Biology subjects

Kievit, R.

Publications and source records attributed to Kievit, R..

10 recordsLinked to original sources

bioRxiv2025

No effect of education on Telomere Length: a natural experiment in aging individuals

Telomere length is increasingly used as a proxy for an aging exposome - an index of non-genetic environmental (and behavioral) exposures that an individual encounters, leading to differences in rates of aging. One of the largest associations with telomere length is schooling, leading to the suggestion of long-lasting protective effects of (additional) education. However, for ethical and practical reasons, education cannot generally be randomized, precluding an understanding of the causal effect of education on health outcomes. Here, we leverage a well-established policy change in the UK to test whether an additional year of education caused differences in telomere length decades later. Employing a fuzzy regression discontinuity design, we find no effect of education on telomere length in the UK BioBank across multiple analytic frameworks (continuity, local randomization & Bayesian approaches) and a series of robustness tests, such as increasing the analysis window around the cutoff, and changing the prior, similarly find no effect of an additional year of education on telomere length. This robust finding contrasts the prevailing aging exposome account of telomere length and offers a cautionary note in attributing causal power to environmental factors based on observational differences.

genetics↗

bioRxiv2024

ggrain - a ggplot2 extension for raincloud plots

Clear data visualization is essential to effectively communicate empirical findings across various research fields. Raincloud plots fill this need by offering a transparent and statistically robust approach to data visualization. This is achieved by combining three plots in an aesthetically pleasing fashion. First, a dot plot displays raw data with minimal distortion, allowing a fast glance at the sample size and outlier identification. Next, a box plot displays key distributional summary statistics such as the median and interquartile range. Lastly, a violin plot transparently displays the underlying distribution of the data. Despite the widespread use of raincloud plots, an R-package in alignment with the grammar of graphics was lacking. `ggrain` fills this need by offering one easy-to-use function (`geom_rain`) allowing the quick and seamless plotting of rainclouds in the R ecosystem. Further, it enables more complex plotting features such as factorial grouping, mapping with a secondary (continuous) covariate, and connecting observations longitudinally across multiple waves.

scientific communication and education↗

bioRxiv2024

No effect of additional education on long-term brain structure: a preregistered natural experiment in over 30,000 individuals.

Education is related to a wide variety of beneficial health, behavioral, and societal outcomes. However, whether education causes long-term structural changes in the brain remains unclear. A pressing challenge is that individuals self-select into continued education, thereby introducing a wide variety of environmental and genetic confounders. Fortunately, natural experiments allow us to isolate the causal impact of increased education from individual (and societal) characteristics. Here, we exploit a policy change in the UK (the 1972 ROSLA act) that increased the amount of mandatory schooling from 15 to 16 years of age to study the impact of education on long-term structural brain outcomes in the UK Biobank. Using regression discontinuity - a causal inference method - we find no evidence of an effect from an additional year of education on any structural neuroimaging outcomes. This null result is robust across modalities, regions, and analysis strategies. An additional year of education is a substantial cognitive intervention, yet we find no evidence for sustained experience-dependent plasticity. Our results provide a challenge for prominent accounts of cognitive or brain reserve theories which identify education as a major protective factor to lessen adverse aging effects. Our preregistered findings are one of the first implementations of regression discontinuity on neural data - opening the door for causal inference in population-based neuroimaging.

neuroscience↗

bioRxiv2023

No evidence for a link between childhood (6-10y) cellular aging and brain morphology (12y) in a preregistered longitudinal study.

Animal studies show that early life environmental factors, such as stress and trauma, can have a significant impact on a variety of bodily processes, including cellular aging and brain development. However, whether cellular wear-and-tear effects are also associated with individual differences in brain structures in humans, remains unknown. In this pre-registered study in a community sample of children (N=94, Mean age=12.71 years), we prospectively investigated the predictive value of two markers of cellular aging in childhood (at age 6 and 10) for brain morphology in early adolescence (age 12). More specifically, we associated buccal cell telomere length and epigenetic age in childhood to individual differences in adolescent whole-brain grey matter volume (GMV) including volumes of three regions of interest that have been found to be sensitive to effects of early life stress (i.e. amygdala, hippocampus, (pre)frontal cortex -PFC). Multiple regression analyses revealed no significant associations between childhood cellular aging (at 6 and 10 years) and early adolescent brain morphology. Exploratory Bayesian analyses indicated moderate to strong evidence for the null-findings. These results suggest that although our sample is modest, the associations between middle childhood cellular aging and early adolescent brain morphology are, if they do exist, likely not particularly large in community children. Future work should investigate whether these effects are similarly absent in large samples, in samples with a higher risk profile and in samples characterized by different age ranges. Highlights (3-5)- Investigation of cellular aging in relation to brain morphology in a community sample (N=95) - Epigenetic aging and telomere shortening were not associated with brain structure - Exploratory Bayesian Analyses reveal moderate to strong evidence for null findings - No association was found between cellular aging and white matter volume

neuroscience↗

bioRxiv2023

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

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation. Significance StatementThis paper enriches the recent debates on the challenges of linking variation in brain structure to phenotypic differences (Marek et al., 2022). We demonstrate that using latent variables (to improve power), structural equation modelling (to allow greater flexibility in linking brain to behaviour), and by simultaneously incorporating multiple measures of grey and white matter in a large sample, we demonstrate relatively strong and robust brain-behaviour associations, which highlight the complementarity of grey and white matter metrics in predicting cognitive performance as well as the importance of incorporating the full complexity of these associations over 1-to-1 linkages. This finding should lead researchers to consider integrating both grey and white matter measures when demonstrating a more comprehensive picture of brain-cognition relationships.

neuroscience↗

bioRxiv2022

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.

neuroscience↗

bioRxiv2021

Brain age relates to early life factors but not to accelerated brain aging

Brain age is a widely used index for quantifying individuals brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

neuroscience↗

bioRxiv2019

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N=551 (N=165 imaging), age range: 5-18 years, NKI-Rockland: N=337 (N=65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.

neuroscience↗

bioRxiv2018

Lifestyle activities in mid-life contribute to cognitive reserve in late-life, independent of education, occupation and late-life activities

IntroductionThis study tested the hypothesis that mid-life intellectual, physical and social activities contribute to cognitive reserve (CR).\n\nMethods205 individuals (196 with MRI) aged 66-88 from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com) were studied, with cognitive ability and structural brain health measured as fluid IQ and total grey matter volume, respectively. Mid-life activities were measured using the Lifetime of Experiences Questionnaire.\n\nResultsMultivariable linear regression found that mid-life activities (MA) made a unique contribution to late-life cognitive ability independent of education, occupation and late-life activities. Crucially, MA moderated the relationship between late-life cognitive ability and brain structure, with the cognitive ability of people with higher MA less dependent on their brain structure, consistent with the concept of CR.\n\nConclusions. Mid-life intellectual, physical and social activities contribute uniquely to CR. The modifiability of these activities has implications for public health initiatives aimed at dementia prevention.

neuroscience↗

bioRxiv2017

Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N=204 (N=32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N=176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and {Omega}nyx).\n\nHighlightsO_LIWe describe Latent change score modelling as a flexible statistical tool\nC_LIO_LIKey developmental questions can be readily formalized using LCS models\nC_LIO_LIWe provide accessible open source code and software examples to fit LCS models\nC_LIO_LIWhite matter structural change is negatively correlated with processing speed gains\nC_LIO_LIFrontal lobe thinning in adolescence is more variable in males than females\nC_LI

neuroscience↗