Characterizing the dimensional structure of early-life adversity in the Adolescent Brain Cognitive Development (ABCD) Study

A longitudinal study of the impact of childhood adversity dimensions on social and psychological factors and symptoms of psychosis, depression, and anxiety

The present study examined three empirically-derived childhood adversity dimensions as predictors of social, psychological, and symptom outcomes across three prospective assessments of a young adult sample. Participants were assessed five times over eight years with semi-structured interviews and questionnaires. The analyses used the dimensions underlying multiple subscales from well-established childhood adversity measures administered at the first two assessment waves (described in a previous report). Outcome data pertain to the last three assessment waves, with sample sizes ranging from 89 to 169. As hypothesized, the childhood adversity dimensions demonstrated overlapping and differential longitudinal associations with the outcomes. Deprivation predicted the negative (deficit-like) dimension of psychosis, while Threat and Intrafamilial Adversity predicted the positive (psychotic-like) dimension. Depression and anxiety symptoms were predicted by different childhood adversity dimensions over time. Furthermore, Threat predicted a smaller and less diverse social network, Intrafamilial Adversity predicted anxious attachment, and Deprivation predicted a smaller social network, anxious and avoidant attachment, perceived social support, and loneliness. The three adversity dimensions combined accounted for moderate to large proportions of variance in several outcomes. These results extend prior work by identifying associations of three meaningful dimensions of childhood adversity with different risk profiles across psychological, social, and psychopathological domains. The findings enhance our understanding of the impact of childhood adversity across young adulthood.

Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation

BACKGROUND

Different types of early-life adversity (ELA) have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge.

METHODS

This study investigates the neural correlates of 10 robust dimensions of ELA identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development Study. Brain age models were trained, validated, and tested separately on T1-weighted (n = 9524), diffusion tensor (n = 8834), and resting-state functional (n = 8233) magnetic resonance imaging data from two time points (mean age = 10.7 years, SD = 1.2, age range = 8.9-13.8 years).

RESULTS

Bayesian multilevel modeling supported distinct associations between different types of ELA exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support and lack of caregiver supervision, were associated with lower brain age gaps, i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socioeconomic disadvantage and neighborhood safety were associated with higher brain age gaps, i.e., older-looking brains.

CONCLUSIONS

The findings suggest that dimensions of ELA are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.

Food insecurity and adolescent impulsivity: The mediating role of functional connectivity in the context of family flexibility

Adolescent food insecurity is a salient adversity hypothesized to affect neural systems associated with increased impulsive behavior. Family environments shape how adverse experiences influence development. In this study, hypotheses were tested regarding the conjoint effects of food insecurity and family flexibility on impulsivity via alterations in connectivity between regions within the salience and central executive networks. Such alterations are reflected in resting-state functional connectivity (rsFC) metrics between the anterior insula (AI) and the middle frontal gyrus (MFG). Hypotheses were tested in a longitudinal moderated mediation model with two waves of data from 142 adolescents (Time 1 [T1] Mage = 12.89, SD = 0.85; Time 2 [T2] Mage = 15.01, SD = 1.07). Data on past-year household food insecurity, family flexibility, and rsFC were obtained at T1. Impulsivity was self-reported by the adolescent at T1 and T2. Findings revealed that high T1 left-to-left rsFC between the AI and MFG was associated with increased impulsivity at T2. The interaction of family flexibility and food insecurity was associated with AI and MFG rsFC. In the context of low family flexibility, food insecurity was linked to high levels of AI and MFG rsFC. Conversely, in the context of optimal family flexibility, food insecurity was associated with low levels of AI and MFG rsFC. Conditional indirect analysis suggests that the links among food insecurity, rsFC, and impulsive behavior depend on family flexibility. RESEARCH HIGHLIGHTS: Adolescent food insecurity was associated with anterior insula and middle frontal gyrus connectivity only at certain levels of family flexibility. High family flexibility attenuated the link between food insecurity and neural connectivity, while low levels of family flexibility increased this risk. High left anterior insula and left middle frontal gyrus connectivity was associated with increased impulsivity 1 year later.

How adverse childhood experiences get under the skin: A systematic review, integration and methodological discussion on threat and reward learning mechanisms

Adverse childhood experiences (ACEs) are a major risk factor for the development of multiple psychopathological conditions, but the mechanisms underlying this link are poorly understood. Associative learning encompasses key mechanisms through which individuals learn to link important environmental inputs to emotional and behavioral responses. ACEs may impact the normative maturation of associative learning processes, resulting in their enduring maladaptive expression manifesting in psychopathology. In this review, we lay out a systematic and methodological overview and integration of the available evidence of the proposed association between ACEs and threat and reward learning processes. We summarize results from a systematic literature search (following PRISMA guidelines) which yielded a total of 81 articles (threat: n=38, reward: n=43). Across the threat and reward learning fields, behaviorally, we observed a converging pattern of aberrant learning in individuals with a history of ACEs, independent of other sample characteristics, specific ACE types, and outcome measures. Specifically, blunted threat learning was reflected in reduced discrimination between threat and safety cues, primarily driven by diminished responding to conditioned threat cues. Furthermore, attenuated reward learning manifested in reduced accuracy and learning rate in tasks involving acquisition of reward contingencies. Importantly, this pattern emerged despite substantial heterogeneity in ACE assessment and operationalization across both fields. We conclude that blunted threat and reward learning may represent a mechanistic route by which ACEs may become physiologically and neurobiologically embedded and ultimately confer greater risk for psychopathology. In closing, we discuss potentially fruitful future directions for the research field, including methodological and ACE assessment considerations.

Threat, Emotion Dysregulation, and Parenting in a Clinical Sample of Children with Disruptive Behaviour

Early-life adversity is associated with the development of internalizing and externalizing problems in children. Despite this, there is a need to understand the mechanisms linking these experiences to psychopathology, especially in clinical samples. This cross-sectional study tested emotion dysregulation as a mechanism linking early-life threat to psychopathology in a clinical sample of children with disruptive behavior problems. We also explored parental positive reinforcement as a protective factor in these pathways. A clinical sample of 606 children aged 6-12 years, referred to a mental healthcare hospital, were included. Parent-reported child threat, and parent- and teacher-reported child emotion dysregulation and psychopathology, were collected. Path analysis was used to explore the mediating effect of emotion dysregulation in the relation between threat and psychopathology. The moderating effects of parental positive reinforcement were explored through moderated-mediation analyses. Emotion dysregulation partially mediated the association between threat and both internalizing (β = .18, P = .006) and externalizing (β = .19, P = .002) problems. Positive reinforcement did not buffer the association between threat and emotion dysregulation (β = .09, P = .62) or the association between emotion dysregulation and internalizing (β = - .003, P = .20) or externalizing (β = - .002, P = .35). Poor emotion regulation may be a transdiagnostic mechanism linking early-threat with internalizing and externalizing problems in clinic-referred children with disruptive behaviors. Factors aside from parental positive reinforcement should be explored as protective factors in these pathways, including those directly implicated in the purported mechanisms linking these factors over time.

Growing Up Together in Society (GUTS): A team science effort to predict societal trajectories in adolescence and young adulthood

Our society faces a great diversity of opportunities for youth. The 10-year Growing Up Together in Society (GUTS) program has the long-term goal to understand which combination of measures best predict societal trajectories, such as school success, mental health, well-being, and developing a sense of belonging in society. Our leading hypothesis is that self-regulation is key to how adolescents successfully navigate the demands of contemporary society. We aim to test these questions using socio-economic, questionnaire (including experience sampling methods), behavioral, brain (fMRI, sMRI, EEG), hormonal, and genetic measures in four large cohorts including adolescents and young adults. Two cohorts are designed as test and replication cohorts to test the developmental trajectory of self-regulation, including adolescents of different socioeconomic status thereby bridging individual, family, and societal perspectives. The third cohort consists of an entire social network to examine how neural and self-regulatory development influences and is influenced by whom adolescents and young adults choose to interact with. The fourth cohort includes youth with early signs of antisocial and delinquent behavior to understand patterns of societal development in individuals at the extreme ends of self-regulation and societal participation, and examines pathways into and out of delinquency. We will complement the newly collected cohorts with data from existing large-scale population-based and case-control cohorts. The study is embedded in a transdisciplinary approach that engages stakeholders throughout the design stage, with a strong focus on citizen science and youth participation in study design, data collection, and interpretation of results, to ensure optimal translation to youth in society.

Limitations of two time point data for understanding individual differences in longitudinal modeling - What can difference reveal about change?

Emerging neuroimaging studies investigating changes in the brain aim to collect sufficient data points to examine trajectories of change across key developmental periods. Yet, current studies are often constrained by the number of time points available now. We demonstrate that these constraints should be taken seriously and that studies with two time points should focus on particular questions (e.g., group-level or intervention effects), while complex questions of individual differences and investigations into causes and consequences of those differences should be deferred until additional time points can be incorporated into models of change. We generated underlying longitudinal data and fit models with 2, 3, 4, and 5 time points across 1000 samples. While fixed effects could be recovered on average even with few time points, recovery of individual differences was particularly poor for the two time point model, correlating at r = 0.41 with the true individual parameters - meaning these scores share only 16.8% of variance As expected, models with more time points recovered the growth parameter more accurately; yet parameter recovery for the three time point model was still low, correlating around r = 0.57. We argue that preliminary analyses on early subsets of time points in longitudinal analyses should focus on these average or group-level effects and that individual difference questions should be addressed in samples that maximize the number of time points available. We conclude with recommendations for researchers using early time point models, including ideas for preregistration, careful interpretation of 2 time point results, and treating longitudinal analyses as dynamic, where early findings are updated as additional information becomes available.

Leveraging multivariate approaches to advance the science of early-life adversity

Since the landmark Adverse Childhood Experiences (ACEs) study, adversity research has expanded to more precisely account for the multifaceted nature of adverse experiences. The complex data structures and interrelated nature of adversity data require robust multivariate statistical methods, and recent methodological and statistical innovations have facilitated advancements in research on childhood adversity. Here, we provide an overview of a subset of multivariate methods that we believe hold particular promise for advancing the field's understanding of early-life adversity, and discuss how these approaches can be practically applied to explore different research questions. This review covers data-driven or unsupervised approaches (including dimensionality reduction and person-centered clustering/subtype identification) as well as supervised/prediction-based approaches (including linear and tree-based models and neural networks). For each, we highlight studies that have effectively applied the method to provide novel insight into early-life adversity. Taken together, we hope this review serves as a resource to adversity researchers looking to expand upon the cumulative approach described in the original ACEs study, thereby advancing the field's understanding of the complexity of adversity and related developmental consequences.