suicidality, psychopathology, and experiences or perpetration of offline peer aggression.

RESULTS

A total of 10 414 ABCD participants were included in this study. Participants had a mean (SD) age of 12.0 (0.7) years and 4962 (47.6%) were female; 796 (7.6%) endorsed suicidality. A total of 930 (8.9%) reported experiencing cyberbullying and 96 (0.9%) reported perpetrating cyberbullying. Of the perpetrators, 66 (69.0%) also endorsed experiencing cyberbullying. Controlling for demographics, experiencing cyberbullying was associated with suicidality (odds ratio [OR], 4.2 [95% CI, 3.5-5.1]; P < .001), whereas perpetrating cyberbullying was not (OR, 1.3 [95% CI, 0.8-2.3]; P = .30). Experiencing cyberbullying remained associated with suicidality when accounting for negative life events, family conflict, parental monitoring, school environment, and racial and ethnic discrimination (OR, 2.5 [95% CI, 2.0-3.0]; P < .001) and when further covarying for internalizing and externalizing psychopathology (OR, 1.8 [95% CI, 1.4-2.4]; P < .001). Both being a target and being a perpetrator of offline peer aggression were associated with suicidality (OR, 1.5 [95% CI, 1.1-2.0] for both), controlling for all covariates described earlier. Cyberbullying experiences remained associated with suicidality (OR, 1.7 [95% CI, 1.3-2.2]; P < .001, controlling for all covariates) when included with offline peer aggression experiences and perpetration.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, experiencing-but not perpetrating-cyberbullying was associated with suicidality in early adolescence. This association was significant over and above other suicidality risk factors, including offline peer aggression experiences or perpetration. These findings can inform adolescent suicide prevention strategies, and they suggest that clinicians and educational staff working with this population should routinely evaluate for adolescents' experience with cyberbullying.