Integrated analysis of direct and proxy genome wide association studies highlights polygenicity of Alzheimer's disease outside of the APOE region.

Recent meta-analyses combining direct genome-wide association studies (GWAS) with those of family history (GWAX) have indicated very low SNP heritability of Alzheimer's disease (AD). These low estimates may call into question the prospects of continued progress in genetic discovery for AD within the spectrum of common variants. We highlight dramatic downward biases in previous methods, and we validate a novel method for the estimation of SNP heritability via integration of GWAS and GWAX summary data. We apply our method to investigate the genetic architecture of AD using GWAX from UK Biobank and direct case-control GWAS from the International Genomics of Alzheimer's Project (IGAP). We estimate the liability scale common variant SNP heritability of Clinical AD outside of APOE region at ~7-11%, and we project the corresponding estimate for AD pathology to be up to approximately 23%. We estimate that nearly 90% of common variant SNP heritability of Clinical AD exists outside the APOE region. Rare variants not tagged in standard GWAS may account for additional variance. Our results indicate that, while GWAX for AD in UK Biobank may result in greater attenuation of genetic effects beyond that conventionally assumed, it does not introduce appreciable contamination of signal by genetically distinct traits relative to direct case-control GWAS in IGAP. Genetic risk for AD represents a strong effect of APOE superimposed upon a highly polygenic background.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|ß| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples.

BACKGROUND: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS: We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS: In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION: Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain.

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

General dimensions of human brain morphometry inferred from genome-wide association data.

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

Socially Stratified Epigenetic Profiles Are Associated With Cognitive Functioning in Children and Adolescents.

Children's cognitive functioning and educational performance are socially stratified. Social inequality, including classism and racism, may operate partly via epigenetic mechanisms that modulate neurocognitive development. Following preregistered analyses of data from 1,183 participants, ages 8 to 19 years, from the Texas Twin Project, we found that children growing up in more socioeconomically disadvantaged families and neighborhoods and children from marginalized racial/ethnic groups exhibit DNA methylation profiles that, in previous studies of adults, were indicative of higher chronic inflammation, lower cognitive functioning, and a faster pace of biological aging. Furthermore, children's salivary DNA methylation profiles were associated with their performance on in-laboratory tests of cognitive and academic skills, including processing speed, general executive function, perceptual reasoning, verbal comprehension, reading, and math. Given that the DNA methylation measures that we examined were originally developed in adults, our results suggest that children show molecular signatures that reflect the early life social determinants of lifelong disparities in health and cognition.

A Mixed-Methods Approach to Refining and Measuring the Construct of Positive Risk-Taking in Adolescence.

Adolescence is a peak period for risk-taking, but research has largely overlooked positive manifestations of adolescent risk-taking due to ambiguity regarding operationalization and measurement of positive risk-taking. We address this limitation using a mixed-methods approach. We elicited free responses from contemporary college students (N = 74, Mage  = 20.1 years) describing a time they took a risk. Qualitative analysis informed the construction of a self-report positive risk-taking scale, which was administered to a population-based sample of adolescents (N = 1,249, Mage  = 16 years) for quantitative validation and examination of associations with normative and impulsive personality. Sensation seeking predicted negative and positive risk-taking, whereas extraversion and openness were predominantly related to positive risk-taking. Results provide promising evidence for a valid measure of adolescents' engagement in positive risks.

Niche Diversity Predicts Personality Structure Across 115 Nations.

The niche-diversity hypothesis proposes that personality structure arises from the affordances of unique trait combinations within a society. It predicts that personality traits will be both more variable and differentiated in populations with more distinct social and ecological niches. Prior tests of this hypothesis in 55 nations suffered from potential confounds associated with differences in the measurement properties of personality scales across groups. Using psychometric methods for the approximation of cross-national measurement invariance, we tested the niche-diversity hypothesis in a sample of 115 nations (N = 685,089). We found that an index of niche diversity was robustly associated with lower intertrait covariance and greater personality dimensionality across nations but was not consistently related to trait variances. These findings generally bolster the core of the niche-diversity hypothesis, demonstrating the contingency of human personality structure on socioecological contexts.

Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.

Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Weak and uneven associations of home, neighborhood, and school environments with stress hormone output across multiple timescales.

The progression of lifelong trajectories of socioeconomic inequalities in health and mortality begins in childhood. Dysregulation in cortisol, a stress hormone that is the primary output of the hypothalamus-pituitary-adrenal (HPA) axis, has been hypothesized to be a mechanism for how early environmental adversity compromises health. However, despite the popularity of cortisol as a biomarker for stress and adversity, little is known about whether cortisol output differs in children being raised in socioeconomically disadvantaged environments. Here, we show that there are few differences between advantaged and disadvantaged children in their cortisol output. In 8-14-year-old children from the population-based Texas Twin Project, we measured cortisol output at three different timescales: (a) diurnal fluctuation in salivary cortisol (n = 400), (b) salivary cortisol reactivity and recovery after exposure to the Trier Social Stress Test (n = 444), and (c) cortisol concentration in hair (n = 1210). These measures converged on two moderately correlated, yet distinguishable, dimensions of HPA function. We tested differences in cortisol output across nine aspects of social disadvantage at the home (e.g., family socioeconomic status), school (e.g., average levels of academic achievement), and neighborhood (e.g., concentrated poverty). Children living in neighborhoods with higher concentrated poverty had higher diurnal cortisol output, as measured in saliva; otherwise, child cortisol output was unrelated to any other aspect of social disadvantage. Overall, we find limited support for alteration in HPA axis functioning as a general mechanism for the health consequences of socioeconomic inequality in childhood.

The relationship between executive function, processing speed, and attention-deficit hyperactivity disorder in middle childhood.

Attention-Deficit Hyperactivity Disorder (ADHD) is a heterogeneous disorder that is highly impairing. Early, accurate diagnosis maximizes long-term positive outcomes for youth with ADHD. Tests of executive functioning (EF) are potential tools for screening and differential diagnosis of ADHD subtypes. However, previous research has been inconsistent regarding the specificity and magnitude of EF deficits across ADHD subtypes. Here, we advance knowledge of the EF-ADHD relationship by using: (1) dimensional latent factor models of ADHD that captures the heterogeneity of expression, and (2) a comprehensive, reliable battery of EF tasks and modeling relationships with a general factor of EF ability. We tested 1548 children and adolescents (ages 7-15 years) from the Texas Twin Project, a population-based cohort with a diverse socioeconomic and ethnic composition. We show that EF deficits were specific to the inattention domain of ADHD. Moreover, we found that the association between EF task performance and inattention was stable across sociodemographic groups. Our results demonstrate that failures of executive control are selectively manifested as covert inattentive symptoms, such as trouble with organization, forgetfulness, and distractedness, rather than overt symptoms, such as inappropriate talkativeness and interruption. Future research, utilizing a bifactor characterization of ADHD in clinical samples, is needed to further refine understanding of the nature of cognitive deficits in ADHD across the full range of symptom variation.

Examining relations between performance on non-verbal executive function and verbal self-regulation tasks in demographically-diverse populations.

Self-regulation is a widely studied construct, generally assumed to be cognitively supported by executive functions (EFs). There is a lack of clarity and consensus over the roles of specific components of EFs in self-regulation. The current study examines the relations between performance on (a) a self-regulation task (Heads, Toes, Knees Shoulders Task) and (b) two EF tasks (Knox Cube and Beads Tasks) that measure different components of updating: working memory and short-term memory, respectively. We compared 107 8- to 13-year-old children (64 females) across demographically-diverse populations in four low and middle-income countries, including: Tanna, Vanuatu; Keningau, Malaysia; Saltpond, Ghana; and Natal, Brazil. The communities we studied vary in market integration/urbanicity as well as level of access, structure, and quality of schooling. We found that performance on the visuospatial working memory task (Knox Cube) and the visuospatial short-term memory task (Beads) are each independently associated with performance on the self-regulation task, even when controlling for schooling and location effects. These effects were robust across demographically-diverse populations of children in low-and middle-income countries. We conclude that this study found evidence supporting visuospatial working memory and visuospatial short-term memory as distinct cognitive processes which each support the development of self-regulation.

Error-signaling in the developing brain.

While learning from mistakes is a lifelong process, the rate at which an individual makes errors on any given task decreases through late adolescence. Previous fMRI adult work indicates that several control brain networks are reliably active when participants make errors across multiple tasks. Less is known about the consistency and localization of error processing in the child brain because previous research has used single tasks. The current analysis pooled data across three studies to examine error-related task activation (two tasks per study, three tasks in total) for a group of 232 children aged 8-17 years. We found that, consistent with the adult literature, the majority of applied cingulo-opercular brain regions, including medial superior frontal cortex, dorsal anterior cingulate, and bilateral anterior insula, showed consistent error processing engagement in children across multiple tasks. Error-related activity in many of these cingulo-opercular regions correlated with task performance. However, unlike in the adult literature, we found a lack of error-related activation across tasks in dorsolateral frontal areas, and we also did not find any task-consistent relations with age in these regions. Our findings suggest that the task-general error processing signal in the developing brain is fairly robust and similar to adults, with the exception of lateral frontal cortex.

Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936.

Multi-scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T1 -weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community-dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6-11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between-scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612-.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504-.763). Between-scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475-.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole-brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275-.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533-.647). Regression-based k-fold cross-validation showed that, particularly for global volumes, between-scanner differences could be largely eliminated (R2 range .615-.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.

The effect of network thresholding and weighting on structural brain networks in the UK Biobank.

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 â€‹≤ â€‹|ß| â€‹≤ â€‹0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 â€‹≤ â€‹|ß| â€‹≤ â€‹0.406) than the consistency-based approach which retained only 30% of connections (0.140 â€‹≤ â€‹|ß| â€‹≤ â€‹0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC â€‹= â€‹0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean ߠ​≤ â€‹|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean ߠ​≤ â€‹|0.219|, p â€‹< â€‹0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

Developmental transformations in the structure of executive functions.

Comparisons of results from studies of executive function (EF) in early childhood to those of EF in middle and late childhood suggest that individual differences in EFs may differentiate from a unitary factor in early childhood to an increasingly multidimensional structure in middle childhood and adolescence. We tested whether associations among EFs strengthened from middle childhood to adolescence using cross-sectional data from a population-based sample of 1019 children aged 7-15 years (M = 10.79 years). Participants completed a comprehensive EF battery consisting of 15 measures tapping working memory, updating, switching, and inhibition domains. Moderated factor analysis, local structural equation modeling, and network modeling were applied to assess age-related differences in the factor structure of EF. Results from all three approaches indicated that working memory and updating maintained uniformly high patterns of covariation across the age range, whereas inhibition became increasingly differentiated from the other three domains beginning around 10 years of age. However, consistent with past research, inhibition tasks were only weakly intercorrelated. Age-related differences in the organization of switching abilities were mixed.

Associations between vascular risk factors and brain MRI indices in UK Biobank.

AIMS: Several factors are known to increase risk for cerebrovascular disease and dementia, but there is limited evidence on associations between multiple vascular risk factors (VRFs) and detailed aspects of brain macrostructure and microstructure in large community-dwelling populations across middle and older age. METHODS AND RESULTS: Associations between VRFs (smoking, hypertension, pulse pressure, diabetes, hypercholesterolaemia, body mass index, and waist-hip ratio) and brain structural and diffusion MRI markers were examined in UK Biobank (N = 9722, age range 44-79 years). A larger number of VRFs was associated with greater brain atrophy, lower grey matter volume, and poorer white matter health. Effect sizes were small (brain structural R2 ≤1.8%). Higher aggregate vascular risk was related to multiple regional MRI hallmarks associated with dementia risk: lower frontal and temporal cortical volumes, lower subcortical volumes, higher white matter hyperintensity volumes, and poorer white matter microstructure in association and thalamic pathways. Smoking pack years, hypertension and diabetes showed the most consistent associations across all brain measures. Hypercholesterolaemia was not uniquely associated with any MRI marker. CONCLUSION: Higher levels of VRFs were associated with poorer brain health across grey and white matter macrostructure and microstructure. Effects are mainly additive, converging upon frontal and temporal cortex, subcortical structures, and specific classes of white matter fibres. Though effect sizes were small, these results emphasize the vulnerability of brain health to vascular factors even in relatively healthy middle and older age, and the potential to partly ameliorate cognitive decline by addressing these malleable risk factors.

The neural architecture of executive functions is established by middle childhood.

Executive functions (EFs) are regulatory cognitive processes that support goal-directed thoughts and behaviors and that involve two primary networks of functional brain activity in adulthood: the fronto-parietal and cingulo-opercular networks. The current study assessed whether the same networks identified in adulthood underlie child EFs. Using task-based fMRI data from a diverse sample of N = 117 children and early adolescents (M age = 10.17 years), we assessed the extent to which neural activity was shared across switching, updating, and inhibition domains, and whether these patterns were qualitatively consistent with adult EF-related activity. Brain regions that were consistently engaged across switching, updating, and inhibition tasks closely corresponded to the cingulo-opercular and fronto-parietal networks identified in studies of adults. Isolating brain activity during more demanding task periods highlighted contributions of the dorsal anterior cingulate and anterior insular regions of the cingulo-opercular network. Results were independent of age and time-on-task effects. These results indicate that the two core brain networks that support EFs are in place by middle childhood, in agreement with resting-state findings of adultlike brain network organization. Improvement in EFs from middle childhood to adulthood, therefore, are likely due to quantitative changes in activity within these networks, rather than qualitative changes in the organization of the networks themselves. Improved knowledge of how the brain's functional organization supports EF in childhood has critical implications for understanding the maturation of cognitive abilities.

National Gross Domestic Product, Science Interest, and Science Achievement: A Direct Replication and Extension of the Tucker-Drob, Cheung, and Briley (2014) Study.

We replicated the study by Tucker-Drob, Cheung, and Briley (2014), who found that the association between science interest and science knowledge depended on economic resources at the family, school, and national levels, using data from the 2006 Programme for International Student Assessment (PISA). In more economically prosperous families, schools, and nations, student interest was more strongly correlated with actual knowledge. Here, we investigated whether these results still held despite substantial changes to educational and economic systems over roughly a decade. Using similar data from PISA 2015 ( N = 537,170), we found largely consistent results. Students from more economically advantaged homes, schools, and nations exhibited a stronger link between interests and knowledge. However, these moderation effects were substantially reduced, and the main effect of science interest increased by nearly 25%, driven almost entirely by families of low socioeconomic status and nations with low gross domestic product. The interdependence of interests and resources is robust but perhaps weakening with educational progress.