Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

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.

The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods.

Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental neuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmental sciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginner-friendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications.

Differential processing of risk and reward in delinquent and non-delinquent youth.

The present study examined the behavioral and neural differences in risky decision-making between delinquent (n = 23) and non-delinquent (n = 27) youth ages 13-17 years (M = 16, SD = 0.97) in relation to reward processing. While undergoing functional neuroimaging, participants completed an experimental risk task wherein they received feedback about the riskiness of their behavior in the form of facial expressions that morphed from happy to angry. Behavioral results indicated that delinquent youth took fewer risks and earned fewer rewards on the task than non-delinquent youth. Results from whole-brain analyses indicated no group differences in sensitivity to punishments (i.e. angry faces), but instead showed that delinquent youth evinced greater neural tracking of reward outcomes (i.e. cash-ins) in regions including the ventral striatum and inferior frontal gyrus. While behavioral results show that delinquent youth were more risk-averse, the neural results indicated that delinquent youth were also more reward-driven, potentially suggesting a preference for immediate rewards. Results offer important insights into differential decision-making processes between delinquent and non-delinquent youth.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort.

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Neighborhood disadvantage, race/ethnicity and neural sensitivity to social threat and reward among adolescents.

Experiences within one's social environment shape neural sensitivity to threatening and rewarding social cues. However, in racialized societies like the USA, youth from minoritized racial/ethnic backgrounds can have different experiences and perceptions within neighborhoods that share similar characteristics. The current study examined how neighborhood disadvantage intersects with racial/ethnic background in relation to neural sensitivity to social cues. A racially diverse (59 Hispanic/Latine, 48 White, 37 Black/African American, 15 multi-racial and 6 other) and primarily low to middle socioeconomic status sample of 165 adolescents (88 female; Mage = 12.89) completed a social incentive delay task while undergoing functional magnetic resonance imaging (fMRI) scanning. We tested for differences in the association between neighborhood disadvantage and neural responses to social threat and reward cues across racial/ethnic groups. For threat processing, compared to White youth, neighborhood disadvantage was related to greater neural activation in regions involved in salience detection (e.g. anterior cingulate cortex) for Black youth and regions involved in mentalizing (e.g. temporoparietal junction) for Latine youth. For reward processing, neighborhood disadvantage was related to greater brain activation in reward, salience and mentalizing regions for Black youth only. This study offers a novel exploration of diversity within adolescent neural development and important insights into our understanding of how social environments may 'get under the skull' differentially across racial/ethnic groups.

Digital access constraints predict worse mental health among adolescents during COVID-19.

The COVID-19 pandemic and ensuing social restrictions disrupted young people's social interactions and resulted in several periods during which school closures necessitated online learning. We hypothesised that digitally excluded young people would demonstrate greater deterioration in their mental health than their digitally connected peers during this time. We analysed representative mental health data from a sample of UK 10-15-year-olds (N = 1387) who completed a mental health inventory in 2017-2019 and thrice during the pandemic (July 2020, November 2020 and March 2021). We employed longitudinal modelling to describe trajectories of adolescent mental health for participants with and without access to a computer or a good internet connection for schoolwork. Adolescent mental health symptoms rose early in the COVID-19 pandemic, with the highest mean Total Difficulties score around December 2020. The worsening and subsequent recovery of mental health during the pandemic was greatly pronounced among those without access to a computer, although we did not find evidence for a similar effect among those without a good internet connection. We conclude that lack of access to a computer is a tractable risk factor that likely compounds other adversities facing children and young people during periods of social isolation or educational disruption.

Intrinsic connectivity within the affective salience network moderates adolescent susceptibility to negative and positive peer norms.

Not all adolescents are equally susceptible to peer influence, and for some, peer influence exerts positive rather than negative effects. Using resting-state functional magnetic resonance imaging, the current study examined how intrinsic functional connectivity networks associated with processing social cognitive and affective stimuli predict adolescents' (n = 87, ages 11-14 years) prosocial tendencies and risky behaviors in the context of positive and negative peer norms. We tested the moderating role of four candidate intrinsic brain networks-associated with mentalizing, cognitive control, motivational relevance, and affective salience-in peer influence susceptibility. Only intrinsic connectivity within the affective salience network significantly moderated the association between peer norms and adolescent behavior above and beyond the other networks. Adolescents with high intrinsic connectivity within the affective salience network reported greater prosocial tendencies in contexts with more positive peer norms but greater risk-taking behavior in contexts with more negative peer norms. In contrast, peer norms were not associated with adolescent behavior for individuals with low affective salience within-network intrinsic connectivity. The mentalizing network, cognitive control network, and motivational relevance network were not associated with individual differences in peer influence susceptibility. This study identifies key neural mechanisms underlying differential susceptibility to positive and negative peer influence in early adolescence, with a particular emphasis on the role of affective salience over traditional mentalizing, regulatory, and motivational processes.

Latent functional connectivity underlying multiple brain states.

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain's intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in seven highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared with resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflect state-general connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

Using large, publicly available data sets to study adolescent development: opportunities and challenges.

Adolescence is a period of rapid change, with cognitive, mental wellbeing, environmental biological factors interacting to shape lifelong outcomes. Large, longitudinal phenotypically rich data sets available for reuse (secondary data) have revolutionized the way we study adolescence, allowing the field to examine these unfolding processes across hundreds or even thousands of individuals. Here, we outline the opportunities and challenges associated with such secondary data sets, provide an overview of particularly valuable resources available to the field, and recommend best practices to improve the rigor and transparency of analyses conducted on large, secondary data sets.

Multi-Level Multi-Growth Models: New opportunities for addressing developmental theory using advanced longitudinal designs with planned missingness.

Longitudinal models have become increasingly popular in recent years because of their power to test theoretically derived hypotheses by modeling within-person processes with repeated measures. Growth models constitute a flexible framework for modeling a range of complex trajectories across time in outcomes of interest, including non-linearities and time-varying covariates. However, these models can be expanded to include the effects of multiple growth processes at once on a single outcome. Here, I outline such an extension, showing how multiple growth processes can be modeled as a specific case of the general ability to include time-varying covariates in growth models. I show that this extension of growth models cannot be accomplished by statistical models alone, and that study design plays a crucial role in allowing for proper parameter recovery. I demonstrate these principles through simulations to mimic important theoretical conditions where modeling the effects of multiple growth processes can address developmental theory including, disaggregating the effects of age and practice or treatment in repeated assessments and modeling age- and puberty-related effects during adolescence. I compare how these models behave in two common longitudinal designs, cohort and accelerated, and how planned missingness in observations is key to parameter recovery. I conclude with directions for future substantive research using the method outlined here.

Hormonal and neural correlates of prosocial conformity in adolescents.

The dual hormone hypothesis, which centers on the interaction between testosterone and cortisol on social behavior, offers a compelling framework for examining the role of hormones on the neural correlates of adolescent peer conformity. Expanding on this hypothesis, the present study explored the interaction between testosterone and cortisol via hair concentrations on adolescents' conformity to peers. During fMRI, 136 adolescents (51 % female) ages 11-14 years (M = 12.32; SD = 0.6) completed a prosocial decision-making task. Participants chose how much of their time to donate to charity before and after observing a low- or high-prosocial peer. Conformity was measured as change in behavior pre- to post-observation. High testosterone with low cortisol was associated with greater conformity to high-prosocial peers but not low prosocial peers. Focusing on high prosocial peers, whole-brain analyses indicated greater activation post- vs. pre-observation as a function of high testosterone and low cortisol in regions implicated in social cognition, salience detection, and reward processing: pSTS/TPJ, insula, OFC, and caudate nucleus. Results highlight the relevance of hormones for understanding the neural correlates of adolescents' conformity to prosocial peers.

Longitudinal network re-organization across learning and development.

While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals.

Neural Correlates of Conflicting Social Influence on Adolescent Risk Taking.

Adolescence is often characterized by heightened risk-taking behaviors, which are shaped by social influence from parents and peers. However, little is understood about how adolescents make risky decisions under conflicting influence. The valuation system in the brain may elucidate how adolescents differentially integrate conflicting social information. Twenty-eight adolescents (Mage  = 12.7 years) completed a social influence task during a functional magnetic resonance imaging scan. Behaviorally, adolescents took more risks only when their parent endorsed risky decisions but not when their peers endorsed risky decisions. At the neural level, adolescents showed enhanced vmPFC-striatum functional connectivity when they made risky decisions that followed their parents' risky decisions. Results suggest that parents' decisions may guide youths' risk-taking behavior under conflicting influence.

Neural sensitivity to conflicting attitudes supports greater conformity toward positive over negative influence in early adolescence.

Adolescents often need to reconcile discrepancies between their own attitudes and those of their parents and peers, but the social contexts under which adolescents conform to the attitudes of others, or the neurocognitive processes underlying decisions to conform, remain unexplored. This fMRI study assessed the extent to which early adolescents (n = 39, ages 12-14) conform to their parents' and peers' conflicting attitudes toward different types of behavior (unconstructive and constructive) and in response to different types of influence (negative and positive). Overall, adolescents exhibited low rates of conformity, sticking with their pre-existing attitudes 65 % of the time. When they did conform, adolescents were more likely to conform to their peers' attitudes towards constructive than unconstructive behaviors, exhibiting decreased activation in the ventromedial prefrontal cortex, dorsal anterior cingulate cortex, insula, and inferior frontal gyrus during peer conformity toward constructive over unconstructive behaviors. Adolescents were also more likely to conform when their parents and peers endorsed relatively more positive influence than negative influence, exhibiting increased activation in the temporoparietal junction when considering conforming to negative over positive influence. These results highlight early adolescents' ability to stick with their own opinions when confronted with opposing attitudes and conform selectively based on the social context.

Differential Behavioral and Neural Profiles in Youth With Conduct Problems During Risky Decision-Making.

Neuroimaging work has examined neural processes underlying risk taking in adolescence, yet predominantly in low-risk youth. To determine whether we can extrapolate from current neurobiological models, this functional magnetic resonance imaging study investigated risk taking and peer effects in youth with conduct problems (CP; N = 19) and typically developing youth (TD; N = 25). Results revealed higher real-life risk taking, lower risky decisions, and no peer effects on a risk-taking task in CP youth. CP youth showed greater ventral striatum (VS) activity during safe than risky decisions, whereas TD youth showed greater VS activation during risky decisions. Differential VS activity explained higher real-life risk taking in CP youth. Findings provide preliminary evidence that risk-taking behavior in youth with CD problems is characterized by differential neural patterns.

Maternal emotion socialization in early childhood predicts adolescents' amygdala-vmPFC functional connectivity to emotion faces.

Guided by Eisenberg, Cumberland, and Spinrad's (1998) conceptual framework, we examined multiple components of maternal emotion socialization (i.e., reactions to children's negative emotion, emotion talk, emotional expressiveness) at 33 months of age as predictors of adolescents' amygdala-vmPFC connectivity and amygdala activation when labeling and passively observing angry and happy faces. For angry faces, more positive maternal emotion socialization behaviors predicted (a) less positive amygdala-vmPFC connectivity, which may reflect more mature vmPFC downregulation of the amygdala activation underlying implicit emotion regulation, and (b) more amygdala activation, which may reflect higher sensitivity to others' emotional cues. Associations between negative emotion socialization behaviors and neural responses to angry faces were nonsignificant, and findings for the models predicting neural responses to happy faces showed a less consistent pattern. By expanding Eisenberg et al.'s (1998) framework to consider neural processing of negative emotions, the current findings point toward the potential long-term implications of positive emotion socialization experiences during early childhood for optimal functioning of the amygdala-vmPFC circuitry during adolescence. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Apples to apples? Neural correlates of emotion regulation differences between high- and low-risk adolescents.

Adolescence has been noted as a period of increased risk taking. The literature on normative neurodevelopment implicates aberrant activation of affective and regulatory regions as key to inhibitory failures. However, many of these studies have not included adolescents engaging in high rates of risky behavior, making generalizations to the most at-risk populations potentially problematic. We conducted a comparative study of nondelinquent community (n = 24, mean age = 15.8 years, 12 female) and delinquent adolescents (n = 24, mean age = 16.2 years, 12 female) who completed a cognitive control task during functional magnetic resonance imaging, where behavioral inhibition was assessed in the presence of appetitive and aversive socioaffective cues. Community adolescents showed poorer behavioral regulation to appetitive relative to aversive cues, whereas the delinquent sample showed the opposite pattern. Recruitment of the inferior frontal gyrus, medial prefrontal cortex, and tempoparietal junction differentiated community and high-risk adolescents, as delinquent adolescents showed significantly greater recruitment when inhibiting their responses in the presence of aversive cues, while the community sample showed greater recruitment when inhibiting their responses in the presence of appetitive cues. Accounting for behavioral history may be key in understanding when adolescents will have regulatory difficulties, highlighting a need for comparative research into normative and nonnormative risk-taking trajectories.