Sample composition alters associations between age and brain structure

Modality-level obstacles and initiatives to improve representation in fetal, infant, and toddler neuroimaging research samples

Fetal, infant, and toddler (FIT) neuroimaging researchers study early brain development to gain insights into neurodevelopmental processes and identify early markers of neurobiological vulnerabilities to target for intervention. However, the field has historically excluded people from global majority countries and from marginalized communities in FIT neuroimaging research. Inclusive and representative samples are essential for generalizing findings across neuroimaging modalities, such as magnetic resonance imaging, magnetoencephalography, electroencephalography, functional near-infrared spectroscopy, and cranial ultrasonography. These FIT neuroimaging techniques pose unique and overlapping challenges to equitable representation in research through sampling bias, technical constraints, limited accessibility, and insufficient resources. The present article adds to the conversation around the need to improve inclusivity by highlighting modality-specific historical and current obstacles and ongoing initiatives. We conclude by discussing tangible solutions that transcend individual modalities, ultimately providing recommendations to promote equitable FIT neuroscience.

Dynamic Field Theory of Executive Function: Identifying Early Neurocognitive Markers

In this Monograph, we explored neurocognitive predictors of executive function (EF) development in a cohort of children followed longitudinally from 30 to 54 months of age. We tested predictions of a dynamic field model that explains development in a benchmark measure of EF development, the dimensional change card sort (DCCS) task. This is a rule-use task that measures children's ability to switch between sorting cards by shape or color rules. A key developmental mechanism in the model is that dimensional label learning drives EF development. Data collection began in February 2019 and was completed in April 2022 on the Knoxville campus of the University of Tennessee. Our cohort included 20 children (13 female) all of whom were White (not Hispanic/Latinx) from an urban area in southern United States, and the sample annual family income distribution ranged from low to high (most families falling between $40,000 and 59,000 per year (note that we address issues of generalizability and the small sample size throughout the monograph)). We tested the influence of dimensional label learning on DCCS performance by longitudinally assessing neurocognitive function across multiple domains at 30 and 54 months of age. We measured dimensional label learning with comprehension and production tasks for shape and color labels. Simple EF was measured with the Simon task which required children to respond to images of a cat or dog with a lateralized (left/right) button press. Response conflict was manipulated in this task based on the spatial location of the stimulus which could be neutral (central), congruent, or incongruent with the spatial lateralization of the response. Dimensional understanding was measured with an object matching task requiring children to generalize similarity between objects that matched within the dimensions of color or shape. We first identified neural measures associated with performance and development on each of these tasks. We then examined which of these measures predicted performance on the DCCS task at 54 months. We measured neural activity with functional near-infrared spectroscopy across bilateral frontal, temporal, and parietal cortices. Our results identified an array of neurocognitive mechanisms associated with development within each domain we assessed. Importantly, our results suggest that dimensional label learning impacts the development of EF. Neural activation in left frontal cortex during dimensional label production at 30 months of age predicted EF performance at 54 months of age. We discussed these results in the context of efforts to train EF with broad transfer. We also discussed a new autonomy-centered EF framework. The dynamic field model on which we have motivated the current research makes decisions autonomously and various factors can influence the types of decisions that the model makes. In this way, EF is a property of neurocognitive dynamics, which can be influenced by individual factors and contextual effects. We also discuss how this conceptual framework can generalize beyond the specific example of dimensional label learning and DCCS performance to other aspects of EF and how this framework can help to understand how EF unfolds in unique individual, cultural, and contextual factors. Measures of EF during early childhood are associated with a wide range of development outcomes, including academic skills and quality of life. The hope is that broad aspects of development can be improved by implementing interventions aimed at facilitating EF development. However, this promise has been largely unrealized. Previous work on EF development has been limited by a focus on EF components, such as inhibition, working memory, and switching. Similarly, intervention research has focused on practicing EF tasks that target these specific components of EF. While performance typically improves on the practiced task, improvement rarely generalizes to other EF tasks or other developmental outcomes. The current work is unique because we looked beyond EF itself to identify the lower-level learning processes that predict EF development. Indeed, the results of this study identify the first learning mechanism involved in the development of EF. Although the work here provides new targets for interventions in future work, there are also important limitations. First, our sample is not representative of the underlying population of children in the United States under the age of 5. This is a problem in much of the existing developmental cognitive neuroscience research. We discussed challenges to the generalizability of our findings to the population at large. This is particularly important given that our theory is largely contextual, suggesting that children's unique experiences with learning labels for visual dimensions will impact EF development. Second, we identified a learning mechanism to target in future intervention research; however, it is not clear whether such interventions would benefit all children or how to identify children who would benefit most from such interventions. We also discuss prospective lines of research that can address these limitations, such as targeting families that are typically underrepresented in research, expanding longitudinal studies to examine longer term outcomes such as school-readiness and academic skills, and using the dynamic field (DF) model to systematically explore how exposure to objects and labels can optimize the neural representations underlying dimensional label learning. Future work remains to understand how such learning processes come to define the contextually and culturally specific skills that emerge over development and how these skills lay the foundation for broad developmental trajectories.

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.

Early Exposure to Deprivation or Threat Moderates Expected Associations Between Neural Structure and Age in Adolescent Girls

Childhood adversity (CA) is associated with increased risk of negative health outcomes. Research implicates brain structure following CA as a key mechanism of this risk, and recent models suggest different forms of adversity differentially impact neural structure as a function of development (accelerated or attenuated development). Employing the Dimensional Model of Adversity and Psychopathology, we examined whether deprivation and threat differentially impact age-related change in cortical thickness, cortical surface area, and subcortical structure volume, using whole-brain and region of interest analyses (N = 135). In youth without CA, age predicted less surface area across adolescence, consistent with normative data. However, for adolescents with more deprivation exposure, as age increased there was attenuated surface area decreases in the orbitofrontal and superior-parietal cortex, regions recruited for higher-order cognition. Further, for those with more threat exposure, as age increased surface area increased in the inferior-temporal and parietal cortex, regions recruited in socio-emotional tasks. These novel findings extend work examining the impact of dimensions of adversity at a single-age and broaden current conceptualizations of how adversity might impact developmental timing.

Ventral striatal-cingulate resting-state functional connectivity in healthy adolescents relates to later depression symptoms in adulthood

BACKGROUND

Depression is a significant public health concern. Identifying biopsychosocial risk factors for depression is important for developing targeted prevention. Studies have demonstrated that blunted striatal activation during reward processing is a risk factor for depression; however, few have prospectively examined whether adolescent reward-related resting-state functional connectivity (rsFC) predicts depression symptoms in adulthood and how this relates to known risk factors (e.g., childhood trauma).

METHODS

At baseline, 66 adolescents (mean age = 14.7, SD = 1.4, 68 % female) underwent rsFC magnetic resonance imaging and completed the Children's Depression Inventory (CDI). At follow-up (mean time between adolescent scan and adult follow-up = 10.1 years, SD = 1.6, mean adult age = 24.8 years, SD = 1.7), participants completed the Childhood Trauma Questionnaire (CTQ) and Beck Depression Inventory- Second Edition (BDI-2). Average rsFC was calculated between nodes in mesocorticolimbic reward circuitry: ventral striatum (VS), rostral anterior cingulate cortex (rACC), medial orbitofrontal cortex, and ventral tegmental area. Linear regressions assessed associations between rsFC, BDI-2, and CTQ, controlling for adolescent CDI, sex assigned at birth, and scan age (Bonferroni corrected).

RESULTS

Greater childhood trauma was associated with higher adulthood depression symptoms. Stronger VS-rACC rsFC during adolescence was associated with greater depression symptoms in adulthood and greater childhood trauma.

LIMITATIONS

The small sample size, limited depression severity, and seed-based approach are limitations.

CONCLUSIONS

The associations between adolescent striatal-cingulate rsFC and childhood trauma and adult depression symptoms suggest this connectivity may be an early neurobiological risk factor for depression and that early life experience plays an important role. Increased VS-rACC connectivity may represent an over-regulatory response on the striatum, commonly reported in depression, and warrants further investigation.

OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain

This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1- weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP-T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull-stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP-T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real-world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP-T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP-T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging.

Glycemic Control, Cognitive Aging, and Impairment Among Diverse Hispanic/Latino Individuals: Study of Latinos- Investigation of Neurocognitive Aging (Hispanic Community Health Study/Study of Latinos)

OBJECTIVE

Hispanic/Latino individuals in the U.S. have the highest prevalence of undiagnosed and untreated diabetes and are at increased risk for cognitive impairment. In this study, we examine glycemic control in relation to cognitive aging and impairment in a large prospective cohort of middle-aged and older Hispanic/Latino individuals of diverse heritages.

RESEARCH DESIGN AND METHODS

Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) is a Hispanic Community Health Study/Study of Latinos (HCHS/SOL) ancillary study. HCHS/SOL is a multisite (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA), probability sampled prospective cohort study. SOL-INCA enrolled 6,377 diverse Hispanic/Latino individuals aged 50 years and older (2016-2018). The primary outcomes were cognitive function, 7-year cognitive decline, and mild cognitive impairment (MCI). The primary glycemia exposure variables were measured from fasting blood samples collected at HCHS/SOL visit 1 (2008-2011).

RESULTS

Visit 1 mean age was 56.5 years ± 8.2 SD, and the average glycosylated hemoglobin A1C (HbA1c) was 6.12% (43.5 ± 14.6 mmol/mol). After covariate adjustment, higher HbA1c was associated with accelerated 7-year global (b = -0.045; 95% CI -0.070; -0.021; in z score units) and executive cognitive decline and a higher prevalence of MCI (odds ratio 1.20; 95% CI 1.11; 1.29).

CONCLUSIONS

Elevated HbA1c levels were associated with 7-year executive cognitive decline and increased MCI risk among diverse middle-aged and older Hispanic/Latino individuals. Our findings indicate that poor glycemic control in midlife may pose significant risks for cognitive decline and MCI later in life among Hispanic/Latino individuals of diverse heritages.

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.

Attention-deficit hyperactivity disorder symptoms and brain morphology: Addressing potential selection bias with inverse probability weighting

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9-11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex

Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.

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.

A preliminary investigation into cortical structural alterations in adolescents with nonsuicidal self-injury

The structural neural correlates underlying youth nonsuicidal self-injury (NSSI) warrant further exploration. Few studies have explored the association between NSSI and brain structure in adolescence, and no studies have investigated differences in the relation between age and brain structure in youth with NSSI. This preliminary investigation examined associations between NSSI history, age, and cortical structure using magnetic resonance imaging in adolescent girls (N=100, Mage=13.4 years) at increased risk for psychopathology. We conducted whole-brain analyses to investigate the associations between age and cortical structure, NSSI history and cortical structure, and NSSI history as a moderator of the association between age and cortical structure. Results suggested that age was associated with less cortical thickness and surface area in the left and right prefrontal, temporal, and parietal cortex. NSSI history was associated with less left insula and left inferior parietal cortex cortical surface area. Among adolescents with NSSI history, older age predicted greater left inferior parietal cortex surface area and was not associated with left precentral cortex surface area. Among adolescents without NSSI history, older age predicted smaller surface areas as expected with the typical trajectory of neurodevelopment. Overall, our results suggest differences in cortical surface area development in adolescents with NSSI history.

A canonical trajectory of executive function maturation from adolescence to adulthood

Theories of human neurobehavioral development suggest executive functions mature from childhood through adolescence, underlying adolescent risk-taking and the emergence of psychopathology. Investigations with relatively small datasets or narrow subsets of measures have identified general executive function development, but the specific maturational timing and independence of potential executive function subcomponents remain unknown. Integrating four independent datasets (N = 10,766; 8-35 years old) with twenty-three measures from seventeen tasks, we provide a precise charting, multi-assessment investigation, and replication of executive function development from adolescence to adulthood. Across assessments and datasets, executive functions follow a canonical non-linear trajectory, with rapid and statistically significant development in late childhood to mid-adolescence (10-15 years old), before stabilizing to adult-levels in late adolescence (18-20 years old). Age effects are well captured by domain-general processes that generate reproducible developmental templates across assessments and datasets. Results provide a canonical trajectory of executive function maturation that demarcates the boundaries of adolescence and can be integrated into future studies.

Childhood socioeconomic status and the pace of structural neurodevelopment: accelerated, delayed, or simply different?

Socioeconomic status (SES) is associated with children's brain and behavioral development. Several theories propose that early experiences of adversity or low SES can alter the pace of neurodevelopment during childhood and adolescence. These theories make contrasting predictions about whether adverse experiences and low SES are associated with accelerated or delayed neurodevelopment. We contextualize these predictions within the context of normative development of cortical and subcortical structure and review existing evidence on SES and structural brain development to adjudicate between competing hypotheses. Although none of these theories are fully consistent with observed SES-related differences in brain development, existing evidence suggests that low SES is associated with brain structure trajectories more consistent with a delayed or simply different developmental pattern than an acceleration in neurodevelopment.

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Physical symptoms and brain morphology: a population neuroimaging study in 12,286 pre-adolescents

Physical symptoms, also known as somatic symptoms, are those for which medical examinations do not reveal a sufficient underlying root cause (e.g., pain and fatigue). The extant literature of the neurobiological underpinnings of physical symptoms is largely inconsistent and primarily comprises of (clinical) case-control studies with small sample sizes. In this cross-sectional study, we studied the association between dimensionally measured physical symptoms and brain morphology in pre-adolescents from two population-based cohorts; the Generation R Study (n = 2649, 10.1 ± 0.6 years old) and ABCD Study (n = 9637, 9.9 ± 0.6 years old). Physical symptoms were evaluated using continuous scores from the somatic complaints syndrome scale from the parent-reported Child Behavior Checklist (CBCL). High-resolution structural magnetic resonance imaging (MRI) was collected using 3-Tesla MRI systems. Linear regression models were fitted for global brain metrics (cortical and subcortical grey matter and total white matter volume) and surface-based vertex-wise measures (surface area and cortical thickness). Results were meta-analysed. Symptoms of anxiety/depression were studied as a contrasting comorbidity. In the meta-analyses across cohorts, we found negative associations between physical symptoms and surface area in the (i) left hemisphere; in the lateral orbitofrontal cortex and pars triangularis and (ii) right hemisphere; in the pars triangularis, the pars orbitalis, insula, middle temporal gyrus and caudal anterior cingulate cortex. However, only a subset of regions (left lateral orbitofrontal cortex and right pars triangularis) were specifically associated with physical symptoms, while others were also related to symptoms of anxiety/depression. No significant associations were observed for cortical thickness. This study in preadolescents, the most representative and well-powered to date, showed that more physical symptoms are modestly related to less surface area of the prefrontal cortex mostly. While these effects are subtle, future prospective research is warranted to understand the longitudinal relationship of physical symptoms and brain changes over time. Particularly, to elucidate whether physical symptoms are a potential cause or consequence of distinct neurodevelopmental trajectories.

A Mega-analytic Study of White Matter Microstructural Differences Across 5 Cohorts of Youths With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

While attention-deficit/hyperactivity disorder (ADHD) has been associated with differences in the structural connections formed by the brain's white matter tracts, studies of such differences have yielded inconsistent findings, likely reflecting small sample sizes. Thus, we conducted a mega-analysis on in vivo measures of white matter microstructure obtained through diffusion tensor imaging of more than 6000 participants from 5 cohorts.

METHODS

In a mega-analysis, linear mixed models were used to test for associations between the fractional anisotropy of 42 white matter tracts and ADHD traits and diagnosis. Contrasts were made against measures of mood, anxiety, and other externalizing problems.

RESULTS

Overall, 6993 participants (ages 6-18 years, mean age 10.62 years [SD 1.99]; 3368 girls, 3625 boys; 764 African American, 4146 non-Hispanic White, and 2083 other race/ethnicities) had measures of ADHD and other emotional/behavioral symptoms (N = 6933) and/or enough clinical data to allow a diagnosis of ADHD (n = 951) or its absence (n = 4884). Both the diagnosis and symptoms of ADHD were associated with lower fractional anisotropy of the inferior longitudinal and left uncinate fasciculi (at a false discovery rate-adjusted p < .05). Associated effect sizes were small (the strongest association with ADHD traits had an effect size of partial r = -0.14, while the largest case-control difference was associated with an effect size of d = -0.3). Similar microstructural anomalies were not present for anxiety, mood, or externalizing problems. Findings held when ADHD cases and control subjects were matched on in-scanner motion.

CONCLUSIONS

While present across cohorts, ADHD-associated microstructural differences had small effects, underscoring the limited clinical utility of this imaging modality used in isolation.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Associations between cortical thickness and anxious/depressive symptoms differ by the quality of early care

A variety of childhood experiences can lead to anxious/depressed (A/D) symptoms. The aim of the present study was to explore the brain morphological (cortical thickness and surface area) correlates of A/D symptoms and the extent to which these phenotypes vary depending on the quality of the parenting context in which children develop. Structural magnetic resonance imaging (MRI) scans were acquired on 45 children with Child Protective Services (CPS) involvement due to risk of not receiving adequate care (high-risk group) and 25 children without CPS involvement (low-risk group) (range_age = 8.08-12.14; M_age = 10.05) to assess cortical thickness (CT) and cortical surface area (SA). A/D symptoms were measured using the Child Behavioral Checklist. The association between A/D symptoms and CT, but not SA, differed by risk status such that high-risk children showed decreasing CT as A/D scores increased, whereas low-risk children showed increasing CT as A/D scores increased. This interaction was specific to CT in prefrontal, frontal, temporal, and parietal cortical regions. The groups had marginally different A/D scores, in the direction of higher risk being associated with lower A/D scores. Results suggest that CT correlates of A/D symptoms are differentially shaped by the quality of early caregiving experiences and should be distinguished between high- and low-risk children.

Why weight? Analytic approaches for large-scale population neuroscience data

Population-based neuroimaging studies that feature complex sampling designs enable researchers to generalize their results more widely. However, several theoretical and analytical questions pose challenges to researchers interested in these data. The following is a resource for researchers interested in using population-based neuroimaging data. We provide an overview of sampling designs and describe the differences between traditional model-based analyses and survey-oriented design-based analyses. To elucidate key concepts, we leverage data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), a population-based sample of 11,878 9-10-year-olds in the United States. Analyses revealed modest sociodemographic discrepancies between the target population of 9-10-year-olds in the U.S. and both the recruited ABCD sample and the analytic sample with usable structural and functional imaging data. In evaluating the associations between socioeconomic resources (i.e., constructs that are tightly linked to recruitment biases) and several metrics of brain development, we show that model-based approaches over-estimated the associations of household income and under-estimated the associations of caregiver education with total cortical volume and surface area. Comparable results were found in models predicting neural function during two fMRI task paradigms. We conclude with recommendations for ABCD Study® users and users of population-based neuroimaging cohorts more broadly.

Increasing diversity in developmental cognitive neuroscience: A roadmap for increasing representation in pediatric neuroimaging research

Understanding of human brain development has advanced rapidly as the field of developmental cognitive neuroscience (DCN) has matured into an established scientific discipline. Despite substantial progress, DCN lags behind other related disciplines in terms of diverse representation, standardized reporting requirements for socio-demographic characteristics of participants in pediatric neuroimaging studies, and use of intentional sampling strategies to more accurately represent the socio-demographic, ethnic, and racial composition of the populations from which participants are sampled. Additional efforts are needed to shift DCN towards a more inclusive field that facilitates the study of individual differences across a variety of cultural and contextual experiences. In this commentary, we outline and discuss barriers within our current scientific practice (e.g., research methods) and beliefs (i.e., what constitutes good science, good scientists, and good research questions) that contribute to under-representation and limited diversity within pediatric neuroimaging studies and propose strategies to overcome those barriers. We discuss strategies to address barriers at intrapersonal, interpersonal, community, systemic, and structural levels. Highlighting strength-based models of inclusion and recognition of the value of diversity in DCN research, along with acknowledgement of the support needed to diversify the field is critical for advancing understanding of neurodevelopment and reducing health inequities.

Impact of obesity and diet on brain structure and function: a gut-brain-body crosstalk

Most societies witness an ever increasing prevalence of both obesity and dementia, a scenario related to often underestimated individual and public health burden. Overnutrition and weight gain have been linked with abnormal functionality of homoeostasis brain networks and changes in higher cognitive functions such as reward evaluation, executive functions and learning and memory. In parallel, evidence has accumulated that modifiable factors such as obesity and diet impact the gut-brain axis and modulate brain health and cognition through various pathways. Using neuroimaging data from epidemiological studies and randomised clinical trials, we aim to shed light on the underlying mechanisms and to determine both determinants and consequences of obesity and diet at the level of human brain structure and function. We analysed multimodal 3T MRI of about 2600 randomly selected adults (47 % female, 18-80 years of age, BMI 18-47 kg/m²) of the LIFE-Adult study, a deeply phenotyped population-based cohort. In addition, brain MRI data of controlled intervention studies on weight loss and healthy diets acquired in lean, overweight and obese participants may help to understand the role of the gut-brain axis in food craving and cognitive ageing. We find that higher BMI and visceral fat accumulation correlate with accelerated brain age, microstructure of the hypothalamus, lower thickness and connectivity in default mode- and reward-related areas, as well as with subtle grey matter atrophy and white matter lesion load in non-demented individuals. Mediation analyses indicated that higher visceral fat affects brain tissue through systemic low-grade inflammation, and that obesity-related regional changes translate into cognitive disadvantages. Considering longitudinal studies, some, but not all data indicate beneficial effects of weight loss and healthy diets such as plant-based nutrients and dietary patterns on brain ageing and cognition. Confounding effects of concurrent changes in other lifestyle factors or false positives might help to explain these findings. Therefore a more holistic intervention approach, along with open science tools such as data and code sharing, in-depth pre-registration and pooling of data could help to overcome these limitations. In addition, as higher BMI relates to increased head micro-movements during MRI, and as head motion in turn systematically induces image artefacts, future studies need to rigorously control for head motion during MRI to enable valid neuroimaging results. In sum, our results support the view that overweight and obesity are intertwined with markers of brain health in the general population, and that weight loss and plant-based diets may help to promote brain plasticity. Meta-analyses and longitudinal cohort studies are underway to further differentiate causation from correlation in obesity- and nutrition-brain research.

Future Directions for the HRS Harmonized Cognitive Assessment Protocol

In the absence of effective pharmacological treatment to halt or reverse the course of Alzheimer's disease and related dementias (ADRDs), population-level research on the modifiable determinants of dementia risk and outcomes for those living with ADRD is critical. The Harmonized Cognitive Assessment Protocol (HCAP), fielded in 2016 as part of the U.S. Health and Retirement Study (HRS) and multiple international counterparts, has the potential to play an important role in such efforts. The stated goals of the HCAP are to improve our ability to understand the determinants, prevalence, costs, and consequences of cognitive impairment and dementia in the U.S. and to support cross-national comparisons. The first wave of the HCAP demonstrated the feasibility and value of the more detailed cognitive assessments in the HCAP compared to the brief cognitive assessments in the core HRS interviews. To achieve its full potential, we provide eight recommendations for improving future iterations of the HCAP. Our highest priority recommendation is to increase the representation of historically marginalized racial/ethnic groups disproportionately affected by ADRDs. Additional recommendations relate to the timing of the HCAP assessments; clinical and biomarker validation data, including to improve cross-national comparisons; dropping lower performing items; enhanced documentation; and the addition of measures related to caregiver impact. We believe that the capacity of the HCAP to achieve its stated goals will be greatly enhanced by considering these changes and additions.

Effects of the COVID-19 Pandemic on Mental Health and Brain Maturation in Adolescents: Implications for Analyzing Longitudinal Data

Background

The COVID-19 pandemic has caused significant stress and disruption for young people, likely leading to alterations in their mental health and neurodevelopment. In this context, it is not clear whether youth who lived through the pandemic and its shutdowns are comparable psychobiologically to their age- and sex-matched peers assessed before the pandemic. This question is particularly important for researchers who are analyzing longitudinal data that span the pandemic.

Methods

In this study we compared carefully matched youth assessed before the pandemic (n=81) and after the pandemic-related shutdowns ended (n=82).

Results

We found that youth assessed after the pandemic shutdowns had more severe internalizing mental health problems, reduced cortical thickness, larger hippocampal and amygdala volume, and more advanced brain age.

Conclusions

Thus, not only does the COVID-19 pandemic appear to have led to poorer mental health and accelerated brain aging in adolescents, but it also poses significant challenges to researchers analyzing data from longitudinal studies of normative development that were interrupted by the pandemic.

Early deprivation alters structural brain development from middle childhood to adolescence

Hypotheses concerning the biologic embedding of early adversity via developmental neuroplasticity mechanisms have been proposed on the basis of experimental studies in animals. However, no studies have demonstrated a causal link between early adversity and neural development in humans. Here, we present evidence from a randomized controlled trial linking psychosocial deprivation in early childhood to changes in cortical development from childhood to adolescence using longitudinal data from the Bucharest Early Intervention Project. Changes in cortical structure due to randomization to foster care were most pronounced in the lateral and medial prefrontal cortex and in white matter tracts connecting the prefrontal and parietal cortex. Demonstrating the causal impact of exposure to deprivation on the development of neural structure highlights the importance of early placement into family-based care to mitigate lasting neurodevelopmental consequences associated with early-life deprivation.

Radically reframing studies on neurobiology and socioeconomic circumstances: A call for social justice-oriented neuroscience

Socioeconomic circumstances are associated with symptoms and diagnostic status of nearly all mental health conditions. Given these robust relationships, neuroscientists have attempted to elucidate how socioeconomic-based adversity "gets under the skin." Historically, this work emphasized individual proxies of socioeconomic position (e.g., income, education), ignoring the effects of broader socioeconomic contexts (e.g., neighborhood socioeconomic disadvantage) which may uniquely contribute to chronic stress. This omission represented a disconnect between neuroscience and other allied fields that have recognized health is undeniably linked to interactions between systems of power and individual characteristics. More recently, neuroscience work has considered how sociopolitical context affects brain structure and function; however, the products of this exciting line of research have lacked critical sociological and historical perspectives. While empirical evidence on this topic is burgeoning, the cultural, ethical, societal, and legal implications of this work have been elusive. Although the mechanisms by which socioeconomic circumstances impact brain structure and function may be similar across people, not everyone is exposed to these factors at similar rates. Individuals from ethnoracially minoritized groups are disproportionally exposed to neighborhood disadvantage. Thus, socioeconomic inequities examined in neuroscience research are undergirding with other forms of oppression, namely structural racism. We utilize a holistic, interdisciplinary approach to interpret findings from neuroscience research and interweave relevant theories from the fields of public health, social sciences, and Black feminist thought. In this perspective piece, we discuss the complex relationship that continues to exist between academic institutions and underserved surrounding communities, acknowledging the areas in which neuroscience research has historically harmed and/or excluded structurally disadvantaged communities. We conclude by envisioning how this work can be used; not just to inform policymakers, but also to engage and partner with communities and shape the future direction of human neuroscience research.

A perspective on enhancing representative samples in developmental human neuroscience: Connecting science to society

Marginalized groups are often underrepresented in human developmental neuroscientific studies. This is problematic for the generalizability of findings about brain-behavior mechanisms, as well as for the validity, reliability, and reproducibility of results. In the present paper we discuss selection bias in cohort studies, which is known to contribute to the underrepresentation of marginalized groups. First, we address the issue of exclusion bias, as marginalized groups are sometimes excluded from studies because they do not fit the inclusion criteria. Second, we highlight examples of sampling bias. Recruitment strategies are not always designed to reach and attract a diverse group of youth. Third, we explain how diversity can be lost due to attrition of marginalized groups in longitudinal cohort studies. We provide experience- and evidence-based recommendations to stimulate neuroscientists to enhance study population representativeness via science communication and citizen science with youth. By connecting science to society, researchers have the opportunity to establish sustainable and equal researcher-community relationships, which can positively contribute to tackling selection biases.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality

The presence, magnitude, and significance of sex differences in the human brain are hotly debated topics in the scientific community and popular media. This debate is largely fueled by studies containing strong, opposing conclusions: either little to no evidence exists for sex differences in human neuroanatomy, or there are small-to-moderate differences in the size of certain brain regions that are highly reproducible across cohorts (even after controlling for sex differences in average brain size). Our Commentary uses the specific comparison between two recent large-scale studies that adopt these opposing views-namely the review by Eliot and colleagues (2021) and the direct analysis of ~ 40k brains by Williams and colleagues (2021)-in an effort to clarify this controversy and provide a framework for conducting this research. First, we review observations that motivate research on sex differences in human neuroanatomy, including potential causes (evolutionary, genetic, and environmental) and effects (epidemiological and clinical evidence for sex-biased brain disorders). We also summarize methodological and empirical support for using structural MRI to investigate such patterns. Next, we outline how researchers focused on sex differences can better specify their study design (e.g., how sex was defined, if and how brain size was adjusted for) and results (by e.g., distinguishing sexual dimorphisms from sex differences). We then compare the different approaches available for studying sex differences across a large number of individuals: direct analysis, meta-analysis, and review. We stress that reviews do not account for methodological differences across studies, and that this variation explains many of the apparent inconsistencies reported throughout recent reviews (including the work by Eliot and colleagues). For instance, we show that amygdala volume is consistently reported as male-biased in studies with sufficient sample sizes and appropriate methods for brain size correction. In fact, comparing the results from multiple large direct analyses highlights small, highly reproducible sex differences in the volume of many brain regions (controlling for brain size). Finally, we describe best practices for the presentation and interpretation of these findings. Care in interpretation is important for all domains of science, but especially so for research on sex differences in the human brain, given the existence of broad societal gender-biases and a history of biological data being used justify sexist ideas. As such, we urge researchers to discuss their results from simultaneously scientific and anti-sexist viewpoints.

Maladaptive self-focused attention and default mode network connectivity: a transdiagnostic investigation across social anxiety and body dysmorphic disorders

Maladaptive self-focused attention (SFA) is a bias toward internal thoughts, feelings and physical states. Despite its role as a core maintaining factor of symptoms in cognitive theories of social anxiety and body dysmorphic disorders (BDDs), studies have not examined its neural basis. In this study, we hypothesized that maladaptive SFA would be associated with hyperconnectivity in the default mode network (DMN) in self-focused patients with these disorders. Thirty patients with primary social anxiety disorder or primary BDD and 28 healthy individuals were eligible and scanned. Eligibility was determined by scoring greater than 1SD or below 1SD of the Public Self-Consciousness Scale normative mean, respectively, for each group. Seed-to-voxel functional connectivity was computed using a DMN posterior cingulate cortex (PCC) seed. There was no evidence of increased DMN functional connectivity in patients compared to controls. Patients (regardless of diagnosis) showed reduced functional connectivity of the PCC with several brain regions, including the bilateral superior parietal lobule (SPL), compared to controls, which was inversely correlated with maladaptive SFA but not associated with social anxiety, body dysmorphic, depression severity or rumination. Abnormal PCC-SPL connectivity may represent a transdiagnostic neural marker of SFA that reflects difficulty shifting between internal versus external attention.

Fetal Hippocampal Connectivity Shows Dissociable Associations with Maternal Cortisol and Self-Reported Distress during Pregnancy

Maternal stress can shape long-term child neurodevelopment beginning in utero. One mechanism by which stress is transmitted from mothers to their offspring is via alterations in maternal cortisol, which can cross the placenta and bind to glucocorticoid receptor-rich regions in the fetal brain, such as the hippocampus. Although prior studies have demonstrated associations between maternal prenatal stress and cortisol levels with child brain development, we lack information about the extent to which these associations originate prior to birth and prior to confounding postnatal influences. Pregnant mothers (n = 77) completed questionnaires about current perceived stress, depressive symptoms, and anxiety symptoms, provided three to four salivary cortisol samples, and completed a fetal resting-state functional MRI scan during their second or third trimester of pregnancy (mean gestational age = 32.8 weeks). Voxelwise seed-based connectivity analyses revealed that higher prenatal self-reported distress and higher maternal cortisol levels corresponded to dissociable differences in fetal hippocampal functional connectivity. Specifically, self-reported distress was correlated with increased positive functional coupling between the hippocampus and right posterior parietal association cortex, while higher maternal cortisol was associated with stronger positive hippocampal coupling with the dorsal anterior cingulate cortex and left medial prefrontal cortex. Moreover, the association between maternal distress, but not maternal cortisol, on fetal hippocampal connectivity was moderated by fetal sex. These results suggest that prenatal stress and peripheral cortisol levels may shape fetal hippocampal development through unique mechanisms.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

Anxiety-Related Frontocortical Activity Is Associated With Dampened Stressor Reactivity in the Real World

Negative affect is a fundamental dimension of human emotion. When extreme, it contributes to a variety of adverse outcomes, from physical and mental illness to divorce and premature death. Mechanistic work in animals and neuroimaging research in humans and monkeys have begun to reveal the broad contours of the neural circuits governing negative affect, but the relevance of these discoveries to everyday distress remains incompletely understood. Here, we used a combination of approaches-including neuroimaging assays of threat anticipation and emotional-face perception and more than 10,000 momentary assessments of emotional experience-to demonstrate that individuals who showed greater activation in a cingulo-opercular circuit during an anxiety-eliciting laboratory paradigm experienced lower levels of stressor-dependent distress in their daily lives (ns = 202-208 university students). Extended amygdala activation was not significantly related to momentary negative affect. These observations provide a framework for understanding the neurobiology of negative affect in the laboratory and in the real world.

Effects of the physical and social environment on youth cognitive performance

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

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.

A survey of protocols from 54 infant and toddler neuroimaging research labs

Infant and toddler MRI enables unprecedented insight into the developing brain. However, consensus about optimal data collection practices is lacking, which slows growth of the field and impedes replication efforts. The goal of this study was to collect systematic data across a large number of infant/toddler research laboratories to better understand preferred practices. Survey data addressed MRI acquisition strategies, scan success rates, visit preparations, scanning protocols, accommodations for families, study design, and policies regarding incidental findings. Respondents had on average 8 years' experience in early life neuroimaging and represented more than fifty research laboratories. Areas of consensus across labs included higher success rates among newborns compared to older infants or toddlers, high rates of data loss across age groups, endorsement of multiple layers of hearing protection, and age-specific scan preparation and participant accommodation. Researchers remain divided on decisions in longitudinal study design and practices regarding incidental findings. This study summarizes practices honed over years of work by a large collection of scientists, which may serve as an important resource for those new to the field. The ability to reference data about best practices facilitates future harmonization, data sharing, and reproducibility, all of which advance this important frontier in developmental science.

Annual Research Review: Attention-deficit/hyperactivity disorder in girls and women: underrepresentation, longitudinal processes, and key directions

Attention-deficit/hyperactivity disorder (ADHD) - and its underlying behavioral dimensions of inattention and hyperactivity-impulsivity - have been understudied in females. We first cover the conceptual issues of prevalence, diagnostic practices, diversity, comorbidity, and causal factors, plus forces limiting awareness of ADHD in females. After a narrative review of cross-sectional and longitudinal findings, we conclude the following. (a) Girls meet diagnostic criteria for ADHD at just under half the rates of boys, a ratio that becomes much closer to equal by adulthood. (b) Girls and women with ADHD show a predominance of inattention and associated internalizing problems; boys and men display greater levels of hyperactive-impulsive symptoms and associated externalizing problems. (c) Sex differences in ADHD symptoms and related outcomes depend heavily on the clinical versus nonreferred nature of the samples under investigation. (d) Females with ADHD experience, on average, serious impairments, with a particularly heightened risk for problems in close relationships and engagement in self-harm. (e) Clinicians may overlook symptoms and impairments in females because of less overt (but still impairing) symptom manifestations in girls and women and their frequent adoption of compensatory strategies. Our review of predictors and mediators of adult outcomes highlights (a) the potential for heterotypically continuous pathways in females with childhood ADHD and (b) developmental progressions to self-harm, intimate partner violence, unplanned pregnancy, and comorbid psychopathology. Focusing on ADHD in females is necessary to characterize causal and maintaining mechanisms with accuracy and to foster responsive interventions, as highlighted in our closing list of clinical implications and research priorities.

Inhibitory control of positive and negative information and adolescent depressive symptoms: a population-based cohort study

BACKGROUND

Large population-based cohort studies of neuropsychological factors that characterise or precede depressive symptoms are rare. Most studies use small case-control or cross-sectional designs, which may cause selection bias and cannot test temporality. In a large UK population-based cohort, we investigated cross-sectional and longitudinal associations between inhibitory control of positive and negative information and adolescent depressive symptoms.

METHODS

Cohort study of 2328 UK adolescents who completed an affective go/no-go task at age 18. Depressive symptoms were assessed with the Clinical Interview Schedule Revised (CIS-R) and short Mood and Feeling Questionnaire (sMFQ) at age 18, and with the sMFQ 1 year later (age 19). Analyses were multilevel and traditional linear regressions, before and after adjusting for confounders.

RESULTS

Cross-sectionally, we found little evidence that adolescents with more depressive symptoms made more inhibitory control errors [after adjustments, errors increased by 0.04% per 1 s.d. increase in sMFQ score (95% confidence interval 0.02-0.06)], but this association was not observed for the CIS-R. There was no evidence for an influence of valence. Longitudinally, there was no evidence that reduced inhibitory control was associated with future depressive symptoms.

CONCLUSIONS

Inhibitory control of positive and negative information does not appear to be a marker of current or future depressive symptoms in adolescents and would not be a useful target in interventions to prevent adolescent depression. Our lack of convincing evidence for associations with depressive symptoms suggests that the affective go/no-go task is not a promising candidate for future neuroimaging studies of adolescent depression.

Brain and cognitive ageing: The present, and some predictions (…about the future)

Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.

Racial Discrimination and White Matter Microstructure in Trauma-Exposed Black Women

BACKGROUND

Experiences of racial discrimination are linked to a range of negative brain health outcomes, but little is known about how these experiences impact neural architecture, including white matter microstructure, which may partially mediate these outcomes. Our goal was to examine associations between racially discriminatory experiences and white matter structural integrity in a sample of Black American women.

METHODS

We recruited 116 Black American women as part of a long-standing study of trauma. Participants completed assessments of racial discrimination, trauma exposure, and posttraumatic stress disorder and underwent diffusion tensor imaging. Fractional anisotropy and mean diffusivity values were extracted from major white matter tracts throughout the brain.

RESULTS

Experiences of racial discrimination were associated with significantly lower fractional anisotropy in multiple white matter tracts, including the corpus callosum, cingulum, and superior longitudinal fasciculus (ps < .004), even after accounting for variance associated with trauma, posttraumatic stress disorder, and demographic- and scanner-related factors.

CONCLUSIONS

These findings suggest that experiences of racial discrimination are independently related to decrements in white matter microarchitecture throughout the brain. In individuals who have experienced other types of adversity, racial discrimination clearly has additive and distinctive deleterious effects on white matter structure. Our findings suggest a pathway through which racial discrimination can contribute to brain health disparities in Black Americans; the deleterious contributions of racial discrimination on the microstructure of major white matter pathways may increase vulnerability for the development of neurodegenerative disorders as well as the development of mental health problems.

Low household income and neurodevelopment from infancy through adolescence

Despite advancements in the study of brain maturation at different developmental epochs, no work has linked the significant neural changes occurring just after birth to the subtler refinements in the brain occurring in childhood and adolescence. We aimed to provide a comprehensive picture regarding foundational neurodevelopment and examine systematic differences by family income. Using a nationally representative longitudinal sample of 486 infants, children, and adolescents (age 5 months to 20 years) from the NIH MRI Study of Normal Brain Development and leveraging advances in statistical modeling, we mapped developmental trajectories for the four major cortical lobes and constructed charts that show the statistical distribution of gray matter and reveal the considerable variability in regional volumes and structural change, even among healthy, typically developing children. Further, the data reveal that significant structural differences in gray matter development for children living in or near poverty, first detected during childhood (age 2.5-6.5 years), evolve throughout adolescence.

Reproducibility in the absence of selective reporting: An illustration from large-scale brain asymmetry research

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

Adverse childhood experiences and fronto-subcortical structures in the developing brain

The impact of adverse childhood experiences (ACEs) differs between individuals and depends on the type and timing of the ACE. The aim of this study was to assess the relation between various recently occurred ACEs and morphology in the developing brain of children between 8 and 11 years of age. We measured subcortical volumes, cortical thickness, cortical surface area and fractional anisotropy in regions of interest in brain scans acquired in 1,184 children from the YOUth cohort. ACEs were based on parent-reports of recent experiences and included: financial problems; parental mental health problems; physical health problems in the family; substance abuse in the family; trouble with police, justice or child protective services; change in household composition; change in housing; bereavement; divorce or conflict in the family; exposure to violence in the family and bullying victimization. We ran separate linear models for each ACE and each brain measure. Results were adjusted for the false discovery rate across regions of interest. ACEs were reported for 83% of children in the past year. Children were on average exposed to two ACEs. Substance abuse in the household was associated with larger cortical surface area in the left superior frontal gyrus, t(781) = 3.724, p _FDR = 0.0077, right superior frontal gyrus, t(781) = 3.409, p _FDR = 0.0110, left pars triangularis, t(781) = 3.614, p _FDR = 0.0077, left rostral middle frontal gyrus, t(781) = 3.163, p _FDR = 0.0195 and right caudal anterior cingulate gyrus, t(781) = 2.918, p _FDR = 0.0348. Household exposure to violence (was associated with lower fractional anisotropy in the left and right cingulum bundle hippocampus region t(697) = -3.154, p _FDR = 0.0101 and t(697) = -3.401, p _FDR = 0.0085, respectively. Lower household incomes were more prevalent when parents reported exposure to violence and the mean parental education in years was lower when parents reported substance abuse in the family. No other significant associations with brain structures were found. Longer intervals between adversity and brain measurements and longitudinal measurements may reveal whether more evidence for the impact of ACEs on brain development will emerge later in life.

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population-based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large-scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex-specific phenotypes in major brain diseases.