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.

Hypertension, Cognitive Decline, and Mild Cognitive Impairment Among Diverse Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging Results (SOL-INCA)

BACKGROUND

Hypertension can have deleterious effects on cognitive function; however, few studies have examined its effects on cognition among Hispanics/Latinos.

OBJECTIVE

To assess associations between hypertension status with 1) change in cognitive performance, and 2) having mild cognitive impairment (MCI) among diverse Hispanics/Latinos.

METHODS

This population-based, prospective cohort, multisite study included Hispanic/Latino adults aged 45 to 72 years in enrolled in the Hispanic Community Health Study/Study of Latinos at Visit 1 (2008-2011; mean age of 63.40±8.24 years), and the Study of Latinos-Investigation of Neurocognitive Aging at Visit 2 (2016-2018), with a mean follow-up duration of 7 years (n = 6,173). Hypertension status was assessed at both visits: normotension (no hypertension), incident hypertension (only at Visit 2), and persistent hypertension (at both visits). We examined change in cognitive performance and having MCI (only assessed at Visit 2) relative to hypertension status and adjusted for demographics and cardiovascular disease risk factors.

RESULTS

Compared to normotension, persistent hypertension was associated with significantly increased decline in verbal fluency (β= -0.08; CI = [-0.16;-0.01]; p < 0.05), and processing speed (β= -0.11; CI = [-0.20;-0.02]; p < 0.05). Incident hypertension was not associated with significant change in cognitive performance. Both incident (OR = 1.70; CI = [1.16;2.50]; p < 0.01) and persistent hypertension (OR = 2.13; CI = [1.57;2.88]; p < 0.001) were associated with significantly higher odds ratios of having MCI.

CONCLUSIONS

These findings indicate that persistent hypertension is associated with clinical impairment and domain-specific cognitive decline in middle-aged and older Hispanics/Latinos. It underscores the importance of monitoring blood pressure in routine healthcare visits beginning at midlife in this population to reduce the burden of cognitive decline.

Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs

Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of social groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.

Erratum: Detecting suicide ideation in the era of social media: The population neuroscience perspective

[This corrects the article DOI: 10.3389/fpsyt.2022.652167.].

A training program for researchers in population neuroimaging: Early experiences

Recent advances in neuroimaging create groundbreaking opportunities to better understand human neurological and psychiatric diseases, but also bring new challenges. With the advent of more and more sophisticated and efficient multimodal image processing software, we can now study much larger populations and integrate information from multiple modalities. In consequence, investigators that use neuroimaging techniques must also understand and apply principles of population sampling and contemporary data analytic techniques. The next generation of neuroimaging researchers must be skilled in numerous previously distinct disciplines and so a new integrated model of training is needed. This tutorial presents the rationale for such a new training model and presents the results from the first years of the training program focused on population neuroimaging of Alzheimer's Disease. This approach is applicable to other areas of population neuroimaging.

Population variation in social brain morphology: Links to socioeconomic status and health disparity

Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes people's daily interaction with their social environment and is known to impact various health outcomes. Using generative probabilistic modeling, we investigate health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.

Smaller Hippocampal Volume Among Black and Latinx Youth Living in High-Stigma Contexts

OBJECTIVE

To determine whether structural and individual forms of stigma are associated with neurodevelopment in children.

METHOD

Stigma related to gender, race, and Latinx ethnicity was measured at the structural level using objective state-level indicators of social policies and prejudicial attitudes and at the individual level using self-reports of perceived discrimination. Respective associations of stigma with hippocampal volume and amygdala reactivity to threat were examined using data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,534, mean age 9.9 years), the first multisite neuroimaging study that provided substantial variability in sociopolitical contexts and that included individual-level measures of stigma among youth.

RESULTS

In a preregistered analysis, Black (B = -58.26, p = .023) and Latinx (B = -40.10, p = .044) youths in higher (vs lower) structural stigma contexts were found to have smaller hippocampal volume, controlling for total intracranial volume, demographics, and family socioeconomic status. This association was also observed at a trend-level among girls (p = .082). The magnitude of the difference in hippocampal volume between high and low structural stigma states was equivalent to the predicted impact of a $20,000 difference in annual family income in this sample. As hypothesized, structural stigma was not associated with hippocampal volume in nonstigmatized youths, providing evidence of specificity. Perceived discrimination was unrelated to hippocampal volume in stigmatized groups. No associations between perceived discrimination or structural stigma and amygdala reactivity to threat were observed.

CONCLUSION

This study provides novel evidence that an objective measure of structural stigma may be more strongly related to hippocampal volume than subjective perceptions of stigma, suggesting that contextual approaches to stigma could yield new insights into neurodevelopment among marginalized youth.

Lacking Social Support is Associated With Structural Divergences in Hippocampus-Default Network Co-Variation Patterns

Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominating factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40 000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3 and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC–DN co-variation followed a clear segregation into the left and right brain hemispheres. Separable regimes of structural HC–DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.

Loneliness is linked to specific subregional alterations in hippocampus-default network covariation

Social interaction complexity makes humans unique. But in times of social deprivation, this strength risks exposure of important vulnerabilities. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically covary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By codecomposition using structural brain scans of ∼40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex patterns coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN covariation had distinct associations with the genetic predisposition for loneliness at the population level.

NEW & NOTEWORTHY The hippocampus and default network have been implicated in rich social interaction. Yet, these allocortical and neocortical neural systems have been interrogated in mostly separate literatures. Here, we conjointly investigate the hippocampus and default network at a subregion level, by capitalizing structural brain scans from ∼40,000 participants. We thus reveal unique insights on the nature of the “lonely brain” by estimating the regimes of covariation between the hippocampus and default network at population scale.

Population variability in social brain morphology for social support, household size and friendship satisfaction

The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate 'inner' and wider 'outer' social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the 'social brain', ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.

Variability in Brain Structure and Function Reflects Lack of Peer Support

Humans are a highly social species. Complex interactions for mutual support range from helping neighbors to building social welfare institutions. During times of distress or crisis, sharing life experiences within one's social circle is critical for well-being. By translating pattern-learning algorithms to the UK Biobank imaging-genetics cohort (n = ~40 000 participants), we have delineated manifestations of regular social support in multimodal whole-brain measurements. In structural brain variation, we identified characteristic volumetric signatures in the salience and limbic networks for high- versus low-social support individuals. In patterns derived from functional coupling, we also located interindividual differences in social support in action-perception circuits related to binding sensory cues and initiating behavioral responses. In line with our demographic profiling analysis, the uncovered neural substrates have potential implications for loneliness, substance misuse, and resilience to stress.

Population-neuroscience study of the Tokyo TEEN Cohort (pn-TTC): Cohort longitudinal study to explore the neurobiological substrates of adolescent psychological and behavioral development

AIM

Adolescence is a crucial stage of psychological development and is critically vulnerable to the onset of psychopathology. Our understanding of how the maturation of endocrine, epigenetics, and brain circuit may underlie psychological development in adolescence, however, has not been integrated. Here, we introduce our research project, the population-neuroscience study of the Tokyo TEEN Cohort (pn-TTC), a longitudinal study to explore the neurobiological substrates of development during adolescence.

METHODS

Participants in the first wave of the pn-TTC (pn-TTC-1) study were recruited from those of the TTC study, a large-scale epidemiological survey in which 3171 parent-adolescent pairs were recruited from the general population. Participants underwent psychological, cognitive, sociological, and physical assessment. Moreover, adolescents and their parents underwent magnetic resonance imaging (MRI; structural MRI, resting-state functional MRI, and magnetic resonance spectroscopy), and adolescents provided saliva samples for hormone analysis and for DNA analysis including epigenetics. Furthermore, the second wave (pn-TTC-2) followed similar methods as in the first wave.

RESULTS

A total of 301 parent-adolescent pairs participated in the pn-TTC-1 study. Moreover, 281 adolescents participated in the pn-TTC-2 study, 238 of whom were recruited from the pn-TTC-1 sample. The instruction for data request is available at: http://value.umin.jp/data-resource.html.

CONCLUSION

The pn-TTC project is a large-scale and population-neuroscience-based survey with a plan of longitudinal biennial follow up. Through this approach we seek to elucidate adolescent developmental mechanisms according to biopsychosocial models. This current biomarker research project, using minimally biased samples recruited from the general population, has the potential to expand the new research field of population neuroscience.

Neuromarkers for Mental Disorders: Harnessing Population Neuroscience

Despite abundant research into the neurobiology of mental disorders, to date neurobiological insights have had very little impact on psychiatric diagnosis or treatment. In this review, we contend that the search for neuroimaging biomarkers-neuromarkers-of mental disorders is a highly promising avenue toward improved psychiatric healthcare. However, many of the traditional tools used for psychiatric neuroimaging are inadequate for the identification of neuromarkers. Specifically, we highlight the need for larger samples and for multivariate analysis. Approaches such as machine learning are likely to be beneficial for interrogating high-dimensional neuroimaging data. We suggest that broad, population-based study designs will be important for developing neuromarkers of mental disorders, and will facilitate a move away from a phenomenological definition of mental disorder categories and toward psychiatric nosology based on biological evidence. We provide an outline of how the development of neuromarkers should occur, emphasizing the need for tests of external and construct validity, and for collaborative research efforts. Finally, we highlight some concerns regarding the development, and use of, neuromarkers in psychiatric healthcare.

Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction

Motion-related artifacts are one of the major challenges associated with pediatric neuroimaging. Recent studies have shown a relationship between visual quality ratings of T₁ images and cortical reconstruction measures. Automated algorithms offer more precision in quantifying movement-related artifacts compared to visual inspection. Thus, the goal of this study was to test three different automated quality assessment algorithms for structural MRI scans. The three algorithms included a Fourier-, integral-, and a gradient-based approach which were run on raw T₁ -weighted imaging data collected from four different scanners. The four cohorts included a total of 6,662 MRI scans from two waves of the Generation R Study, the NIH NHGRI Study, and the GUSTO Study. Using receiver operating characteristics with visually inspected quality ratings of the T₁ images, the area under the curve (AUC) for the gradient algorithm, which performed better than either the integral or Fourier approaches, was 0.95, 0.88, and 0.82 for the Generation R, NHGRI, and GUSTO studies, respectively. For scans of poor initial quality, repeating the scan often resulted in a better quality second image. Finally, we found that even minor differences in automated quality measurements were associated with FreeSurfer derived measures of cortical thickness and surface area, even in scans that were rated as good quality. Our findings suggest that the inclusion of automated quality assessment measures can augment visual inspection and may find use as a covariate in analyses or to identify thresholds to exclude poor quality data.