Association of adverse prenatal exposure burden with child psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study

Considerations When Accounting for Race and Ethnicity in Studies of Poverty and Neurodevelopment

OBJECTIVE

Poverty and systemic racism are intertwined. Children of marginalized racial and ethnic identities experience higher levels of poverty and adverse psychiatric outcomes. Thus, in models of poverty and neurodevelopment, race and ethnicity, as proxies for exposure to systemic disadvantage, are regularly considered confounders. Recently, however, some researchers have claimed that using race and ethnicity as confounders is statistically dubious, and potentially socially damaging. Instead, they argue for the use of variables measuring other social determinants of health (SDoH). We explore this approach herein.

METHOD

Data are from 7,836 children 10 years of age in the Adolescent Brain Cognitive Development Study (ABCD Study). We fit mixed regression models for the association of household poverty measures with psychiatric symptoms, magnetic resonance imaging (MRI)-derived cortical measures, and cognition with and without (1) race and ethnicity adjustment, (2) poverty-by-race and ethnicity interaction terms, and (3) alternative SDoH variables. Propensity-based weights were used to calibrate the sample to key US demographics.

RESULTS

For psychiatric and cognitive outcomes, poverty-outcome relationships differed across racial and ethnic groups (interaction of poverty by race and ethnicity, p < .05). For MRI-derived outcomes, adjusting for race and ethnicity changed the estimate of the impact of poverty. Alternative SDoH adjustment could not fully account for the impact of race and ethnicity on the associations explored.

CONCLUSION

Poverty and both race and ethnicity combine to influence neurodevelopment. Results suggest that the effects of poverty are generally inconsistent across race and ethnicity, which supports prior research demonstrating the nonequivalence of SDoH indicators by race and ethnicity. Studies exploring these relationships should assess the interaction between poverty and race and ethnicity and/or should stratify when appropriate. Replacing race and ethnicity with alternative SDoH may induce bias.

Brain health Begins Before Birth (B4): A "learning" pregnancy and birth cohort study

Mental health disorders affect over 1 billion people worldwide, profoundly impacting individuals, families, and the global economy. Risk for psychopathology begins early in life, with the perinatal period representing a critical window of vulnerability. The Brain health Begins Before Birth (B4) Study at Massachusetts General Hospital (MGH) is a prospective birth cohort designed to identify modifiable risk and resilience factors influencing early brain development and child- and adult-onset psychopathology. This study aims to deeply phenotype prenatal exposures through maternal surveys administered at multiple time points during and after pregnancy; assess early neurodevelopmental outcomes through the first two years of life; and triangulate exposure and outcome data with underlying biological mechanisms. The B4 Study consists of structured, remote surveys covering psychosocial, environmental, and health-related factors throughout pregnancy and the first two years of life, supplemented by medical record review. By integrating risk and resilience factors into a dynamic learning cohort model, the B4 Study aims to advance the field of preventive psychiatry by identifying actionable pathways for early intervention, testing strategies in real-time, and ultimately shaping policies that promote lifelong mental health and wellbeing from the earliest stages of development.

Dimensions of perinatal and childhood adversities both merge and remain distinct

BACKGROUND

Perinatal and childhood periods are sensitive windows of development wherein adversity exposure can result in disadvantageous outcomes. Data-driven dimensional approaches that appreciate the co-occurrence of adversities allow for extending beyond specificity (individual adversities) and cumulative risk (non-specific summation of adversities) approaches to understand how the type and timing of adversities affect outcomes.

OBJECTIVE

With evolving recommendations on what should be important in adversity research, we sought to establish a data-driven framework that accounts for both type and timing of adversity by (1) replicating dimensions of childhood adversities, (2) determining whether perinatal adversities form unique dimensions and (3) identifying whether adversities during the perinatal and childhood periods overlap or remain distinct.

METHODS

Using 6815 9-10-year-olds from the baseline Adolescent Brain Cognitive Development (ABCD) study, mixed graphical models were fit independently to childhood adversities and perinatal adversities, and simultaneously to perinatal and childhood adversities, to model relationships among adversities.

RESULTS

Data-driven clustering approaches estimated dimensions of adversity within networks. Six dimensions of childhood adversities and five dimensions of perinatal adversities were observed. When considered simultaneously, dimensions of perinatal and childhood adversities both merged (e.g., parental circumstances during perinatal and socioeconomic status during childhood) and stayed independent (e.g., obstetric complications during perinatal and neglect during childhood) underscoring the importance of considering both type and timing when studying early life adversity.

CONCLUSIONS

These results highlight that it may be appropriate to study certain adversity dimensions independently, whereas for others considering the impact of timing and potential continuity in exposure is critical. Recommendations for adversity research are discussed.

Predicting adolescent psychopathology from early life factors: A machine learning tutorial

Objective

The successful implementation and interpretation of machine learning (ML) models in epidemiological studies can be challenging without an extensive programming background. We provide a didactic example of machine learning for risk prediction in this study by determining whether early life factors could be useful for predicting adolescent psychopathology.

Methods

In total, 9643 adolescents ages 9-10 from the Adolescent Brain and Cognitive Development (ABCD) Study were included in ML analysis to predict high Child Behavior Checklist (CBCL) scores (i.e., t-scores ≥ 60). ML models were constructed using a series of predictor combinations (prenatal, family history, sociodemographic) across 5 different algorithms. We assessed ML performance through sensitivity, specificity, F1-score, and area under the curve (AUC) metrics.

Results

A total of 1267 adolescents (13.1 %) were found to have high CBCL scores. The best performing algorithms were elastic net and gradient boosted trees. The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. The most important prenatal predictors were unplanned pregnancy, birth complications, and pregnancy complications.

Conclusion

Our results suggest that inclusion of prenatal, family history, and sociodemographic factors in ML models can generate moderately accurate predictions of adolescent psychopathology. Issues associated with model overfitting, hyperparameter tuning, and system seed setting should be considered throughout model training, testing, and validation. Future early risk predictions models may improve with the inclusion of additional relevant covariates.

Longitudinal effects of prenatal substance use and environmental stressors on executive functioning in low-income African American adolescents: A latent growth modeling analysis

Adverse prenatal substance use and environmental stressors have been linked to prefrontal cortex (PFC) impairments, the brain region that regulates executive functioning. Executive functions (e.g., inhibitory control, working memory, and cognitive flexibility) are crucial for sophisticated cognitive activities throughout child and adolescent development. There is little research on how prenatal substance use and environmental stressors longitudinally program executive functioning in children over time. We investigated the associations between prenatal/environmental stressors (i.e., maternal prenatal substance use, maternal-fetal bonding, and neighborhood disorganization) and executive function performance among low-income African American youth from age 6 until age 18. Analyses were based on four waves of data collected between 1994 and 2014 in the Memphis New Mothers Study, a longitudinal randomized controlled trial that was an intervention during pregnancy and the first two years of the child's life in low-SES women and their first-born children. Mothers and their children were followed longitudinally through 18 years post-childbirth. Prenatal substance use (e.g., prenatal smoke, alcohol, and drug use) and environmental stressor (e.g., food environment, maternal-fetal bonding and neighborhood disorganizations) evaluations were gathered from mothers and children prenatally and postnatally before the age of 4.5 years. Executive function was assessed using the Child Behavior Checklist for impulsivity and inattention, while the coding subscale of the Wechsler Intelligence Scale for Children-Third Edition, the reading recognition subtest of the Peabody Individual Achievement Test, and the digit span subtest of the Wechsler Adult Intelligence Scale were employed to assess working memory at three time periods (6, 12, and 18 years). Covariate-adjusted latent growth models estimated the associations between prenatal substance use and environmental stressors and changes in executive functioning over three time points. Prenatal smoking and alcohol use were associated with changes in impulsivity scores over 12 years. Prenatal alcohol use predicted higher inattention at baseline and a slower rate of change from ages 6 to 18. Neighborhood disorganization at ages 6 and 18 predicted higher inattention and lower working memory in youth at age 18, respectively. Our findings underscore the long-term impact of prenatal substance use exposures and neighborhood environments on cognitive development and highlight the importance of early interventions to mitigate these effects.

Pregnancy complications and childhood mental health: is the association modified by sex or adverse social circumstances? Findings from the 'growing up in Ireland' national infant cohort study

Specific pregnancy complications, socioeconomic position and sex have all been independently associated with child mental health outcomes, but their combined effects remain unclear. We examined whether total number of complications experienced in the pregnancy associated with mental health at 5 and 9-years, and whether this varied by sex or adverse social circumstances. Pregnancy complications were self-reported at 9-months post-natally from a list of 16 complications. Parents completed the Strengths and Difficulties Questionnaire (SDQ) when their child was 5 and 9-years. The primary outcome was the SDQ-total and scoring in the clinical range (> 16) was a secondary outcome. We applied generalized linear mixed models to a large nationally representative Irish cohort (GUI; n = 11,134). Analyses were adjusted for sex, adverse social circumstances (at 9-months), and gestational smoking. We included an interaction term between pregnancy complications and each variable respectively in separate models to examine if associations varied by sex or adverse circumstances.After controlling for covariates, total complications associated with mental health at 5 and 9-years. Each additional pregnancy complication conferred a 10% higher total-SDQ score (exponentiated co-efficient 1.10 [95%CI 1.06-1.14], 1.20 [1.15-1.26], 1.20 [1.12-1.29] and 1.34 [1.21-1.48] for 1, 2, 3 and 4 + complications respectively). For the dichotomised outcome, generally increasing odds for clinical levels of mental health difficulties were observed (OR 1complication = 1.89, 95%CI [1.37-2.59]; OR 2complications = 2.31, 95%CI [1.53-3.50]; OR 3complications = 1.77, 95%CI [0.89-3.52]; OR 4 + complications = 6.88, 95%CI [3.29-14.40]). Females had significantly lower odds of exhibiting clinically significant mental health difficulties than males (OR = 0.43, 95%CI[0.32-0.57]).There was no evidence that the association between pregnancy complications and child's mental health varied by sex or social circumstances at 5 or 9-years. Males exposed to numerous pregnancy complications in the context of adverse social circumstances had the highest predicted probability of having mental health difficulties in middle childhood.

Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder

An individual's risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized a network control theory approach to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naïve youth from the Adolescent Brain Cognitive Development Study. We summarize brain dynamics by calculating transition energy, which probes the ease with which a whole brain, region or network drives the brain towards a specific spatial pattern of activation (i.e., brain state). Our findings reveal that a family history of SUD is associated with alterations in the brain's dynamics wherein: i) independent of sex, certain regions' transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males and decrease efficiency of top-down inhibitory control in females. This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Examining the association between prenatal and perinatal adversity and the psychotic experiences in childhood

BACKGROUND

Prenatal and perinatal complications are established risk factors for psychotic disorder, but far less is known about these measures and psychotic experiences (PEs). We investigated the longitudinal effect of prenatal risk factors (maternal behavior, medication complications) and perinatal risk factors (birth weight, medical complications) on frequency of PEs. We also examined the cumulative risk of prenatal/perinatal risk factors, and differences between transient PE, persistent PE, and controls.

METHODS

The Adolescent Brain Cognitive Development study is a large child cohort (age 9-10 at baseline; n = 11 872 with PE data). PEs were measured longitudinally using the Prodromal Questionnaire-Brief, Child version, and included only if reported as distressing. Mixed-effects models were used for analysis, controlling for random effects, and a substantial number of fixed-effects covariates.

RESULTS

Urinary tract infection (β = 0.11, 95% confidence interval [CI] 0.03-0.19) and severe anemia (β = 0.18, 95% CI 0.07-0.29) increased frequency of distressing PEs in childhood. Number of prenatal complications increased frequency of PEs (β = 0.03, 95% CI 0.01-0.06) and risk of persistent PEs (odds ratio [OR] = 1.08, 95% CI 1.01-1.15). Maternal smoking was associated with an increased frequency of PEs (β = 0.11, 95% CI 0.04-0.18) and persistent PEs (OR = 1.31, 95% CI 1.04-1.66). Maternal substance use was a risk factor for a 48% increased risk of persistent PEs (OR = 1.48, 95% CI 1.08-2.01). Perinatal complications showed no effect on PEs.

CONCLUSIONS

This study provides evidence that certain prenatal medical complications (severe nausea, severe anemia), cumulative number of prenatal medical complications, and maternal behaviors (smoking during pregnancy), increased frequency of distressing PEs in childhood. Maternal smoking and substance use, as well as cumulative number of prenatal complications increased risk of persistent PEs.

Predicting childhood ADHD-linked symptoms from prenatal and perinatal data in the ABCD cohort

This study investigates the capacity of pre/perinatal factors to predict attention-deficit/hyperactivity disorder (ADHD) symptoms in childhood. It also explores whether predictive accuracy of a pre/perinatal model varies for different groups in the population. We used the ABCD (Adolescent Brain Cognitive Development) cohort from the United States (N = 9975). Pre/perinatal information and the Child Behavior Checklist were reported by the parent when the child was aged 9-10. Forty variables which are generally known by birth were input as potential predictors including maternal substance-use, obstetric complications and child demographics. Elastic net regression with 5-fold validation was performed, and subsequently stratified by sex, race/ethnicity, household income and parental psychopathology. Seventeen pre/perinatal variables were identified as robust predictors of ADHD symptoms in this cohort. The model explained just 8.13% of the variance in ADHD symptoms on average (95% CI = 5.6%-11.5%). Predictive accuracy of the model varied significantly by subgroup, particularly across income groups, and several pre/perinatal factors appeared to be sex-specific. Results suggest we may be able to predict childhood ADHD symptoms with modest accuracy from birth. This study needs to be replicated using prospectively measured pre/perinatal data.

Traumatic brain injury, working memory-related neural processing, and alcohol experimentation behaviors in youth from the ABCD cohort

Adolescent traumatic brain injury (TBI) has long-term effects on brain functioning and behavior, impacting neural activity under cognitive load, especially in the reward network. Adolescent TBI is also linked to risk-taking behaviors including alcohol misuse. It remains unclear how TBI and neural functioning interact to predict alcohol experimentation during adolescence. Using Adolescent Brain Cognitive Development (ABCD) study data, this project examined if TBI at ages 9-10 predicts increased odds of alcohol sipping at ages 11-13 and if this association is moderated by neural activity during the Emotional EN-Back working memory task at ages 11-13. Logistic regression analyses showed that neural activity in regions of the fronto-basal ganglia network predicted increased odds of sipping alcohol by ages 11-13 (p < .05). TBI and left frontal pole activity interacted to predict alcohol sipping (OR = 0.507, 95% CI [0.303 - 0.846], p = .009) - increased activity predicted decreased odds of alcohol sipping for those with a TBI (OR = 0.516, 95% CI [0.314 - 0.850], p = .009), but not for those without (OR = 0.971, 95% CI [0.931 -1.012], p = .159). These findings suggest that for youth with a TBI, increased BOLD activity in the frontal pole, underlying working memory, may be uniquely protective against the early initiation of alcohol experimentation. Future work will examine TBI and alcohol misuse in the ABCD cohort across more time points and the impact of personality traits such as impulsivity on these associations.

Development and validation of a risk score model for predicting autism based on pre- and perinatal factors

Background

The use of pre- and perinatal risk factors as predictive factors may lower the age limit for reliable autism prediction. The objective of this study was to develop a clinical model based on these risk factors to predict autism.

Methods

A stepwise logistic regression analysis was conducted to explore the relationships between 28 candidate risk factors and autism risk among 615 Han Chinese children with autism and 615 unrelated typically developing children. The significant factors were subsequently used to create a clinical risk score model. A chi-square automatic interaction detector (CHAID) decision tree was used to validate the selected predictors included in the model. The predictive performance of the model was evaluated by an independent cohort.

Results

Five factors (pregnancy influenza-like illness, pregnancy stressors, maternal allergic/autoimmune disease, cesarean section, and hypoxia) were found to be significantly associated with autism risk. A receiver operating characteristic (ROC) curve indicated that the risk score model had good discrimination ability for autism, with an area under the curve (AUC) of 0.711 (95% CI=0.679-0.744); in the external validation cohort, the model showed slightly worse but overall similar predictive performance. Further subgroup analysis indicated that a higher risk score was associated with more behavioral problems. The risk score also exhibited robustness in a subgroup analysis of patients with mild autism.

Conclusion

This risk score model could lower the age limit for autism prediction with good discrimination performance, and it has unique advantages in clinical application.

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.

Cumulative environmental risk in early life is associated with mental disorders in childhood

BACKGROUND

No single environmental factor is a necessary or sufficient cause of mental disorder; multifactorial and transdiagnostic approaches are needed to understand the impact of the environment on the development of mental disorders across the life course.

METHOD

Using linked multi-agency administrative data for 71 932 children from the New South Wales Child Developmental Study, using logistic regression, we examined associations between 16 environmental risk factors in early life (prenatal period to <6 years of age) and later diagnoses of mental disorder recorded in health service data (from age 6 to 13 years), both individually and summed as an environmental risk score (ERS).

RESULTS

The ERS was associated with all types of mental disorder diagnoses in a dose-response fashion, such that 2.8% of children with no exposure to any of the environmental factors (ERS = 0), compared to 18.3% of children with an ERS of 8 or more indicating exposure to 8 or more environmental factors (ERS ⩾ 8), had been diagnosed with any type of mental disorder up to age 13-14 years. Thirteen of the 16 environmental factors measured (including prenatal factors, neighbourhood characteristics and more proximal experiences of trauma or neglect) were positively associated with at least one category of mental disorder.

CONCLUSION

Exposure to cumulative environmental risk factors in early life is associated with an increased likelihood of presenting to health services in childhood for any kind of mental disorder. In many instances, these factors are preventable or capable of mitigation by appropriate public policy settings.

Parental drug use disorders and youth psychopathology: Meta-analytic review

Parental drug use disorders (PDUDs) represent a highly prevalent risk factor for youth's development of psychological and substance misuse. However, most research on associations between parental substance use and child mental health focuses on composites of parental drug, alcohol, and tobacco use. PDUDs are associated with a range of legal, health, and environmental risks that make them substantially distinct from tobacco and alcohol misuse, yet associations between PDUDs and youth psychopathology symptoms have yet to be assessed quantitatively using meta-analytic techniques. Accordingly, the present meta-analysis assessed the association between PDUDs and youth's internalizing, externalizing, substance use, and total psychological problems across 30 studies (N = 8433). Meta-analytic findings showed that PDUDs were associated with greater substance use and total psychological problems in youth. Across studies, PDUDs were not associated with broad dimensions of youth internalizing and externalizing symptoms but demonstrated a positive relation with youth ADHD and conduct disorder symptoms. There were significant moderation effects for study quality, symptom informant, and child age, where the association between PDUDs and child symptoms of psychopathology was stronger for older youth, in higher quality studies, and studies using joint parent-child symptom informants. Taken together, the meta-analytic findings suggest that PDUDs present a significant risk factor for youth. Future research targeting the relation between parental drug use and youth psychopathology is warranted for prevention and intervention efforts. Implication of findings, mechanisms of interest, and an agenda for future research are discussed.

Sex-specific susceptibility to psychotic-like states provoked by prenatal THC exposure: Reversal by pregnenolone

Sociocultural attitudes towards cannabis legalization contribute to the common misconception that it is a relatively safe drug and its use during pregnancy poses no risk to the fetus. However, longitudinal studies demonstrate that maternal cannabis exposure results in adverse outcomes in the offspring, with a heightened risk for developing psychopathology. One of the most reported psychiatric outcomes is the proneness to psychotic-like experiences during childhood. How exposure to cannabis during gestation increases psychosis susceptibility in children and adolescents remains elusive. Preclinical research has indicated that in utero exposure to the major psychoactive component of cannabis, delta-9-tetrahydrocannabinol (THC), deranges brain developmental trajectories towards vulnerable psychotic-like endophenotypes later in life. Here, we present how prenatal THC exposure (PCE) deregulates mesolimbic dopamine development predisposing the offspring to schizophrenia-relevant phenotypes, exclusively when exposed to environmental challenges, such as stress or THC. Detrimental effects of PCE are sex-specific because female offspring do not display psychotic-like outcomes upon exposure to these challenges. Moreover, we present how pregnenolone, a neurosteroid that showed beneficial properties on the effects elicited by cannabis intoxication, normalizes mesolimbic dopamine function and rescues psychotic-like phenotypes. We, therefore, suggest this neurosteroid as a safe "disease-modifying" aid to prevent the onset of psychoses in vulnerable individuals. Our findings corroborate clinical evidence and highlight the relevance of early diagnostic screening and preventative strategies for young individuals at risk for mental diseases, such as male PCE offspring.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

The impact of prenatal alcohol and/or tobacco exposure on brain structure in a large sample of children from a South African birth cohort

BACKGROUND

Neuroimaging studies have emphasized the impact of prenatal alcohol exposure (PAE) on brain development, traditionally in heavily exposed participants. However, less is known about how naturally occurring community patterns of PAE (including light to moderate exposure) affect brain development, particularly in consideration of commonly occurring concurrent impacts of prenatal tobacco exposure (PTE).

METHODS

Three hundred thirty-two children (ages 8 to 12) living in South Africa's Cape Flats townships underwent structural magnetic resonance imaging. During pregnancy, their mothers reported alcohol and tobacco use, which was used to evaluate PAE and PTE effects on their children's brain structure. Analyses involved the main effects of PAE and PTE (and their interaction) and the effects of PAE and PTE quantity on cortical thickness, surface area, and volume.

RESULTS

After false-discovery rate (FDR) correction, PAE was associated with thinner left parahippocampal cortices, while PTE was associated with smaller cortical surface area in the bilateral pericalcarine, left lateral orbitofrontal, right posterior cingulate, right rostral anterior cingulate, left caudal middle frontal, and right caudal anterior cingulate gyri. There were no PAE × PTE interactions nor any associations of PAE and PTE exposure on volumetrics that survived FDR correction.

CONCLUSION

PAE was associated with reduction in the structure of the medial temporal lobe, a brain region critical for learning and memory. PTE had stronger and broader associations, including with regions associated with executive function, reward processing, and emotional regulation, potentially reflecting continued postnatal exposure to tobacco (i.e., second-hand smoke exposure). These differential effects are discussed with respect to reduced PAE quantity in our exposed group versus prior studies within this geographical location, the deep poverty in which participants live, and the consequences of apartheid and racially and economically driven payment practices that contributed to heavy drinking in the region. Longer-term follow-up is needed to determine potential environmental and other moderators of the brain findings here and assess the extent to which they endure over time.

Prediction of the trajectories of depressive symptoms among children in the adolescent brain cognitive development (ABCD) study using machine learning approach

BACKGROUND

Depression often first emerges during adolescence and evidence shows that the long-term patterns of depressive symptoms over time are heterogeneous. It is meaningful to predict the trajectory of depressive symptoms in adolescents to find early intervention targets.

METHODS

Based on the Adolescent Brain Cognitive Development Study, we included 4962 participants aged 9-10 who were followed-up for 2 years. Trajectories of depressive symptoms were identified by Latent Class Growth Analyses (LCGA). Four types of machine learning models were built to predict the identified trajectories and to obtain variables with predictive value based on the best performance model.

RESULTS

Of all participants, 536 (10.80%) were classified as increasing, 269 (5.42%) as persistently high, 433 (8.73%) as decreasing, and 3724 (75.05%) as persistently low by LCGA. Gradient Boosting Machine (GBM) model got the highest discriminant performance. Sleep quality, parental emotional state and family financial adversities were the most important predictors and three resting state functional magnetic resonance imaging functional connectivity data were also helpful to distinguish trajectories.

LIMITATION

We only have depressive symptom scores at three time points. Some valuable predictors are not specific to depression. External validation is an important next step. These predictors should not be interpreted as etiology and some variables were reported by parents/caregivers.

CONCLUSION

Using GBM combined with baseline characteristics, the trajectories of depressive symptoms with two years among adolescents aged 9-10 years can be well predicted, which might further facilitate the identification of adolescents at high risk of depressive symptoms and development of effective early interventions.

Estimating the association between exposome and psychosis as well as general psychopathology: results from the ABCD Study

Background

The exposome comprises all nongenetic factors an individual is exposed to across their lifespan. Research suggests that exposomic vulnerability for schizophrenia is associated not only with psychosis but also, to a degree, with general psychopathology. Here, we investigated to what degree exposome factors are associated with psychosis and general psychopathology.

Methods

Data were retrieved from the 1-year follow-up assessment of a large U.S. adolescent sample (n = 11,235), the Adolescent Brain Cognitive Development (ABCD) Study. Iterative factor analyses of environmental exposures (n = 798) allowed calculation of 6 exposome factors: household adversity, neighborhood environment, day-to-day experiences, state-level environment, family values, pregnancy/birth complications. Bifactor modeling of clinical symptoms (n = 93) allowed calculation of a general psychopathology factor (p-factor) and 6 subdomains, including a psychosis subdomain. We applied linear regression analyses to estimate the association of exposome factors with the p-factor and psychosis subdomain, respectively.

Results

Individual analyses showed that 5 exposome factors were significantly associated with the p-factor after multiple-comparison correction. In the mutually adjusted model, all exposome factors were significantly associated with the p-factor. Psychosis was particularly associated with 3 exposome factors, with the mutually adjusted model yielding the following results: household adversity (β = 0.04, 95% CI, 0.01 to 0.07), day-to-day experiences (β = 0.10, 95% CI, 0.08 to 0.12), and pregnancy/birth complications (β = 0.03, 95% CI, 0.01 to 0.05).

Conclusions

Our findings demonstrate that multifaceted environmental background is associated with mental disorders. Psychosis was particularly associated with prenatal, perinatal, and childhood (household and school) adversities, although these exposome domains were also associated with psychopathology. The exposome approach can help understand neurodevelopmental psychopathology.

Association Between Measures Derived From Children's Primary Exfoliated Teeth and Psychopathology Symptoms: Results From a Community-Based Study

Mental disorders are among the most disabling health conditions globally. However, there remains a lack of valid, reliable, noninvasive, and inexpensive biomarkers to identify (at an early age) people who are at the greatest risk of experiencing a future mental health condition. Exfoliated primary teeth, when used in combination with established and emerging tools (e.g., family history, imaging, genetics, epigenetics), may provide important additional insights about vulnerability to mental illness. Teeth are especially promising because they develop in parallel with the brain and maintain a permanent record of environmental insults occurring during prenatal and perinatal development. Despite their potential, few empirical studies have investigated features of exfoliated teeth in relation to mental health. Here, we used micro-CT imaging to test the hypothesis that measures derived from exfoliated primary incisors associated with psychopathology symptoms in a community-based sample of children (n = 37). We found that enamel volume (β = −0.77, 95% CI, −1.35 to −0.18, P = 0.01) had large negative associations with internalizing symptoms, and enamel mineral density (β = 0.77, 95% CI, 0.18–1.35, P = 0.01) had large positive associations with internalizing behavioral symptoms, even after stringent control for multiple testing. Pulp volume (β = −0.50, 95% CI, −0.90 to −0.09, P = 0.02) had a moderately-large negative association with externalizing behavioral symptoms, though these associations did not survive multiple testing correction. These results support the ongoing investigation of teeth as potential novel biomarkers of mental health risk.

Modeling environment through a general exposome factor in two independent adolescent cohorts

Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.