What is a representative brain? Neuroscience meets population science

Population-weighted Image-on-scalar Regression Analyses of Large Scale Neuroimaging Data

Recent advances in neuroimaging modeling highlight the importance of accounting for subgroup heterogeneity in population-based neuroscience research through various investigations in large scale neuroimaging data collection. To integrate survey methodology with neuroscience research, we present an imaging data analysis and yield population generalizability with screened subsets of data. The Adolescent Brain Cognitive Development (ABCD) Study has enrolled a large cohort of participants to reflect the individual variation of the U.S. population in adolescent development. To ensure population representation, the ABCD Study has released the base weights. We estimated the associations between brain activities and cognitive performance using the functional Magnetic Resonance Imaging (fMRI) data from the ABCD Study's N-Back working memory task. Notably, the imaging subsample exhibits differences from the baseline cohort in key child characteristics and such discrepancies cannot be addressed simply by applying the ABCD base weights. We developed new population weights specific to the subsample and included the adjusted weights in the image-on-scalar regression model. We validated the approach through synthetic simulations and applications to fMRI data from the ABCD Study. Our findings demonstrate that population weighting adjustments effectively capture active brain areas associated with cognition, enhancing the validity and generalizability of population neuroscience research.

Stuttering: Our Current Knowledge, Research Opportunities, and Ways to Address Critical Gaps

Our understanding of the neurobiological bases of stuttering remains limited, hampering development of effective treatments that are informed by basic science. Stuttering affects more than 5% of all preschool-age children and remains chronic in approximately 1% of adults worldwide. As a condition that affects a most fundamental human ability to engage in fluid and spontaneous verbal communication, stuttering can have substantial psychosocial, occupational, and educational impacts on those who are affected. This article summarizes invited talks and breakout sessions that were held in June 2023 as part of a 2-day workshop sponsored by the US National Institute on Deafness and Other Communication Disorders. The workshop encompassed topics including neurobiology, genetics, speech motor control, cognitive, social, and emotional impacts, and intervention. Updates on current research in these areas were summarized by each speaker, and critical gaps and priorities for future research were raised, and then discussed by participants. Research talks were followed by smaller, moderated breakout sessions intended to elicit diverse perspectives, including on the matter of defining therapeutic targets for stuttering. A major concern that emerged following participant discussion was whether priorities for treatment in older children and adults should focus on targeting core speech symptoms of stuttering, or on embracing effective communication regardless of whether the speaker exhibits overt stuttering. This article concludes with accumulated convergent points endorsed by most attendees on research and clinical priorities that may lead to breakthroughs with substantial potential to contribute to bettering the lives of those living with this complex speech disorder.

Mindful young brains and minds: a systematic review of the neural correlates of mindfulness-based interventions in youth

This systematic narrative review examines neuroimaging studies that investigated the neural correlates of mindfulness-based interventions in youth (ages 0-18). We extracted 13 studies with a total of 467 participants aged 5-18 years from the MEDLINE database on February 21st, 2024. These studies included both typically developing youth and those at risk of developing or recovering from neuropsychiatric disorders. Most studies (76.9%) utilized a pre-post intervention design, with resting-state functional magnetic resonance imaging (fMRI) being the most common imaging modality (46.1%), followed by task-based fMRI (38.4%), diffusion-weighted imaging (15.4%), and structural MRI (7.7%). Despite substantial heterogeneity across study designs and findings, several consistent patterns emerged. Resting-state fMRI studies generally reported increased functional connectivity within and between networks, notably involving the salience network, frontoparietal network, and default mode network. Studies using diffusion-weighted imaging indicated enhancements in white matter microstructural properties, supporting overall connectivity improvements. Several task-based fMRI studies identified decreased activation of the default mode network and heightened reactivity of the salience network during or after mindfulness practice, with real-time neurofeedback further amplifying these effects. While preliminary, the reviewed studies suggest that mindfulness interventions may alter both functional and structural connectivity and activity in youth, potentially bolstering self-regulation and cognitive control. Nonetheless, the variability in methodologies and small sample sizes restricts the generalizability of these results. Future research should prioritize larger and more diverse samples, and standardized mindfulness-based interventions to deepen our understanding of the neural mechanisms underlying mindfulness-based interventions in youth and to optimize their efficacy.

Multi-voxel pattern analysis for developmental cognitive neuroscientists

The current prevailing approaches to analyzing task fMRI data in developmental cognitive neuroscience are brain connectivity and mass univariate task-based analyses, used either in isolation or as part of a broader analytic framework (e.g., BWAS). While these are powerful tools, it is somewhat surprising that multi-voxel pattern analysis (MVPA) is not more common in developmental cognitive neuroscience given its enhanced ability to both probe neural population codes and greater sensitivity relative to the mass univariate approach. Omitting MVPA methods might represent a missed opportunity to leverage a suite of tools that are uniquely poised to reveal mechanisms underlying brain development. The goal of this review is to spur awareness and adoption of MVPA in developmental cognitive neuroscience by providing a practical introduction to foundational MVPA concepts. We begin by defining MVPA and explain why examining multi-voxel patterns of brain activity can aid in understanding the developing human brain. We then survey four different types of MVPA: Decoding, representational similarity analysis (RSA), pattern expression, and voxel-wise encoding models. Each variant of MVPA is presented with a conceptual overview of the method followed by practical considerations and subvariants thereof. We go on to highlight the types of developmental questions that can be answered by MPVA, discuss practical matters in MVPA implementation germane to developmental cognitive neuroscientists, and make recommendations for integrating MVPA with the existing analytic ecosystem in the field.

Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort

Despite the mounting demand for generative population models, their limited generalizability to underrepresented demographic groups hinders widespread adoption in real-world applications. Here we propose a diversity-aware population modeling framework that can guide targeted strategies in public health and education, by estimating subgroup-level effects and stratifying predictions to capture sociodemographic variability. We leverage Bayesian multilevel regression and post-stratification to systematically quantify inter-individual differences in the relationship between socioeconomic status and cognitive development. Post-stratification enhanced the interpretability of model predictions across underrepresented groups by incorporating US Census data to gain additional insights into smaller subgroups in the Adolescent Brain Cognitive Development Study. This ensured that predictions were not skewed by overly heterogeneous or homogeneous representations. Our analyses underscore the importance of combining Bayesian multilevel modeling with post-stratification to validate reliability and provide a more holistic explanation of sociodemographic disparities in our diversity-aware population modeling framework.

Bilingual Vocabulary Development in Mexican Indigenous Infants: The Effects of Language Exposure from Home and Mothers' Language Dominance

This study evaluates how language exposure and mothers' language dominance relate to infants' early bilingual vocabulary development in a low-socioeconomic status (SES) sample from an understudied population: Mexican Indigenous bilinguals. Thirty-two mother-child dyads participated. All mothers were bilingual speakers of Spanish and one of Mexican Indigenous languages, including Zapotec, Mixtec, and Otomi. Infants' (between 16 and 37 months) vocabulary size was estimated in both languages using the Mexican Spanish version of the MacArthur-Bates CDI II. Infants' language exposure, mothers' bilingual profile, and their SES were estimated on numerical scales. The results of Spearman correlations showed infants' vocabulary size in Spanish grows with age, while their vocabulary in the Indigenous language depends on relative language exposure. Mothers' language dominance correlated with Indigenous language exposure and infants' vocabulary size in the Indigenous language. These findings are discussed in the context of early bilingual vocabulary acquisition in speakers of minority languages.

Hemispheric organization of the brain and its prevailing impact on the neuropsychology of aging

Age differences in brain hemispheric asymmetry have figured prominently in the neuropsychology of aging. Here, a broad overview of these empirical and theoretical approaches is provided that dates back to the 1970s and continues to the present day. Methodological advances often brought new evidence to bear on older ideas and promoted the development of new ones. The deficit-focused hypothesis of accelerated right-hemisphere aging is reviewed first, followed by subsequent accounts pertaining to compensation, reserve, and their potential hemispheric underpinnings. Structural and functional neuroimaging reveal important and consistent age-related patterns, including indications of reduced brain asymmetry in older relative to younger adults. While not mutually exclusive, different neuropsychologic theories of aging offer divergent interpretations of such patterns, including age-related reductions in neural specificity (dedifferentiation) and age-related compensatory bilateral recruitment [e.g., Hemispheric Asymmetry Reduction in Older Adults (HAROLD); Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH)]. Further, recent neurobehavioral evidence suggests that the right hemisphere plays a unique role in resisting the neurocognitive effects of aging via brain reserve. Future advances in human cognitive neuroscience, including neurostimulation methods for targeted interventions, along with analytic techniques informed by machine learning promise new insights into the neuropsychology of aging and the role of hemispheric processes in resilience and decline.

The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

Increasing diversity in neuroimaging research: Participant-driven recommendations from a qualitative study of an under-represented sample

Enhancing the generalizability of neuroimaging studies requires actively engaging participants from under-represented communities. This paper leverages qualitative data to outline participant-driven recommendations for incorporating under-represented populations in neuroimaging protocols. Thirty-one participants, who had participated in neuroimaging research or could be eligible for one as part of an ongoing longitudinal study, engaged in semi-structured one-on-one interviews (84 % under-represented ethnic-racial identities and low-income backgrounds). Through thematic analysis, we identified nine relevant research practices from participants' reports, highlighting aspects of their experience that they appreciated and suggestions for improvement: (1) forming a diverse research team comprising members with whom participants can interact as equals; (2) increasing accessibility to research by providing transportation and flexible scheduling; (3) providing family-oriented spaces; (4) enriching the campus visits to include optional on-campus activities to connect with the University; (5) developing safe strategies to accommodate participants with tattoos during the MRI; (6) incorporating engaging and interactive tasks during neuroimaging sessions; (7) providing small gifts, such as a picture of one's brain, in addition to financial compensation; (8) sharing research findings with the research participants; and (9) fostering long-term bidirectional relationships. The findings may be used to develop best practices for enhancing participant diversity in future neuroimaging studies.

The future of neuroscience in developmental psychopathology

Developmental psychopathology started as an intersection of fields and is now a field itself. As we contemplate the future of this field, we consider the ways in which a newer, interdisciplinary field - human developmental neuroscience - can inform, and be informed by, developmental psychopathology. To do so, we outline principles of developmental psychopathology and how they are and/or can be implemented in developmental neuroscience. In turn, we highlight how the collaboration between these fields can lead to richer models and more impactful translation. In doing so, we describe the ways in which models from developmental psychopathology can enrich developmental neuroscience and future directions for developmental psychopathology.

The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research

Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.

Links between socioeconomic position and cognitive and behavioral regulation in adolescence: The role of pubertal development

Adolescent cognitive and behavioral regulation is influenced by multidimensional and multidirectional processes within and across biological and contextual systems that shift throughout development. Key among these influences are distal processes such as early life socioeconomic position (SEP), and proximal processes such as pubertal development, but questions remain concerning how links between SEP, pubertal development, and cognitive and behavioral regulation accumulate and unfold over adolescence. In the current study, and in line with Dr. John Schulenberg's foundational work, direct associations between SEP, puberty, and adolescent cognitive and behavioral regulation were examined; then pubertal timing and tempo were considered as moderators and mediators of links between SEP and adolescent cognitive and behavioral regulation. Data were drawn from the NICHD Study of Early Child Care and Youth Development, a longitudinal study of 970 youth (52% male; 80% White, 13% Black, and 7% another race/ethnicity). Cognitive and behavioral regulation was measured using direct assessments of working memory, planning, risky decision-making, and impulse control at age 15. SEP included maternal education and family income-to-needs and was averaged from birth to 54 months old; estimates of pubertal timing and tempo were derived using logistic growth curve models from age 9 to age 15. SEP was directly associated with cognitive and behavioral regulation. Pubertal development tended to moderate those links, but rarely mediated them. Specifically, socioeconomic disadvantage along with earlier timing or faster tempo tended to be associated with worse cognitive and behavioral regulation. Overall, findings suggest that pubertal timing and tempo may exacerbate existing environmental constraints.

Functional brain network organization and multidomain resilience to neighborhood disadvantage in youth

Though youth living in disadvantaged neighborhoods experience greater risk for poor behavioral and mental health outcomes, many go on to show resilience in the face of adversity. A few recent studies have identified neural markers of resilience in cognitive and affective brain networks, yet the broader network organization supporting resilience in youth remains unknown, particularly in relation to neighborhood disadvantage. Moreover, most studies have defined resilience as the absence of psychopathology, which does not consider growing evidence that resilience also includes positive outcomes across multiple domains (e.g., social, academic). We examined associations between brain network organization and multiple resilience domains in a sample of 708 twins (7-19 years old) recruited from neighborhoods with above-average poverty levels. Graph analysis on functional connectivity data from resting-state and task-based functional magnetic resonance imaging was used to characterize features of intrinsic whole-brain and network-level organization, from which we explored associations with resilience in three domains: psychological, social, and academic. Fewer connections between a brain network involved in self-referential processing (i.e., default mode network) and the subcortical system were associated with greater social resilience. Further, greater whole-brain functional integration (i.e., efficiency) was associated with better psychological resilience among youth with relatively lower levels of cumulative adversity exposure. Alternatively, lower whole-brain efficiency and higher whole-brain robustness to disruption (i.e., assortativity) were associated with greater psychological and social resilience among youth with relatively higher levels of cumulative adversity. These findings advance support for multidimensional resilience models and reveal distinct neural mechanisms supporting resilience to neighborhood disadvantage across specific domains in youth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Resting state functional brain connectivity in child and adolescent psychiatry: where are we now?

Approaching the 30th anniversary of the discovery of resting state functional magnetic resonance imaging (rsfMRI) functional connectivity, we reflect on the impact of this neuroimaging breakthrough on the field of child and adolescent psychiatry. The study of intrinsic functional brain architecture that rsfMRI affords across a wide range of ages and abilities has yielded numerous key insights. For example, we now know that many neurodevelopmental conditions are associated with more widespread circuit alterations across multiple large-scale brain networks than previously suspected. The emergence of population neuroscience and effective data-sharing initiatives have made large rsfMRI datasets publicly available, providing sufficient power to begin to identify brain-based subtypes within heterogeneous clinical conditions. Nevertheless, several methodological and theoretical challenges must still be addressed to fulfill the promises of personalized child and adolescent psychiatry. In particular, incomplete understanding of the physiological mechanisms driving developmental changes in intrinsic functional connectivity remains an obstacle to further progress. Future directions include cross-species and multimodal neuroimaging investigations to illuminate such mechanisms. Data collection and harmonization efforts that span multiple countries and diverse cohorts are urgently needed. Finally, incorporating naturalistic fMRI paradigms such as movie watching should be a priority for future research efforts.

Why experimental variation in neuroimaging should be embraced

In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.

The role (and limits) of developmental neuroscience in determining adolescents' autonomy rights: The case for reproductive and voting rights

Neuroscientific evidence documenting continued neural development throughout adolescence has been leveraged in advocacy for more lenient treatment of adolescents in the criminal justice system. In recent years, developmental science, including neuroscience, has progressed and enabled more nuanced interpretations of what continuing neural development in adolescence likely means functionally for adolescents' capabilities. However, oversimplified interpretations equating continuing neural development to overall "immaturity" are frequently used to make the case that adolescents should have fewer legal rights to make decisions on their own behalf, including regarding reproductive and voting rights. Here we address ongoing debates about adolescents' autonomy rights and whether such rights should be expanded or restricted. We review extant neuroscientific and developmental research that can inform these debates. We call for: (1) a more nuanced application of developmental neuroscience to specific rights issues in specific contexts; (2) additional research designed to inform our understanding of the developmental benefits or harms of rights-based policies on young people over time; and (3) the grounding of developmental neuroscientific research on adolescents within a human rights framework. We offer suggestions to developmental and neuroscience scholars on how to discuss the science of adolescent development with those seeking guidance in their design of law and policy.

An estimate of the longitudinal pace of aging from a single brain scan predicts dementia conversion, morbidity, and mortality

To understand how aging affects functional decline and increases disease risk, it is necessary to develop accurate and reliable measures of how fast a person is aging. Epigenetic clocks measure aging but require DNA methylation data, which many studies lack. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain MRI: the Dunedin Pace of Aging Calculated from NeuroImaging or DunedinPACNI. Exporting this measure to the Alzheimer's Disease Neuroimaging Initiative and UK Biobank neuroimaging datasets revealed that faster DunedinPACNI predicted participants' cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed the expected socioeconomic health gradient. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.

Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity

Importance

Adverse childhood experiences are pervasive and heterogeneous, with potential lifelong consequences for psychiatric morbidity and brain health. Existing research does not capture the complex interplay of multiple adversities, resulting in a lack of precision in understanding their associations with neural function and mental health.

Objectives

To identify distinct childhood adversity profiles and examine their associations with adolescent mental health and brain connectivity.

Design, Setting, and Participants

This population-based birth cohort used data for children who were born in 20 large US cities between 1998 and 2000 and participated in the Future Families and Child Well-Being Study. Families were interviewed when children were born and at ages 1, 3, 5, 9, and 15 years. At age 15 years, neuroimaging data were collected from a subset of these youths. Data were collected from February 1998 to April 2017. Analyses were conducted from March to December 2023.

Exposures

Latent profiles of childhood adversity, defined by family and neighborhood risks across ages 0 to 9 years.

Main Outcomes and Measures

Internalizing and externalizing symptoms at age 15 years using parent- and youth-reported measures. Profile-specific functional magnetic resonance imaging connectivity across the default mode network (DMN), salience network (SN), and frontoparietal network (FPN).

Results

Data from 4210 individuals (2211 [52.5%] male; 1959 [46.5%] Black, 1169 [27.7%] Hispanic, and 786 [18.7%] White) revealed 4 childhood adversity profiles: low-adversity (1230 individuals [29.2%]), medium-adversity (1973 [46.9%]), high-adversity (457 [10.9%]), and high maternal depression (MD; 550 [13.1%]). High-adversity, followed by MD, profiles had the highest symptoms. Notably, internalizing symptoms did not differ between these 2 profiles (mean difference, 0.11; 95% CI, -0.03 to 0.26), despite the MD profile showing adversity levels most similar to the medium-adversity profile. In the neuroimaging subsample of 167 individuals (91 [54.5%] female; 128 [76.6%] Black, 11 [6.6%] Hispanic, and 20 [12.0%] White; mean [SD] age, 15.9 [0.5] years), high-adversity and MD profiles had the highest DMN density relative to other profiles (F(3,163) = 11.14; P < .001). The high-adversity profile had lower SN density relative to the low-adversity profile (mean difference, -0.02; 95% CI, -0.04 to -0.003) and the highest FPN density among all profiles (F(3,163) = 18.96; P < .001). These differences were specific to brain connectivity during an emotion task, but not at rest.

Conclusions and Relevance

In this cohort study, children who experienced multiple adversities, or only elevated MD, had worse mental health and different neural connectivity in adolescence. Interventions targeting multiple risk factors, with a focus on maternal mental health, could produce the greatest benefits.

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities' diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Why and how to collect representative study samples in educational neuroscience research

BACKGROUND

Educational neuroscience research, which investigates the neurobiological mechanisms of learning, has historically incorporated samples drawn mostly from white, middle-class, and/or suburban populations. However, sampling in research without attending to representation can lead to biased interpretations and results that are less generalizable to an intended target population. Prior research revealing differences in neurocognitive outcomes both within- and across-groups further suggests that such practices may obscure significant effects with practical implications.

BARRIERS

Negative attitudes among historically marginalized communities, stemming from historical mistreatment, biased research outcomes, and implicit or explicit attitudes among research teams, can hinder diverse participation. Qualities of the research process including language requirements, study locations, and time demands create additional barriers.

SOLUTIONS

Flexible data collection approaches, community engaugement, and transparent reporting could build trust and enhance sampling diversity. Longer-term solutions include prioritizing research questions relevant to marginalized communities, increasing workforce diversity, and detailed reporting of sample demographics. Such concerted efforts are essential for robust educational neuroscience research to maximize positive impacts broadly across learners.

Exposure to community violence as a mechanism linking neighborhood socioeconomic disadvantage and neural responses to reward

A growing literature links socioeconomic disadvantage and adversity to brain function, including disruptions in reward processing. Less research has examined exposure to community violence (ECV) as a specific adversity related to differences in reward-related brain activation, despite the prevalence of community violence exposure for those living in disadvantaged contexts. The current study tested whether ECV was associated with reward-related ventral striatum (VS) activation after accounting for familial factors associated with differences in reward-related activation (e.g. parenting and family income). Moreover, we tested whether ECV is a mechanism linking socioeconomic disadvantage to reward-related activation in the VS. We utilized data from 444 adolescent twins sampled from birth records and residing in neighborhoods with above-average levels of poverty. ECV was associated with greater reward-related VS activation, and the association remained after accounting for family-level markers of disadvantage. We identified an indirect pathway in which socioeconomic disadvantage predicted greater reward-related activation via greater ECV, over and above family-level adversity. These findings highlight the unique impact of community violence exposure on reward processing and provide a mechanism through which socioeconomic disadvantage may shape brain function.

Peace through health: traditional medicine meditation in the prevention of collective stress, violence, and war

In the midst of global armed conflicts, notably the Israel-Hamas and Ukraine-Russia wars, there is an urgent need for innovative public health strategies in peacebuilding. The devastating impact of wars, including mortality, injury, disease, and the diversion of healthcare resources, necessitates effective and durable interventions. This perspective aligns with WHO recommendations and examines the role of evidence-based meditation from Ayurveda and Yoga in public health to mitigate collective stress and prevent collective violence and war. It highlights the Transcendental Meditation program, recognized for reducing stress, with contemporary evidence supporting its effectiveness in mental health, mind-body disorders, cardiovascular disease, and public health. Empirical studies with cross-cultural replications indicate that these Traditional Medicine meditation practices can reduce collective stress and prevent collective violence and war activity while improving quality of life. The mechanisms of group meditation in mitigating collective violence are explored through public health models, cognitive neuroscience, population neuroscience, quantum physics principles, and systems medicine. This perspective suggests that Transcendental Meditation and the advanced TM-Sidhi program, as a component of Traditional Medicine, can provide a valuable platform for enhancing societal well-being and peace by addressing brain-based factors fundamental to collective stress and violence.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION

Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.

The role of research design in the reproducibility of L1 and L2 language networks: A review of bilingual neuroimaging meta-analyses

Meta-analyses are a method by which to increase the statistical power and generalizability of neuroimaging findings. In the neurolinguistics literature, meta-analyses have the potential to substantiate hypotheses about L1 and L2 processing networks and to reveal differences between the two that may escape detection in individual studies. Why then is there so little consensus between the reported findings of even the most recently published and most highly powered meta-analyses? Limitations in the literature, such as the absence of a common method to define and measure descriptive categories (e.g., proficiency level, degree of language exposure, age of acquisition, etc.) are often cited. An equally plausible explanation lies in the technical details of how individual meta-analyses are conducted. This paper provides a review of recent meta-analyses, with a discussion of their methodological choices and the possible effect those choices may have on the reported findings.

Population Neuroscience: Understanding Concepts of Generalizability and Transportability and Their Application to Improving the Public's Health

In population neuroscience, samples are not often selected with equal or known probability from an underlying population of interest; in other words, samples are not often formally representative of a specified underlying population. This chapter provides an overview of an epidemiological approach to considering the implications of selective participation on the value of our results for population health. We discuss definitions of generalizability and transportability, given the growing recognition that generalizability and transportability are central for interpreting data that are aiming to be population-based. We provide evidence that differences in the prevalence of effect measure modifiers between a study sample and a target population will lead to a lack of generalizability and transportability. We provide an example of an association between a poly-genetic risk score and depression, showing how an internally valid association can differ based on the prevalence of effect measure modifiers. We show that when estimating associations, inferences from a study sample to a population can depend on clearly defining a target population. Given that representative sampling from explicitly defined target populations may not be feasible or realistic in many situations, especially given the sample sizes needed for statistical power for many exposures of interest (and especially when interactions are being tested), researchers should be well versed in tools available to enhance the interpretability of samples regarding target populations.

Racial, Ethnic, and Geographic Diversity in Population Neuroscience

In this chapter, we consider lack of racial, ethnic, and geographic diversity in research studies from a public health perspective in which representation of a target population is critical. We review the state of the research field with respect to racial, ethnic, and geographic diversity in study participants. We next focus on key factors which can arise from the lack of diversity and can negatively impact external validity. Finally, we argue that the public's health, and future research, will ultimately be served by approaches from both recruitment and representation science and population neuroscience, and we close with recommendations from these two fields to improve diversity in studies.

A research agenda for understanding how social inequality is linked to brain structure and function

Consistent evidence documents powerful effects of social inequality on health, well-being and academic achievement. Yet research on whether social inequality may also be linked to brain structure and function has, until recently, been rare. Here we describe three methodological approaches that can be used to study this question-single site, single study; multi-site, single study; and spatial meta-analysis. We review empirical work that, using these approaches, has observed associations between neural outcomes and structural measures of social inequality-including structural stigma, community-level prejudice, gender inequality, neighbourhood disadvantage and the generosity of the social safety net for low-income families. We evaluate the relative strengths and limitations of these approaches, discuss ethical considerations and outline directions for future research. In doing so, we advocate for a paradigm shift in cognitive neuroscience that explicitly incorporates upstream structural and contextual factors, which we argue holds promise for uncovering the neural correlates of social inequality.

Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy

Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.

Biomarker adoption in developmental science: A data-driven modelling of trends from 90 biomarkers across 20 years

Developmental scientists have adopted numerous biomarkers in their research to better understand the biological underpinnings of development, environmental exposures, and variation in long-term health. Yet, adoption patterns merit investigation given the substantial resources used to collect, analyse, and train to use biomarkers in research with infants and children. We document trends in use of 90 biomarkers between 2000 and 2020 from approximately 430,000 publications indexed by the Web of Science. We provide a tool for researchers to examine each of these biomarkers individually using a data-driven approach to estimate the biomarker growth trajectory based on yearly publication number, publication growth rate, number of author affiliations, National Institutes of Health dedicated funding resources, journal impact factor, and years since the first publication. Results indicate that most biomarkers fit a "learning curve" trajectory (i.e., experience rapid growth followed by a plateau), though a small subset decline in use over time.

Brain morphological variability between whites and African Americans: the importance of racial identity in brain imaging research

In a segregated society, marked by a historical background of inequalities, there is a consistent under-representation of ethnic and racial minorities in biomedical research, causing disparities in understanding genetic and acquired diseases as well as in the effectiveness of clinical treatments affecting different groups. The repeated inclusion of small and non-representative samples of the population in neuroimaging research has led to generalization bias in the morphological characterization of the human brain. A few brain morphometric studies between Whites and African Americans have reported differences in orbitofrontal volumetry and insula cortical thickness. Nevertheless, these studies are mostly conducted in small samples and populations with cognitive impairment. For this reason, this study aimed to identify brain morphological variability due to racial identity in representative samples. We hypothesized that, in neurotypical young adults, there are differences in brain morphometry between participants with distinct racial identities. We analyzed the Human Connectome Project (HCP) database to test this hypothesis. Brain volumetry, cortical thickness, and cortical surface area measures of participants identified as Whites (n = 338) or African Americans (n = 56) were analyzed. Non-parametrical permutation analysis of covariance between these racial identity groups adjusting for age, sex, education, and economic income was implemented. Results indicated volumetric differences in choroid plexus, supratentorial, white matter, and subcortical brain structures. Moreover, differences in cortical thickness and surface area in frontal, parietal, temporal, and occipital brain regions were identified between groups. In this regard, the inclusion of sub-representative minorities in neuroimaging research, such as African American persons, is fundamental for the comprehension of human brain morphometric diversity and to design personalized clinical brain treatments for this population.

Component-specific developmental trajectories of ERP indices of cognitive control in early childhood

Early childhood is characterized by robust developmental changes in cognitive control. However, our understanding of intra-individual change in neural indices of cognitive control during this period remains limited. Here, we examined developmental changes in event-related potential (ERP) indices of cognitive control from preschool through first grade, in a large and diverse sample of children (N = 257). We recorded ERPs during a visual Go/No-Go task. N2 and P3b mean amplitudes were extracted from the observed waveforms (Go and No-Go) and the difference wave (No-Go minus Go, or ∆). Latent growth curve modeling revealed that while N2 Go and No-Go amplitudes showed no linear change, P3b Go and No-Go amplitudes displayed linear decreases in magnitude (became less positive) over time. ∆N2 amplitude demonstrated a linear increase in magnitude (became more negative) over time whereas ∆P3b amplitude was more positive in kindergarten compared to preschool. Younger age in preschool predicted greater rates of change in ∆N2 amplitude, and higher maternal education predicted larger initial P3b Go and No-Go amplitudes in preschool. Our findings suggest that observed waveforms and difference waves are not interchangeable for indexing neurodevelopment, and the developmental trajectories of different ERP indices of cognitive control are component-specific in early childhood.

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.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Fetal Frontolimbic Connectivity Prospectively Associates With Aggression in Toddlers

Background

Aggression is a major public health concern that emerges early in development and lacks optimized treatment, highlighting need for improved mechanistic understanding regarding the etiology of aggression. The present study leveraged fetal resting-state functional magnetic resonance imaging to identify candidate neurocircuitry for the onset of aggressive behaviors before symptom emergence.

Methods

Pregnant mothers were recruited during the third trimester of pregnancy to complete a fetal resting-state functional magnetic resonance imaging scan. Mothers subsequently completed the Child Behavior Checklist to assess child aggression at 3 years postpartum (n = 79). Independent component analysis was used to define frontal and limbic regions of interest.

Results

Child aggression was not related to within-network connectivity of subcortical limbic regions or within-medial prefrontal network connectivity in fetuses. However, weaker functional coupling between the subcortical limbic network and medial prefrontal network in fetuses was prospectively associated with greater maternal-rated child aggression at 3 years of age even after controlling for maternal emotion dysregulation and toddler language ability. We observed similar, but weaker, associations between fetal frontolimbic functional connectivity and toddler internalizing symptoms.

Conclusions

Neural correlates of aggressive behavior may be detectable in utero, well before the onset of aggression symptoms. These preliminary results highlight frontolimbic connections as potential candidate neurocircuitry that should be further investigated in relation to the unfolding of child behavior and psychiatric risk.

In models we trust: preregistration, large samples, and replication may not suffice

Despite discussions about the replicability of findings in psychological research, two issues have been largely ignored: selection mechanisms and model assumptions. Both topics address the same fundamental question: Does the chosen statistical analysis tool adequately model the data generation process? In this article, we address both issues and show, in a first step, that in the face of selective samples and contrary to common practice, the validity of inferences, even when based on experimental designs, can be claimed without further justification and adaptation of standard methods only in very specific situations. We then broaden our perspective to discuss consequences of violated assumptions in linear models in the context of psychological research in general and in generalized linear mixed models as used in item response theory. These types of misspecification are oftentimes ignored in the psychological research literature. It is emphasized that the above problems cannot be overcome by strategies such as preregistration, large samples, replications, or a ban on testing null hypotheses. To avoid biased conclusions, we briefly discuss tools such as model diagnostics, statistical methods to compensate for selectivity and semi- or non-parametric estimation. At a more fundamental level, however, a twofold strategy seems indispensable: (1) iterative, cumulative theory development based on statistical methods with theoretically justified assumptions, and (2) empirical research on variables that affect (self-) selection into the observed part of the sample and the use of this information to compensate for selectivity.

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

Crowdsourced EEG experiments: A proof of concept for remote EEG acquisition using EmotivPRO Builder and EmotivLABS

The development of online research platforms has made data collection more efficient and representative of populations. However, these benefits have not been available for use with cognitive neuroscience tools such as electroencephalography (EEG). In this study, we introduce an approach for remote EEG data collection. We demonstrate how an experiment can be built via the EmotivPRO Builder and deployed to the EmotivLABS website where it can be completed by participants who own EMOTIV EEG headsets. To demonstrate the data collection technique, we collected EEG while participants engaged in a resting state task where participants sat with their eyes open and then eyes closed for 2 min each. We observed a significant difference in alpha power between the two conditions thereby demonstrating the well-known alpha suppression effect. Thus, we demonstrate that EEG data collection, particularly for frequency domain analysis, can be successfully conducted online.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.

Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity

BACKGROUND

Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences.

METHODS

Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time.

RESULTS

Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .237, p = .021).

CONCLUSIONS

A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.

Antisocial behavior is associated with reduced frontoparietal activity to loss in a population-based sample of adolescents

BACKGROUND

Adolescent antisocial behavior (AB) is a public health concern due to the high financial and social costs of AB on victims and perpetrators. Neural systems involved in reward and loss processing are thought to contribute to AB. However, investigations into these processes are limited: few have considered anticipatory and consummatory components of reward, response to loss, nor whether associations with AB may vary by level of callous-unemotional (CU) traits.

METHODS

A population-based community sample of 128 predominantly low-income youth (mean age = 15.9 years; 42% male) completed a monetary incentive delay task during fMRI. A multi-informant, multi-method latent variable approach was used to test associations between AB and neural response to reward and loss anticipation and outcome and whether CU traits moderated these associations.

RESULTS

AB was not associated with neural response to reward but was associated with reduced frontoparietal activity during loss outcomes. This association was moderated by CU traits such that individuals with higher levels of AB and CU traits had the largest reductions in frontoparietal activity. Co-occurring AB and CU traits were also associated with increased precuneus response during loss anticipation.

CONCLUSIONS

Findings indicate that AB is associated with reduced activity in brain regions involved in cognitive control, attention, and behavior modification during negative outcomes. Moreover, these reductions are most pronounced in youth with co-occurring CU traits. These findings have implications for understanding why adolescents involved in AB continue these behaviors despite severe negative consequences (e.g. incarceration).

Editorial: Shifting the Landscape of Child Psychiatric Epidemiology

The results of recent surveys that show high levels of symptoms of anxiety and depression have generated widespread concern about the mental health of US youth. Although such increases and their causes require immediate action, these symptoms alone do not indicate an epidemic of mental disorders in the US because they do not reflect mental disorders that are characterized by protracted duration and educational or social impairment. Unfortunately, there are no recent comparable data on the full range of common mental disorders. (e.g., Anxiety, Attention Deficit Hyperactivity Disorder, Major Depression, etc.) in nationally representative samples of US youth to provide a baseline for the reported increased distress in recent surveys. Therefore, we must rely on indirect information derived from surveys of subsets of symptoms and behaviors or of restricted age groups, and web-based samples with unknown biases and limited generalizability. This editorial describes how the findings from a recent report of prevalence of mental disorders in 9-10-year-old youths from the ABCD study can contribute to the national profile of mental disorders in youth. We highlight the need to address the lack of systematic data on youth emotional and behavioral disorders in the US through concerted efforts to coordinate the multi-agency sources of data on youth mental health. This will require harmonization of sampling and methods, informed application of internet-based tools based on systematic sampling and non-probability sampling methods and promotion of efforts to bridge the gap between population-based research and interventions at both the societal and individual levels.

Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology

Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.