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The primary aim of this project is to examine shared and unique components of executive functioning (EF) as predictors of changes in externalizing behavior over time. Despite research examining the associations between EF and externalizing behavior in youth, several gaps remain in the literature. First, prior research has relied on small or non-representative samples, many of which were clinical, limiting the generalizability of findings. This study addresses this gap by utilizing a large, nationally representative sample from the Adolescent Brain Cognitive Development (ABCD) study. Additionally, while prior research often examines EF as a unitary construct (e.g., summed composite) or focuses on one specific individual component (e.g., inhibition), this study aims to explore associations between shared (latent EF factor) and unique (inhibition, working memory, cognitive flexibility) components of EF and trajectories of externalizing behavior. This approach allows for a more nuanced understanding of how EF may contribute to changes in externalizing behaviors over time. Moreover, this study aims to examine whether shared and unique components of EF show differential associations with subdimensions of externalizing behavior. Given the noted heterogeneity in externalizing behavior, we aim to utilize a robust and longitudinally invariant factor structure of externalizing behavior, which includes three specific subdimensions: conduct problems, irritability, and neurodevelopmental problems, that was validated using ABCD sample (Vize et al., in press). This approach addresses a significant gap in the literature and examines heterogeneity within externalizing behaviors, which has the potential to elucidate how components of EF may differentially relate to distinct dimensions of externalizing behavior in a large, representative community sample.

Abstract: Magnetic resonance imaging (MRI) is a vital tool for the study of brain structure and function. It is increasingly being used in individual differences research to examine brain-behaviour associations. Prior work has demonstrated low test-retest stability of functional MRI measures, highlighting the need to examine the longitudinal stability (test-retest reliability across long timespans) of MRI measures across brain regions and imaging metrics, particularly in adolescence. In this study, we examined the longitudinal stability of grey matter measures (cortical thickness, surface area, and volume) across brain regions, and testing sites in the Adolescent Brain Cognitive Development (ABCD) study release v4.0. Longitudinal stability ICC estimates ranged from 0 to .98, depending on the measure, parcellation, and brain region. We used Intra-Class Effect Decomposition (ICED) to estimate between-subjects variance and error variance, and assess the relative contribution of each across brain regions and testing sites on longitudinal stability. In further exploratory analyses we examined the influence of parcellation used (Desikan-Killiany-Tourville and Destrieux) on longitudinal stability. Our results highlight meaningful heterogeneity in longitudinal stability across brain regions, structural measures (cortical thickness in particular), parcellations, and ABCD testing sites. Differences in longitudinal stability across brain regions were largely driven by between-subjects variance, whereas differences in longitudinal stability across testing sites was largely driven by differences in error variance. We argue that investigations such as this are essential to capture patterns of longitudinal stability heterogeneity that would otherwise go undiagnosed. Such improved understanding allows the field to more accurately interpret results, compare effect sizes, and plan more powerful studies.

This study aims to explore the relationships between modifiable parental factors, including parental warmth, monitoring, family conflict and parent emotion health, and adolescent sleep patterns in the long-term. Growing research links parenting to the development and regulation of sleep during adolescence, a sensitive period of change (Brand et al., 2009; Meijer et al., 2016). However, most studies in this area have relied on cross-sectional designs and self-report measures of sleep. This study will leverage the Adolescent Brain Cognitive Development (ABCD) study’s longitudinal dataset following over 11,000 youth to assess prospective relationships between parenting behaviors and adolescent sleep outcomes. In a recent systematic and meta-analytic review, Khor et al. (2021) examined a range of modifiable parental factors in relation to sleep in adolescents, identifying several parental factors associated with adolescent sleep outcomes. The review identified parental warmth, monitoring, family conflict, and parent emotional health as having an "emerging" level of evidence for impacts on sleep quality and, to some extent, duration and chronotype. The systematic review demonstrated that nurturing parenting and supportive family environments marked by warmth, active monitoring and low family conflict and parent emotion distress provide a supportive foundation for adolescents to develop better sleep outcomes as they mature. However, before such strategies can be implemented into clinical practice, the authors recommended further research on these parental factors using prospective, longitudinal studies, as well as the examination of under-researched sleep domains such as chronotype and sleep variability. Further, given marked heterogeneity in study methodologies and the types of parenting and sleep measures examined, further large, prospective studies examining a range of parenting behaviors and sleep domains in the same study are warranted. While previous research has established links between parental factors and adolescent sleep, the underlying mechanisms remain poorly understood. One conceptual model (Erath & Tu, 2011) proposes that parenting behaviors influence children's sleep through the modulation of self-regulation, defined as an individual's capacity to understand and manage their emotions and behaviors. According to the model, effective parenting factors enhance a child's ability to self-regulate emotions and behaviors, ultimately benefiting their sleep outcomes. This study will examine whether and how emotion regulation difficulties, as assessed by the Difficulties in Emotion Regulation Scale, may mediate the association between modifiable parental factors (e.g. warmth, monitoring) and aspects of adolescent sleep. Specifically, we aim to test a conceptual model in which parental influences are indirectly related to sleep patterns through their impact on adolescents' self-regulatory abilities in emotion domains. Positive parenting behaviours can model and enhance emotion regulation in children (Morris et al., 2017), which relates to better sleep regulation in adolescents over time (Palmer et al., 2018). In addition to emotion regulation, screen use is another pertinent factor for examining pathways from parenting to adolescent sleep. Adolescence is a developmental period characterized by significant changes in sleep patterns and preferences, as well as increased use of digital technologies (Hirshkowitz et al., 2015; Twenge et al., 2018). Different parental factors have been associated with different levels of screen time in children and adolescents (Geurts et al., 2022; Langer et al., 2014). For example, parental monitoring has been shown to be the strongest protective factor associated with lower digital screen time in adolescents aged 11-15, followed by parental care (Faltýnková et al., 2020; Veldhuis et al., 2014). Beyond the immediate parenting practices, the broader parenting and family environment have also been shown to contribute to children’s screen use, although the evidence is limited. For example, maternal distress and family environments marked by conflict have been linked to greater screen use by youth (Hoyos Cillero & Jago, 2010; Lampard et al., 2013; McDaniel & Coyne, 2016). In turn, screen use has been linked to a range of general sleep problems such as insomnia, lower sleep quality, and shorter sleep duration (Lemola et al., 2015; Oka et al., 2008; Twenge et al., 2017). Although emotion regulation abilities and screen-based activities have been separately linked to a range of parenting factors and adolescent sleep outcomes, the mediating roles of these factors have received limited examination. One cross-sectional study found that higher parental involvement was indirectly related to longer sleep duration among adolescents, with this association mediated through less bedtime media use (Leonard & Khurana, 2022). However, no research to our knowledge has prospectively assessed emotion regulation as a potential mediating mechanism through which certain parental factors may influence multiple domains of sleep across adolescence. Given the critical developmental importance of parenting for cultivating both emotion regulation skills and healthy sleep habits during this period, further longitudinal work is needed to disentangle the interrelationships between these modifiable parental factors and sleep outcomes as youth transition through early-to-mid adolescence. Prior work has found sex differences in both sleep patterns and correlates during adolescence (Organek et al., 2015). For example, females tend to report poorer overall sleep quality and greater susceptibility to nighttime disturbances compared to males (Uy & Gotlib, 2023). There is an emerging body of evidence suggesting certain parenting behaviors may differentially influence aspects of adolescent development based on a child's sex. Several studies have found that, compared to males, females tend to benefit more from higher levels of parental warmth, communication, involvement, and knowledge of their activities. For example, Rusby et al. (2018) observed parental monitoring had a stronger protective influence against substance use in adolescent females relative to males. Similarly, Lippold et al. (2021) found that parental warmth was negatively associated with internalizing problems among females but not males in their adolescent sample. While no research to date has directly examined sex differences in how parental factors relate to sleep outcomes, sleep represents a key developmental process undergoing changes during adolescence that is also deeply socialized within families. Given evidence that certain parenting behaviors may operate through sex-distinct pathways to shape psychosocial adjustment, it is plausible that parenting may differentially relate to sleep health in males and females. To this end, the present study aims to prospectively examine the influence of modifiable parental factors on multiple objective and subjective sleep domains in early adolescence, and the extent to which these associations are explained by emotion regulation and screen use. Specifically, we will test if greater parental warmth, higher monitoring, less family conflict, and better parent emotional health when the adolescent is 9-10 years old predicts improved sleep outcomes including longer duration, better quality, early chronotype, and more regular sleep patterns, 4 years later when the adolescents are aged 13-14. Further, we will examine if lower screen use and better emotion regulation partially mediates the effects of positive parenting on later sleep outcomes. We will also explore whether these relationships differ in females and males given sex differences in sleep patterns and adolescent developmental trajectories. By addressing limitations of prior cross-sectional research and leveraging a large, longitudinal dataset, this study has the potential to elucidate whether parental factors shape youth sleep partly through its effects on emotion regulation and screen use during these sensitive developmental periods. Identifying mediating factors could inform the development of family-based interventions targeting modifiable correlates of healthy sleep among youth.

This is a secondary data analysis of the Adolescent Brain Cognition Development dataset. The overall goal is to explore the relationship between salivary testosterone on reward anticipation and receipt between sexes, across puberty, and body composition.

Three online surveys to better understand the attitudes and beliefs held about dissociation among UK undergraduates, school students (GCSE), and NHS clinicians.

Is SES associated with specific cognitive abilities, or just general cognitive ability?

Increasing evidence demonstrates that environmental factors meaningfully impact the development of the brain (Hyde et al., 2020; McEwen & Akil, 2020). Recent work from the Adolescent Brain Cognitive Development (ABCD) Study® suggests that puberty may indirectly account for some association between the family environment and brain structure and function (Thijssen et al., 2020). However, a limited number of large studies have evaluated what, how, and why environmental factors impact neurodevelopment. When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating different operationalizations of the same construct on underlying interpretations. While one of the assets of Thijssen et al. (2020) was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. The analyses used several ‘researcher degrees of freedom’ (Gelman & Loken, 2014) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents’ environment comprised of different subfactors, such as parental monitoring and child family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. Therefore, this study extends the work of Thijssen et al. (2020) by evaluating the extent that the analytic choices in their study affected their conclusions using multiverse analyses. In Aim 1, using the same variables and models, we extend findings from the original study using the full sample in Release 3.0. Then, in Aim 2, we employ a multiverse analysis to consider nine alternative operationalizations of family, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. We then demonstrate and discuss how different environmental and demographic measures intended to capture stressful experiences in the ABCD data may differentiate findings and interpretations as they relate to puberty and the brain and make recommendations for the future studies using large open datasets, such as the ABCD study.

Part 3 of the Attitudes and Beliefs Concerning Dissociation (ABCD) project will survey patient-facing NHS clinicians. The survey will be repeated every two academic years (beginning in 2024-2025) until 2033.

This study aims to investigate the patterns of change in Attention Deficit and Hyperactivity Disorder (ADHD) symptoms and diagnoses over time. The goal is to identify types of change patterns in ADHD symptomology through a Latent Growth Curve Mixture Model (LGCM). To explain developmental trajectories, the identified typology classes will then be related to a selection of individual difference variables, or as called in machine learning terms, predictors (e.g. demographics, genetics, life events etc.) using a random forest model. In this study, we are using a large and longitudinal dataset consisting of American children from the Adolescent Brain Cognitive Development (ABCD) study. ADHD is a common neurodevelopmental disorder with a usually early childhood onset. Individuals with ADHD may suffer from impairments in paying and sustaining attention, impulsive behaviors, restlessness and hyperactivity. Hyperactivity symptoms are more salient during childhood. After puberty, hyperactivity tends to lessen, and inattentiveness is more present. Of the children with ADHD, 1 in 6 will continue to have ADHD as adults (Sibley et al., 2022). However, the current literature indicates that individual ADHD experiences tend to differ between individuals through development. ADHD is diagnosed by using the Diagnostic and Statistical Manual of Mental Disorders 5, the DSM-5 (American Psychiatric Association, 2013). The diagnostic criteria for ADHD consist of 9 inattention symptoms and 9 hyperactivity-impulsivity symptoms. When a child meets a minimum of 6 out of these 9 symptoms of hyperactivity-impulsivity, they may receive a predominantly hyperactive/impulsive ADHD diagnosis. The same applies to the inattention symptoms and the predominantly inattentive ADHD diagnosis. A third type is the combined ADHD diagnosis, where the child meets both the inattention and hyperactive-impulsive criteria. In addition, children must show some of the symptoms in more than one setting (e.g., classroom and at home) and before the age of 12 and there must be evident proof that the symptoms disrupt or diminish the level of social or academic performance. DSM-5 provides insight into the different types of ADHD and the severity of ADHD, but fails to capture the longitudinal changes that tend to occur in diagnostic status and subthreshold ADHD. In a longitudinal study, Sibley and colleagues (2022) tracked the ADHD trajectories of over 500 children from ages 2 to 16. They demonstrated that 63.8% of children had fluctuating ADHD, indicating that symptom patterns changed over time. Around 30% of children had periods where they were ADHD-free, but then 60% of those children had periods of recurrence. This study shed a very much needed light on the reality that ADHD is not a static disorder, on the contrary, it fluctuates over time. These important findings led us to our research question: Which individual differences variables contribute to different types of ADHD trajectories? To answer this question, we plan to use the largest longitudinal dataset that tracks the development of children, called the ABCD study. The ABCD study tracks over 11,000 children over a course of 10 years starting from ages 9-10 on, across 21 different data collection sites in the US. There are many measures from biospecimens, behavioral measures, to diagnostics. The current study is using the whole sample in order to be able to also detect possible subthreshold symptomology of ADHD.

Adolescence is a key period for the initiation and escalation of alcohol use, perhaps due to differential development rates of key brain structures implicated in addiction. There are a number of factors/changes hypothesised to underpin adolescent alcohol use; our recent systematic review of longitudinal structural and diffusion weighted neuroimaging studies focusing on neurobiological risk factors for alcohol use found that aberrant development of subcortical and frontal systems appeared to prospectively predict increased risk of early and escalating alcohol use in adolescence (Lees et al., 2021). However, inconsistent findings and conflicting direction of effects were observed. These inconsistencies may be due to relatively small sample sizes, as well as the multistage nature of adolescent alcohol use and the different milestones under consideration. In the developmental literature, there is some evidence that alcohol use milestones may be differentially associated with behaviours mediated by reward and inhibitory control systems. Focusing here on early initiation of alcohol use (defined here as first full drink before or by the age of 15 years), machine learning techniques will be employed to identify key structural neuroimaging predictors. These predictors will then be subjected to analyses that test whether they are causally implicated in alcohol use initiation.

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Individuals who identify as Jewish are a relatively small proportion of the population in the U.S. (around 2%; Pew Research Center, 2021b), thus relatively few empirical studies have assessed mental health processes, and among those that have assessed Jewish identity, many studies focus on the even smaller still orthodox sect. Here, we use a large-scale dataset with repeated assessments of psychopathology and indices of cultural, religious, and ethnic identification. The dataset is large enough to sufficiently estimate developmental trajectories of participants with Jewish identities. Our goal is to describe trends for Jewish-American participants and to test individual differences within this identity group. We are not interested in comparing this group of participants to any other group.

This study will investigate the association between PGRS-depression and structural brain measures in the ABCD study.

This study will investigate the association between PGRS-depression and structural brain measures in the ABCD study.

The purpose of the proposed study is to advance scientific knowledge about the neural mechanisms undergirding associations between race-related discrimination and self-regulatory functioning during adolescence. This objective will be addressed across two broad aims using the longitudinal and nationally representative dataset from the Adolescent Brain and Cognitive Development study. Aim 1 is to identify associations among racial discrimination, functional brain connectivity, and self-regulatory functioning among youth of color. This aim will be addressed using a mediation analysis wherein the association between discrimination and self-regulatory functioning is examined via the functional connectivity of neural networks in the brain. Aim 2 explores familism and school support as protective contexts that moderate the effect of racial discrimination on functional brain connectivity. This study is meant to address a critical gap in the neurodevelopmental literature by identifying the association between discrimination and neural functioning during adolescence. Findings have the potential to identify the neurocognitive processes implicated in experiences with discrimination as well as potential protective factors in adolescents' environments.

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This study aims to investigate the relationship between reward processing, impulsivity and externalising behaviours within adolescence. Reward processing and impulsivity will be assessed by performance on well-established cognitive tasks (Monetary Incentive Delay and Stop Signal Task) and associated neural activity. Longitudinal analysis will allow for the examination of directionality of relationships and stability over time. Measures of socio-economic status, parental mental health and pubertal timing will be included as covariates, as these are factors that are frequently associated with mental health outcomes in this age group.

This research investigates how family conflict, digital coping strategies, and psychopathology intersect over time among adolescents aged 11 to 14. Using data from the national Adolescent Brain Cognitive Development (ABCD) Study, we examine how family conflict and psychopathology influence each other over time, and how digital coping strategies moderate this relationship. Specifically, we explore the efficacy of social phone use behaviors as coping mechanism for adolescents in the context of family conflict.

Paraxial lens optics is discussed to study the continuity properties of the $ABCD$ beam transfer matrix. The two-by-two matrix for the one-lens camera-like system can be converted to an equi-diagonal form by a scale transformation, leaving the off-diagonal elements invariant. It is shown that the matrix remains continuous during the focusing process, but this transition is not analytic. However, its first derivative is still continuous, which leads to the concept of "tangential continuity." It is then shown that this tangential continuity is applicable to $ABCD$ matrices pertinent to periodic optical systems, where the equi-diagonalization is achieved by a similarity transformation using rotations. It is also noted that both the scale transformations and the rotations can be unified within the framework of Hermitian transformations.

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DSM-5-TR diagnostic criteria does not fully address behavioral and clinical heterogeneity inherent to ADHD; however, temperament-based profiles may help explain biological heterogeneity within the disorder. Temperament profiles have been defined and replicated among youth with ADHD and have demonstrated unique patterns of resting-state functional connectivity within a small sample. Two temperament profiles have been identified by Kozlowski et al. (this special issue) in the ABCD Study, and the present study sought to replicate and validate documented resting-state patterns. Functional connectivity between bilateral amygdalae and 12 Gordon networks was compared between profiles and typically developing (TD) youth. Surgent youth demonstrated stronger right amygdala-dorsal attention network connectivity (ß=.0434) and right amygdala-retrosplenial temporal network connectivity (ß=.0442) compared to TD youth. Irritable youth demonstrated unique connectivity patterns compared to TD and surgent youth; however, effects did not survive correction for multiple comparisons. Findings provide support for future research examining temperament profiles among ADHD youth.

The congruency sequence effect (CSE) refers to a smaller congruency effect after incongruent trials than after congruent trials. Robust CSEs often appear in the prime-probe task, wherein an initial prime (usually a distractor) precedes a subsequent probe (usually a target). Recent findings indicate a CSE appears in mean probe reaction time (RT) and mean probe error rate (ER), even when subjects respond to both the initial prime and the subsequent probe, such that there are no distractors (Grant & Weissman, 2019; Weissman, 2019; Weissman et al., 2017). Furthermore, a CSE appears in this “modified prime-probe task” even when subjects respond to the prime and probe in each trial using fingers on different hands (Weissman, Grant, and Jones, 2020). In an initial experiment involving this specific paradigm, the prime and probe stimuli were identical letters, and the primes and probes of congruent trials were mapped to anatomically corresponding (i.e., homologous) fingers on the left and right hands. Thus, control processes could be using four types of congruency relations to produce the observed CSE in Experiment 1 of Weissman et al. (2020): 1) perceptual stimulus relations, since the prime and probe were the same letter in congruent trials but not incongruent trials; 2) categorical stimulus relations, since the prime and probe were the same letter category in congruent trials but not incongruent trials; 3) anatomical response relations, since the prime and probe were mapped to homologous finger responses in congruent but not incongruent trials; and 4) spatial response relations, since the prime and probe were mapped to spatially corresponding (i.e., from inner to outer on each hand) finger responses in congruent but not incongruent trials. To begin to better understand which congruency relation(s) these control processes use to produce a CSE, we will conduct a series of experiments. The first will be a variant of Experiment 1 from Weissman et al. (2020), wherein anatomical response relations cannot be used. More specifically, we will investigate whether control processes can engender a CSE (Weissman et al., 2020) when the primes and probes are mapped to spatially, but not anatomically, corresponding fingers (i.e., non-homologous fingers).

We used fMRI functional connectivity during Emotional N-back task in the ABCD dataset to predict cognitive measure constructed from the mean of the 5 of the NIH Toolbox tasks: Picture Vocabulary, Flanker Inhibitory Control & Attention, Pattern Comparison Processing Speed, Picture Sequence Memory, and Oral Reading Recognition. We are registering these Linear Support Vector Regression Model parameters (Beta coefficient maps for the Shen atlas-based functional connectivity matrix) to be applied to the 2nd fMRI release of the ABCD and predict individuals' NIH-5 mean cognitive score in the 2nd release.

A major component of the Adolescent Brain Cognitive Development (ABCD) study is fMRI data from cognitive tasks performed in the scanner, vital to the assessment of neurocognitive development (Volkow et al., 2018). However, the ability of ROI analyses, using general linear models, to extract robust, meaningful relationships between individual differences in task fMRI data and outcome dimensions of interest has been called into question (e.g., Cooper et al., 2018). To address this issue, we will test the application of structural equation modeling to fMRI data taken from activation during the emotional N-back task (EN-back task) to assess whether extracting underlying factor(s) across a prominent brain network underlying cognitive control, the frontoparietal network (FPN), may provide a viable approach to analyzing individual differences in brain-behavior relationships. More specifically, a factor(s) constructed from FPN ROIs should index a latent measure of neurological activation associated with common executive functions (EF); if so, it should predict a latent behavioral factor tapping common EF (see Friedman & Miyake, 2017). This is because areas of the brain that are consistently activated in tasks of cognitive control/executive function, (as in the case of the FPN), may work together to perform general functions such as goal-related processing, including maintaining and adjusting goals, and biasing processing to adhere to goals (Herd et al., 2014). We will test this hypothesis by regressing a common EF factor on our FPN factor(s) via a path coefficient. First, a confirmatory factor analysis (CFA) will be constructed to tap a common factor underlying the brain activation of the frontoparietal network (FPN) when individuals must exert cognitive control, as assessed by the 2-back condition of the EN-back task, as compared to a condition with little or no control demands, as in the 0-back condition. Thus, we will utilize twelve Destrieux parcellations of the FPN (Destrieux, 2010) within the 2-vs 0-back contrast. For model comparison, we will construct a single-factor model and a two-factor model, with two separate factors for the left and right hemisphere. Once we have chosen a best-fit model between these two options, our main analysis of interest is to assess whether the underlying factor associated with the FPN demonstrates significant effect sizes in predicting individual differences in our cognitive-behavioral variables of interest – specifically, latent factors derived from the cognitive battery and task-fMRI behavioral data, including a common EF factor, an updating-specific factor, and an intelligence (IQ) factor. Additionally, we will perform further analyses to explore the robustness and specificity of any observed associations. The first two analyses are designed to determine whether the observed effects of the FPN activation factor in predicting cognitive dimensions are specific to the FPN. First, we will perform permutation tests to determine the extent to which the factor derived from our FPN model better predicts individual differences in our cognitive-behavioral latent factors than a factor representing brain activation across a random selection of brain regions. For example, there may be commonalities in whole-brain activation patterns across random brain regions that are not specifically associated with cognitive control but may still significantly predict individual differences in cognitive dimensions in our dataset. Therefore, the permutation tests allow us to test whether our FPN model performs beyond these commonalities in whole-brain activation. This method will involve performing 1,000 permutation tests of twelve random indicators plugged into a model that replicates the specifications of our chosen FPN model. We are interested in comparing the effect sizes of the factors derived from random indicators in predicting individual differences in our cognitive-behavioral latent factors. Permuted models may not converge due to poor fit – therefore, the final distribution will be from only those models that converged, with the number of models that failed to converge reported. Second, because the regions in this first analysis will be randomly selected and unlikely to index any coherent brain activity, we will also examine the specificity of any observed relationships between the FPN and outcome variables, as compared to a “control” brain network. More specifically, we will extract a factor, using a separate CFA, of activation of the sensorimotor network (SMN) in the same 2-vs 0-back contrast utilizing twelve parcellations of the Destrieux map. For model comparison, we will again construct a single-factor model and a two-factor model, with two separate factors for the left and right hemisphere. From the chosen model, we will determine whether individual differences in activation of this alternative SMN network meaningfully predict our cognitive-behavioral latent factors, as well as whether this alternative network is outperformed by our FPN model. Third, we are interested to what extent the findings with the FPN are specific to our cognitive dimensions by testing a variable from outside the domain of cognitive control. We will do so by testing whether the FPN factor additionally predicts a latent factor for processing speed, using four indicators (Pattern Comparison scores, average go RT for the Stop Signal task, average 0-back RT on the EN-back, and neutral trial average RT on the Monetary Incentive Delay task). Fourth and finally, we are interested in testing whether our proposed analysis approach is feasible for variables outside of cognitive control. To do so we will create a separate CFA to tap the SMN utilizing the 0-back-vs-fixation contrast of the EN-back task, to index change in activation associated with performing a cognitively undemanding task versus staring at a fixation cross, utilizing twelve parcellations from the Destrieux map. For model comparison, we will again construct a single-factor model and a two-factor model. The factor from the chosen SMN model will be tested in predicting the same latent factor for processing speed we have described above, to ensure that the SMN demonstrates meaningful relationships when utilized in a theoretically grounded framework. Additionally, we will test whether the SMN significantly predicts our cognitive dimensions of interest. This analytic approach to examine brain-behavior relationships will be relatively novel, given that fMRI studies rarely have a large enough sample size to power such an analysis (e.g., Elliot et al., 2020). However, a previous analysis from the Human Connectome Project (HCP), which has a sample size of 1,200, has demonstrated the viability of this analytic approach of deriving an underlying factor score to assess brain activation and additionally to link it to individual differences in behavior (Cooper et al., 2018). Therefore, the present work may offer unique evidence in a separate dataset for a novel methodological approach in brain-behavior relationships, as well as possible insight into the nature of such relationships.

This study seeks to examine the discrepancy between sunlight exposure and social responsibility ("social jetlag") on child behavior, cognition, and mental health among youth living near a time zone boundary. This project will leverage multi-site data collected across the US by the Adolescent Brain and Cognitive Development study (abcdstudy.org). The concept of social jetlag pertains to the discontinuity in sunlight and clock time near time zone boundaries. For example, the time of sunset differs by roughly one hour depending on the side of the time zone boundary (locations just east of a time zone boundary have later sunset times). Despite this discontinuity, social responsibilities generally do not differ by geographical location (i.e., school / workplace starts at 8 am on both sides of boundary). This project is informed by a prior study that exploited the discontinuity between sunset and clock time using regression discontinuity designs (Giuntella & Mazzona, 2019). In that paper, authors found a causal effect of social jetlag on economic and health disadvantages. Giuntella, O., & Mazzonna, F. (2019). Sunset time and the economic effects of social jetlag: evidence from US time zone borders. Journal of health economics, 65, 210-226.

Determine whether associations between alcohol sipping and brain structure and behavior are attributable to causal effects of alcohol and/or shared predispositional effects. We will use a discordant twin and sibling design in the Adolescent Brain Cognitive Development (ABCD) Study to do so.

Rates of suicidal thoughts and behaviors increase rapidly during the transition into adolescence, with approximately 19% of adolescents reporting past-year suicide ideation (Ivey-Stephenson et al., 2020) as compared to only approximately 8% of children ages 9-10 reporting lifetime suicide ideation (Janiri et al., 2020). However, although there is evidence of increasing rates of suicidal thoughts and behaviors during this critical developmental period, we can only assume that this increase in reporting is indicating actual increases in rates of this phenomenon if our measures used are invariant over time, meaning that they are measuring the same thing in the same way. Indeed, emerging research highlights the variability in responses based on the ways adolescents are asked about suicidal thoughts and behaviors (Ammerman et al., 2021) and that different measures of suicide ideation may be tapping into multiple latent constructs of suicidal thoughts (Wastler et al., 2022). However, thus far this research has been conducted among adults, leaving many questions unanswered regarding youth. Moreover, both youth and their parents/caregivers are important sources of information about youth suicidal thoughts and behaviors—youth are the best knowers of their own internal experiences and private behaviors, yet their reports might be inaccurate for various reasons, including privacy concerns or shame (e.g., Fox et al., 2021). Parents may be more willing to report youth suicidal thoughts and behaviors in a research context, but do not have access to the same information or level of insight into the child’s thoughts and behaviors. In past research, youth typically report experiencing suicide ideation, planning, and attempt more frequently than do their parents when surveyed about youth experiences (DeVille et al., 2020; Gratch et al., 2021; Klaus et al., 2009). Notably, reporter discrepancies may signal clinically meaningful processes (Spears et al., 2023). As Spears and colleagues (2023) summarize, relationship characteristics (e.g., interpersonal strain, low family support), parental characteristics (e.g., versus non-primary caregiver), and adolescent characteristics (e.g., younger age, multiple suicide attempts, racial minority background) are associated with lesser agreement in reports of suicidal thoughts and behaviors. Moreover, parental depression history has demonstrated mixed evidence for associations with consistency of parent-child reports (Kim et al., 2016; Klaus et al., 2009; Klimes-Dougan, 1998). Confirming measurement invariance across reporters (e.g., youth and their parents) will help us to understand whether youth and parent reports can be directly compared (e.g., as they are measuring the same thing in the same way), or if reporter “differences” are reflective of variation in measurement of suicidality. Documenting discrepant patterns of suicide reporting over time and across reporters has considerable implications for research on suicidal thoughts and behaviors during the important adolescent period. However, to our knowledge, the existing research has not yet sought to evaluate measurement invariance of these measures over time and across reporters during early adolescence. This represents an important gap, given the clear implications of measurement (non)invariance for the interpretation of research using youth suicidality measures. In the present study, we will examine measurement invariance of suicidal thoughts and behaviors items on a widely-used, gold standard diagnostic instrument, the computerized Kiddie Schedule for Affective Disorders for DSM-5 (K-SADS-COMP; Kaufman et al., 1997, 2021), in the large (> 11,000) Adolescent Brain Cognitive Development Study (ABCD Study®) sample of youth and their parents/caregivers, prospectively assessed every 6 months to 2 years, starting at ages 9 and 10 years (and with assessments ongoing and regularly scheduled data releases for future assessments). As a first step, we will leverage the discovery and replication sets from ABCD Reproducible Matched Samples (ARMS) to derive suicidality structure at baseline (age 9/10 years). We expect to find evidence of a single “suicidality” factor or separate “passive” and “active” suicidality factors, as found in previous research with youth and adults (Luxton et al., 2011; Pettit et al., 2009; Wastler et al., 2023) (though measurement invariance analyses can be applied for various structures, if we do not identify this expected structure). We will then conduct measurement invariance analyses of this identified factor structure across time (baseline [age 9/10 years], follow-up 1 [age 10/11 years], follow-up 2 [age/11/12 years]), separately for each reporter, and across reporter (youth, parents/caregivers), separately at each time point. We expect to find evidence of configural, metric, and scalar invariance across time points and reporters, indicating that measured changes in suicidality during this early adolescent developmental period indeed indicate changes in prevalence of the same construct and that measured reporter discrepancies truly indicate discrepant reporting of the same construct. However, findings contrary to our expectations will also be informative, as evidence of a lack of measurement invariance will fundamentally alter interpretations of change over time and reporter convergence or discrepancies within the large-scale ABCD Study and beyond. Moreover, if we fail to find evidence for configural invariance, this would speak to straight structural differences in the very construct of suicidality during this period. Importantly, analyses would then shift to a descriptive approach evaluating the (changing) structure of suicidality across adolescence.

Join in as Mayor Walsh helps to cut the ribbon on ABCD's new state of the art early care and education center at 82 Savin Street in Roxbury. As part of a 25 year lease from the City of Boston and held in trust by the George Robert White Foundation, ABCD undertook significant renovations of this historic space, which now houses Early Head Start and Head Start classrooms, after school programming for infants, toddlers, preschoolers, and young children, as well as a professional development/meeting space. To view the full event please visit: https://www.youtube.com/watch?v=6W8wmXwB31A

Found at a local thrift store.

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Summary Customized version of https://www.thingiverse.com/thing:1672381 Created with Customizer! https://www.thingiverse.com/apps/customizer/run?thing_id=1672381 Instructions Using the following options: LeftRight = Right HandLen = 135 ForearmLen = 140 BicepCircum = 160 CuffLength = 67 PinHoleDia = 3

The current study aims to investigate childhood adversity and family conflict, and their associations with trajectories (i.e., change over time) of anxiety and other mental health problems in a large-scale, representative, longitudinal study of adolescents.

Cohort Study using data from the ABCD study

Maternal cannabis use during pregnancy has been associated with adverse fetal and birth outcomes 1-4. Likewise, prenatal cannabis exposure has been linked to adverse neurodevelopmental outcomes in offspring (i.e., psychosis, attention and behavioral problems) 4-8. Therefore, prenatal cannabis exposure may alter the structural and functional development of the brain 9,10. An animal study shows that in mice exposure to cannabis prenatally is related to brain damage, specifically with a reduced volume of the hypothalamus and thalamus 11. However, human studies in children have generated inconclusive findings using Magnetic Resonance Imaging (MRI) 4,12,13. A cross-sectional study in the United States (US) in 115 fetuses at 32 gestational weeks using functional magnetic resonance imaging, a voxel-wise hippocampal connectivity analysis found that prenatal cannabis exposure was linked to changes in fetal brain connectivity (i.e., dorsolateral, medial and superior frontal, insula, anterior temporal, and posterior cingulate) 13. In contrast, a prospective study in the Netherlands in 263 mother-children pairs found that children aged 6 to 8 years exposed to cannabis prenatally had thicker frontal cortices, but no differences in total brain volumes 12. Conversely, a retrospective study in the US in 11,489 children aged 9 years found no association between prenatal cannabis exposure and brain total volumes 4. These inconsistent findings suggest that prenatal cannabis exposure may affect certain regions of brain development, but likely does not affect overall brain size, and these effects may differ based on age development. It is critical to consider the limitations of each study design to ensure that research findings are reliable and robust. Retrospective studies are more susceptible to recall bias and poorer quality of data, while prospective studies provide more reliable and detailed information of confounder variables, mainly time varying confounders. Thus, a harmonized approach to compare findings from both retrospective and prospective study designs is needed. This may provide us with a better understanding of the associations and help assess the consistency and generalizability of findings. Since cannabis users typically combined cannabis with tobacco, tobacco smoking during pregnancy is an important contrasting exposure in neurodevelopmental studies 14,15. Prenatal tobacco exposure has been associated with adverse fetal and child neurodevelopmental outcomes, but previous studies used small sample sizes 16-19. Furthermore, residual confounding factors may generate non-causal links between maternal cannabis use during pregnancy and brain structure in offspring. We aim to compare the strength of associations between maternal exposure and offspring outcomes versus paternal exposure and offspring outcomes to address these biases. If there is a direct biological effect of intrauterine exposure on offspring health status, the associations with offspring brain structure should be stronger for exposure of mothers than for exposure of fathers 20.

I will use how adolescents themselves describe their pubertal status as an indicator in the puberty-age-gap pipeline and test its association with (1) emotional well-being and (2) brain function in emotional circuits. How does pubertal timing relate to functional activations during emotional processing? Hypothesis: We predict those with earlier pubertal timing, as indicated by the derived puberty age gap, will be associated with differences in brain function during an emotional processing task compared to their on-time and later peers. Specifically, we predict that those who start puberty earlier will exhibit greater activation in emotional circuits during an emotionally salient task. How does pubertal timing, derived from adolescent reports, relate to emotional well-being? Hypothesis: We predict those with earlier pubertal timing will report more decreased emotional well-being later compared to their on-time or later peers.

This is a secondary data analysis looking at the potential mediating effect of glycemia (HbA1c) on changes in brain connectivity between baseline and 2 years.

Childhood conduct disorder (CD) is defined by repetitive and pervasive antisocial behavior, including rule-breaking and aggression. A prominent approach to characterizing risk for CD is based on the presence or absence of callous-unemotional (CU) traits (Frick et al., 2014a). CU traits are characterized by a lack of empathy and guilt, representing a downward extension of the affective features of adult psychopathy (Frick, 2009; Hare, 1991). Children with CU traits are at very high risk for exhibiting future violent behavior and substance use (Baskin-Sommers et al., 2015). Explanations on the etiology of CD have centered on increased sensitivity to cues of reward and decreased sensitivity to punishment cues (Byrd et al., 2014; Blair et al., 2018; Frick et al., 2014a, 2014b; Raine, 2018). These difficulties are thought to be particularly pronounced among children with CU traits (Byrd et al., 2014, 2018; Budhani & Blair, 2005; Frick et al., 2014b). A focus on reward and punishment sensitivity is important since these processes are also central to behavioral interventions designed to reduce CD symptoms (Hawes & Dadds (2005), including when children have high CU traits. However, interventions appear less effective or bring about less change in CD symptoms among children with high CU traits, which may be the result of differences in responding to punishment and reward cues. To better understand differential patterns of reward and punishment sensitivity associated with CD and CU traits, studies have investigated neural activation during functional imaging tasks whereby different cues of punishment and reward are presented. Findings have been mixed in terms of directionality (Byrd et al., 2014; Hyde et al., 2013). For example, decreased activation of the orbitofrontal cortex (OFC; Finger et al., 2011; Rubia et al., 2009), ventromedial prefrontal cortex (vmPFC; Zhang et al., 2019), striatum (Cohn et al., 2015; White et al., 2013; Zhang et al., 2019), and amygdala (Cohn et al., 2015) has been linked to CD and CU traits in children and adolescents during reward receipt. However, increased activation of the striatum, anterior cingulate cortex (ACC), OFC and amygdala (Bjork 2010; Hawes et al., 2021) has also been linked to CD and CU traits during reward receipt, with differences emerging on the basis of task used, as well as whether important covariates such as comorbid attention-deficit/hyperactivity (ADHD) or head motion were included in models. Moreover, during reward anticipation, CU traits were associated with decreased activation in mPFC (Veroude et al., 2016), and dorsal ACC (Hawes et al., 2021) and increased activation in striatum, insula and superior frontal gyrus (Hawes et al., 2021; Huang et al., 2019). Likewise, increased activation in caudate (Finger et al., 2008; White et al., 2013), vmPFC and amygdala (Finger et al., 2008; Cohn et al., 2015) during loss and punishment receipt has been related to CD and CU traits. However, the amygdala was also reported to show reduced activation (Byrd et al., 2018; Huang et al., 2019), or no difference in brain activity in any region when comparing children with low versus high CU traits (Byrd et al., 2021), with some differences based on whether studies accounted for comorbid externalizing symptoms (Huang et al., 2019), ADHD and internalizing symptoms (Byrd et al., 2018, 2021), or used longitudinal data (Cohn et al., 2015). One explanation for these discrepant findings is that children with CD and high CU traits are not a homogeneous group. In the last decade, research has further differentiated between CD with CU traits inspired by work by Karpman (1941), which categorized adults high on psychopathic traits into a “primary” subtype (characterized by low anxiety and low emotional reactivity) and a “secondary” subtype (characterized by high anxiety and high emotional reactivity). Among children with CU traits, studies have provided support for the existence of similar variants, with the secondary variant evidencing higher impulsivity, hostility, and hypervigilance to cues of threat and distress (Craig et al., 2021; Dean et al., 2013; Kimonis et al., 2012a), higher aggression (Ezpeleta et al., 2017; Kahn et al., 2013; Kimonis et al., 2011; Sheti et al., 2018), greater experience of maltreatment (Kimmonis et al., 2012a), physical and sexual abuse (Kahn et al., 2013; Sheti et al., 2018) as well as lower socioeconomic status and inconsistent parenting (Ezpeleta et al., 2017). There is mixed evidence with regards to whether secondary CU traits variant shows higher levels of substance use compared to primary group (Kimonis et al., 2012b) or no differences between groups (Sheti et al., 2018; Waller & Hicks, 2019). Importantly, based on prominent theory, the two variants might be expected to differ in sensitivity to reward and punishment (Lykken, 1995; Gray, 1987). In adults, primary psychopathy is thought to be underpinned by an underactive behavioral inhibition system (BIS) when facing punishment (i.e., fearless temperament) and secondary psychopathy by an overactive behavioral activation system (BAS; greater reward responsiveness). Consistent with Lykken’s hypothesis, some studies of youth that have used self-report measures of BIS and BAS have found a primary psychopathy variant is related to lower BIS scores and a secondary psychopathy variant to higher BAS scores (Bjornebekk & Gjesme, 2009; Gill & Stickle, 2015; Kahn et al., 2015; Krimbel et al., 2007; Ross et al., 2011), similar to findings in adults (Newman et al., 2005). However, in other studies, a secondary variant has also been reported to show high BIS scores (Gill & Stickle, 2015) and higher BAS scores to predict primary variant membership (Bjornebekk & Gjesme, 2009; Krimbel et al., 2007; Ross et al., 2011). When variants are directly compared, a secondary variant showed higher BAS and lower BIS than a primary variant in adolescents (Kahn et al. 2013) and greater sensitivity to both reward and punishment in childhood (Ezpezeleta et al. 2017). Neuroimaging can provide additional insight into differences between primary and secondary variants, including characterizing differences in sensitivity reward and punishment. A recent review concluded, based on endocrine and biological indicators that the secondary variant shows hyperarousal and dysregulation of the stress response in the amygdala and HPA axis, linked to greater experiences of trauma (see Craig et al., 2021 for details). However, only a handful of studies have investigated neural correlates of primary versus secondary variants, with a focus on punishment learning paradigms. In adults, a primary psychopathy variant showed heightened activation to punished stimuli in the middle frontal gyrus and dorsal ACC, suggesting normal learning and fear responding, while a secondary variant showed greater activation in subgenual ACC, previously related with fear inhibition (Schultz et al., 2016). In youth, the primary variant was associated with lower right amygdala activation while in response to unconditioned stimuli (neutral faces). In contrast, the secondary variant showed no difference in amygdala response comparing conditioned with aversive electric stimuli and unconditioned stimuli during extinction (Fanti et al., 2020). To date, however, no prior studies have explored whether primary and secondary CU traits variants differ in neural processing in response to reward and punishment anticipation and receipt, which could provide additional insight into the etiology of these different variants, with implications for intervention and treatment strategies. To address this gap, the current study aims to extend previous research by focusing on differences in reward and punishment processing and exploring factors that contribute to the belongingness of youth with primary versus secondary CU traits variants. Specifically, reward and punishment processing will be examined including the basis of BIS and BAS scores, and neural activation in regions linked to the processing of reward and punishment.

The primary aims of this project are to create PRS from the "Cog" and "Non-Cog" factors from the GWAS-by-subtraction from Demange et al. (2021), and then to test whether those PRS are associated with cognitive, SES-related, psychopathology, risk and decision-making measures, and neuroimaging metrics in the ABCD Study sample.

Adolescence is a period during which many mental health difficulties first arise (Kessler, Berglund, Demler, Jin, Merikangas, & Walters, 2005). Difficulties in emotion processing have been associated with an increased risk of mental health difficulties in adolescence, including internalising difficulties such as anxiety and depression (Garnefski, Kraaij, & van Etten, 2005; Silk, Steinberg, & Morris., 2003). Internalising symptoms in adolescence, even at subclinical levels, can persist into adulthood (Petersen et al., 2018; Winefield, Hammarström, Nygren, & Hägglöf, 2013). Understanding early deficits in emotional processing is therefore essential for developing our understanding of how and when internalising problems first arise. There is a wealth of previous research that has investigated the presence and nature of emotion processing deficits in children and adolescents with internalising problems. Children and adolescents with anxiety disorders perform significantly less accurately on facial emotion recognition tasks compared to non-anxious controls (Collin, Bindra, Raju, Gillberg, & Minnis, 2013). Equally, a general deficit in emotion recognition in depressed individuals has been indicated in reviews and meta-analyses of studies involving both adults and children (Kohler, Hoffman, Eastman, Healey, & Moberg, 2011; Dalili, Penton-Voak, Harmer, & Munafo, 2015). When looking specifically at how anxious and depressed youths process different facial emotions, the literature indicates that it may be more nuanced than a general deficit across the board. For example, adolescents with depression have demonstrated a dual deficit in emotion processing whereby they are more accurate than non-depressed controls at recognising sad faces and less accurate at recognising happy faces (Auerbach, Stewart, Stanton, Mueller & Pizzagalli, 2015). However, the recent literature exploring how well children and adolescents with internalising problems recognise different emotions has yielded mixed results. For example, some studies have found that children and adolescents with increased internalising symptoms are more accurate at identifying fearful faces (Simcock et al, 2020) whilst other studies have found the opposite pattern, with anxious adolescents performing worse than healthy controls when identifying fearful expressions (Wieckowski et al, 2016; Dede, Delk, & White, 2021). Despite these discrepancies, research has consistently identified an attentional bias towards threatening or fearful faces in those with greater internalising symptoms (see Valadez, Pine, Fox, & Bar-Haim, 2022 for review). Collin and colleagues’ (2013) review found that anxious youth demonstrated an attentional bias towards angry or fearful faces relative to controls (McClure et al., 2007; Monk et al., 2006; Waters, Mogg, Bradley, & Pine, 2008), whilst children with both BPAD and anxiety were found to show a bias towards threatening faces (Brotman et al, 2007). Children and adolescents with BPAD were also found to be more afraid in response to neutral faces than healthy controls, further suggesting a bias towards perceived threat (Brotman et al, 2010; Rich et al, 2006). This attentional bias towards threatening faces is likely to impact these children and adolescents’ abilities to recognise threatening emotions, such as fear. The relationship between emotion recognition and internalising problems should be explored in more detail to shed light on different findings found thus far. In order to study developmental processes, we must take a developmental perspective to conducting research. Previous studies have therefore cited the need for more longitudinal research. Longitudinal analyses are required to determine whether behavioural and neurobiological differences in emotion processing can in fact be understood as early indicators of later psychopathology or whether they might be ‘transient developmental variations’ (Blok et al, 2021). This may depend on the impact of biological, psychological or social risk and protective factors, which can also be investigated using longitudinal data. Findings from this kind of research could have important implications for mental health prevention and early intervention planning. This study’s main objective is to better understand the relationship between the processing of fearful faces and internalising traits in adolescents. Specifically, I aim to better understand how individual differences in internalising traits are associated with the recognition of fearful faces as well as the underlying associated brain activity. I will do this by utilising an existing dataset – the Adolescent Brain Cognitive Development (ABCD) study. In the first (cross-sectional) part of my study, I will use data from the most recent ABCD data collection wave, which is from individuals aged 11-12 years. I will consider both performance on a facial emotion recognition task (the Emotional N-back task; Casey et al, 2018) in response to fearful faces and brain activation in key regions of interest during this task in order to provide a thorough exploration of this association. Key brain regions that are thought to underpin the processing of fearful faces include the amygdala, the anterior cingulate cortex (ACC), the ventromedial prefrontal cortex (vmPFC) and the ventrolateral prefrontal cortex (vlPFC; Del Piero, Saxbe, & Margolin, 2016;). Hyperactivation in the amygdala (Hulvershorn, Cullen, & Anandl, 2011; Yang et al, 2010; Monk et al, 2008) and ACC (McClure et al, 2007; Gotlib et al, 2005) have been associated with increased internalising difficulties, whilst hypoactivation in the vmPFC (Pitskel et al, 2011; Johnstone et al, 2007) and vlPFC (Pavuluri et al, 2007; Passarotti et al, 2010) have been associated with increased internalising difficulties. In the second (longitudinal) part of my study, I will explore whether earlier facial emotion recognition ability and the associated underlying brain activity can predict later internalising symptom severity. I will explore this by using ABCD data obtained from two time points: time 1 (age 9-10 years) and time 2 (age 11-12 years). Beyond this, I will also explore whether any key developmental or social variables might also predict internalising symptom severity in early adolescence, as this may highlight which young people are most vulnerable. Specifically, this includes sex, pubertal timing, parental mental health, and socioeconomic status. These factors have all been identified as important risk factors for developing internalising difficulties (Lynch et al, 2021; Reiss, 2013). This project will be published as a doctoral student thesis. It forms part of a larger project exploring predictors of externalising and internalising within the ABCD cohort.

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