Scientific Publications

Abstract

Background
Studies suggest that prosocial behavior, having high empathy and engaging in behaviors intended to benefit others, may predict mental health or vice versa; however, these findings have been mixed. The purpose of the current study was to examine the bidirectional relationships between prosocial behavior and dimensions of psychopathology in children.

Methods
The relationships between prosocial behavior and four dimensions of psychopathology (general psychopathology, internalizing symptoms, conduct problems, and attention-deficit/hyperactivity disorder symptoms) were examined longitudinally in children 9–12 years of age from the Adolescent Brain Cognitive Development Study (N = 9122). We used a random intercept cross-lagged panel model to distinguish between stable, trait-like (between-person) and time-dependent (within-person) fluctuations across a 24-month period.

Results
Between-person results revealed that prosocial behavior was negatively associated with general psychopathology and conduct problems while being positively associated with internalizing symptoms. Within-person results demonstrated that, out of four possible directional paths tested, one was significant. This path showed that greater general psychopathology and conduct problems at the first-year follow-up predicted fewer prosocial behaviors at the second-year follow-up, although the effect size was small. In contrast, prosocial behavior did not predict psychopathology dimensions for any year.

Conclusions
The results of this study suggest that prosocial behaviors have stable associations with psychopathology across preadolescence; however, evidence of a directional association in which psychopathology predicts fewer prosocial behaviors in the future was only modest.

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Brain networks are continuously modified throughout development, yet this plasticity can also make functional networks vulnerable to early life stress. Little is currently known about the effect of early life stress on the functional organization of the brain. The current study investigated the association between environmental stressors and network topology using data from the Adolescent Brain Cognitive Development (ABCD®) Study. Hierarchical modeling identified a general factor of environmental stress, representing the common variance across multiple stressors, as well as four subfactors including familial dynamics, interpersonal support, neighborhood SES deprivation, and urbanicity. Functional network topology metrics were obtained using graph theory at rest and during tasks of reward processing, inhibition, and affective working memory. The general factor of environmental stress was associated with less specialization of networks, represented by lower modularity at rest. Local metrics indicated that general environmental stress was also associated with less efficiency in the subcortical-cerebellar and visual networks while showing greater efficiency in the default mode network at rest. Subfactors of environmental stress were associated with differences in specialization and efficiency in select networks. The current study illustrates that a wide range of stressors in a child’s environment are associated with differences in brain network topology.

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Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n-back task-based and resting-state fMRI data from adults aged 22-35 years (task n = 896; rest n = 898). We applied connectome-based predictive modeling (CPM) to predict participants’ mean sleep duration from their functional connectivity patterns. Models trained and tested using 10-fold cross-validation predicted self-reported average sleep duration for the past month from n-back task and resting-state connectivity patterns. We replicated this finding in data from the 2-year follow-up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n-back task and resting-state fMRI for adolescents aged 11-12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10-fold cross-validation again predicted sleep duration from n-back task and resting-state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting-state functional brain connectivity patterns reflect sleep duration in youth and young adults.

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Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations.

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Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children-and captured individual differences in later recognition memory-but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA.

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When brain networks deviate from typical development, this is thought to contribute to varying forms of psychopathology. However, research has been limited by the reliance on discrete diagnostic categories that overlook the potential for psychological comorbidity and the dimensional nature of symptoms.

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A polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) has been found to be associated with ADHD in multiple studies, but also with many other dimensions of problems. Little is known, however, about the processes underlying these transdiagnostic associations. Using data from the baseline and 1-year follow-up assessments of 9- to 10-year-old children in the Adolescent Brain Cognitive Development™ (ABCD©) Study, associations were assessed between an ADHD PRS and both general and specific factors of psychological problems defined in bifactor modeling. Additionally, prospective mediated paths were tested from the ADHD PRS to dimensions of problems in the follow-up assessment through baseline measures of executive functioning (EF) and two facets of impulsivity: lower perseverance and greater impulsiveness in the presence of surgent positive emotions. Previous findings of modest but significant direct associations of the ADHD PRS with the general factor of psychological problems were replicated in both assessments in 4,483 children of European ancestry. In addition, significant statistical mediation was found from the ADHD PRS to the general factor, specific ADHD, and conduct problems in the follow-up assessment through each of the two facets of impulsivity. In contrast, EF did not statistically mediate associations between the ADHD PRS and psychological problems. These results suggest that polygenic risk transdiagnostically influences both psychological problems and facets of impulsivity, perhaps partly through indirect pathways via facets of impulsivity.

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Early-life stressors can adversely affect the developing brain. While hierarchical modeling has established the existence of a general factor of psychopathology, no studies have modeled a general factor of environmental stress and related this factor to brain development. Using a large sample of children from the ABCD (Adolescent Brain Cognitive Development) Study, the current study aimed to identify general and specific factors of environmental stress and test their associations with brain structure and psychopathology.

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Individual differences in children’s cognitive abilities impact life and health outcomes. What factors influence these individual differences during development? Here, we test whether children’s environments predict cognitive performance, independent of well-characterized socioeconomic effects. We analyzed data from 9002 9- to 10-year olds from the Adolescent Brain Cognitive Development Study, an ongoing longitudinal study with community samples across the United States. Using youth- and caregiver-report questionnaires and national database registries (e.g., neighborhood crime, walkability), we defined principal components summarizing children’s home, school, neighborhood, and cultural environments. In two independent samples (ns = 3475, 5527), environmental components explained unique variance in children’s general cognitive ability, executive functioning, and learning/memory abilities. Furthermore, increased neighborhood enrichment was associated with an attenuated relationship between sociodemographics and general cognitive abilities. Thus, the environment accounts for unique variance in cognitive performance in children and should be considered alongside sociodemographic factors to better understand brain functioning and behavior across development.

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The developing brain is marked by high plasticity, which can lead to vulnerability to early life stressors. Previous studies indicate that childhood maltreatment is associated with structural aberrations across a number of brain regions. However, prior work is limited by small sample sizes, heterogeneous age groups, the examination of one structure in isolation, the confounding of different types of early life stressors, and not accounting for socioeconomic status. These limitations may contribute to high variability across studies. The present study aimed to investigate how trauma is specifically associated with cortical thickness and gray matter volume (GMV) differences by leveraging a large sample of children (N = 9270) from the Adolescent Brain Cognitive Development Study (ABCD Study). A latent measure of trauma exposure was derived from DSM-5 traumatic events, and we related this measure of trauma to the brain using structural equation modeling. Trauma exposure was associated with thinner cortices in the bilateral superior frontal gyri and right caudal middle frontal gyrus (p-values < .001) as well as thicker cortices in the left isthmus cingulate and posterior cingulate (p-values ≤ .027), after controlling age, sex, and race/ethnicity. Furthermore, trauma exposure was associated with smaller GMV in the right amygdala and right putamen (p-values ≤ .048). Sensitivity analyses that controlled for income and parental education were largely consistent with the main findings for cortical thickness. These results suggest that trauma may be an important risk factor for structural aberrations, specifically for cortical thickness differences in frontal and cingulate regions in children.

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Childhood is an important time for the manifestation of psychopathology. Psychopathology is characterized by considerable comorbidity which is mirrored in the underlying neural correlates of psychopathology. Both common and dissociable variations in brain volume have been found across multiple mental disorders in adult and youth samples. However, the majority of these studies used samples with broad age ranges which may obscure developmental differences. The current study examines associations between regional gray matter volumes (GMV) and psychopathology in a large sample of children with a narrowly defined age range. We used data from 9607 children 9-10 years of age collected as part of the Adolescent Brain Cognitive Development Study (ABCD Study). A bifactor model identified a general psychopathology factor that reflects common variance across disorders and specific factors representing internalizing symptoms, ADHD symptoms, and conduct problems. Brain volume was acquired using 3T MRI. After correction for multiple testing, structural equation modeling revealed nearly global inverse associations between regional GMVs and general psychopathology and conduct problems, with associations also found for ADHD symptoms (p-values ≤ 0.048). Age, sex, and race were included as covariates. Sensitivity analyses including total GMV or intracranial volume (ICV) as covariates support this global association, as a large majority of region-specific results became nonsignificant. Sensitivity analyses including income, parental education, and medication use as additional covariates demonstrate largely convergent results. These findings suggest that globally smaller GMVs are a nonspecific risk factor for general psychopathology, and possibly for conduct problems and ADHD as well.

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Executive functions (EFs) are important partly because they are associated with risk for psychopathology and substance use problems. Because EFs have been linked to white matter microstructure, we tested the prediction that fractional anisotropy (FA) and mean diffusivity (MD) in white matter tracts are associated with EFs and dimensions of psychopathology in children younger than the age of widespread psychoactive substance use.

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[Correction Notice: An Erratum for this article was reported in Vol 129(7) of (see record 2020-72912-001). In the article (http://dx.doi.org/10.1037/abn0000601), an acknowledgment is missing from the author note. The missing acknowledgement is included in the erratum.] Psychopathology can be viewed as a hierarchy of correlated dimensions. Many studies have supported this conceptualization, but they have used alternative statistical models with differing interpretations. In bifactor models, every symptom loads on both the general factor and 1 specific factor (e.g., internalizing), which partitions the total explained variance in each symptom between these orthogonal factors. In second-order models, symptoms load on one of several correlated lower-order factors. These lower-order factors load on a second-order general factor, which is defined by the variance shared by the lower-order factors. Thus, the factors in second-order models are not orthogonal. Choosing between these valid statistical models depends on the hypothesis being tested. Because bifactor models define orthogonal phenotypes with distinct sources of variance, they are optimal for studies of shared and unique associations of the dimensions of psychopathology with external variables putatively relevant to etiology and mechanisms. Concerns have been raised, however, about the reliability of the orthogonal specific factors in bifactor models. We evaluated this concern using parent symptom ratings of 9-10 year olds in the ABCD Study. Psychometric indices indicated that all factors in both bifactor and second-order models exhibited at least adequate construct reliability and estimated replicability. The factors defined in bifactor and second-order models were highly to moderately correlated across models, but have different interpretations. All factors in both models demonstrated significant associations with external criterion variables of theoretical and clinical importance, but the interpretation of such associations in second-order models was ambiguous due to shared variance among factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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