Biology subjects

Kievit, R. A.

Publications and source records attributed to Kievit, R. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Adversity and adolescent brain development: differential associations with grey and white matter across two longitudinal cohorts

Globally, 60% of the population has experienced at least one type of adversity (e.g., emotional abuse, bullying) across infancy, childhood, and adolescence. Such experiences have been linked to an increased risk for mental health disorders. Changes in brain structure following experiences of childhood adversity have been hypothesised to be a mechanistic pathway explaining later mental health issues. However, to understand how changes in brain structure might mediate the effects of adversity, it is essential to identify which underlying neuronal processes may be affected by different types of adverse experiences. A key open question is whether grey or white matter is more vulnerable to adversity, as these two structures reflect distinct neurobiological mechanisms. This study investigated whether differences in trajectories of grey and white matter development during adolescence can be explained by exposure to different types of adversity. We applied the Adverse Adolescent Experiences Framework (Pollmann et al., 2025) categorising adversity into four levels: Intrapersonal (e.g., accidents), Caregiver (e.g., emotional neglect), Peer (e.g., bullying), and Community (e.g., neighbourhood safety). Exposure to each of the four factors was estimated through principal components analyses. We analysed two large longitudinal datasets: the Adolescent Brain Cognitive Development study ([~]12,000 adolescents measured at ages 10, 12, and 14) and the IMAGEN study ([~]1,400 adolescents measured at ages 14, 19, and 22). Using latent growth curve models, we captured individual differences in brain development by estimating baseline levels (intercepts) and rates of change (slopes) for total grey matter volume and mean white matter fractional anisotropy. In both cohorts, we found significant interindividual variability in baseline levels and rates of change for both grey matter volume and fractional anisotropy. Caregiver, Peer, and Community adversities were negatively associated only with the intercepts of grey matter volume and white matter fractional anisotropy. Importantly, associations differed between grey and white matter. In ABCD, Peer and Community adversities were more strongly associated with grey matter volume intercepts. In contrast, in IMAGEN, Caregiver, Peer and Community adversities were more strongly linked to white matter fractional anisotropy intercepts. This suggests that adversity has unique associations with grey and white matter, rather than exerting a uniform influence on brain structure. By demonstrating that different environments generate distinct biological associations with brain maturation, this work underscores the need to consider both grey and white matter when assessing the neurodevelopmental pathways to outcomes across the lifespan.

neuroscience↗

bioRxiv2024

Reliability of structural brain change in cognitively healthy adult samples.

In neuroimaging research, tracking individuals over time is key to understanding the interplay between brain changes and genetic, environmental, or cognitive factors across the lifespan. Yet, the extent to which we can estimate the individual trajectories of brain change over time with precision remains uncertain. In this study, we estimated the reliability of structural brain change in cognitively healthy adults from multiple samples and assessed the influence of follow-up time and number of observations. Estimates of cross-sectional measurement error and brain change variance were obtained using the longitudinal FreeSurfer processing stream. Our findings showed, on average, modest longitudinal reliability with two years of follow-up. Increasing the follow-up time was associated with a substantial increase in longitudinal reliability while the impact of increasing the number of observations was comparatively minor. On average, 2-year follow-up studies require {approx}2.7 and {approx}4.0 times more individuals than designs with follow-ups of 4 and 6 years to achieve comparable statistical power. Subcortical volume exhibited higher longitudinal reliability compared to cortical area, thickness, and volume. The reliability estimates were comparable to those estimated from empirical data. The reliability estimates were affected by both the cohorts age where younger adults had lower reliability of change, and the preprocessing pipeline where the FreeSurfers longitudinal stream was notably superior than the cross-sectional. Suboptimal reliability inflated sample size requirements and compromised the ability to distinguish individual trajectories of brain aging. This study underscores the importance of long-term follow-ups and the need to consider reliability in longitudinal neuroimaging research.

neuroscience↗

bioRxiv2023

Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer's disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimers disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above [~]50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.

neuroscience↗

bioRxiv2022

Is short sleep bad for the brain? Brain structure and cognitive function in short sleepers

Many sleep less than recommended without experiencing daytime tiredness. According to prevailing views, short sleep increases risk of lower brain health and cognitive function. Chronic mild sleep deprivation could cause undetected sleep debt, negatively affecting cognitive function and brain health. However, it is possible that some have less sleep need and are more resistant to negative effects of sleep loss. We investigated this question using a combined cross-sectional and longitudinal sample of 47,029 participants (age 20-89 years) with measures of self-reported sleep, including 51,295 MRIs of the brain and cognitive tests. 701 participants who reported to sleep < 6 hours did not experience daytime tiredness or sleep problems. These short sleepers showed significantly larger regional brain volumes than both short sleepers with daytime tiredness and sleep problems (n = 1619) and participants sleeping the recommended 7-8 hours (n = 3754). However, both groups of short sleepers showed slightly lower general cognitive function, 0.16 and 0.19 standard deviations, respectively. Analyses using acelerometer-estimated sleep duration confirmed the findings, and the associations remained after controlling for body mass index, depression symptoms, income and education. The results suggest that some people can cope with less sleep without obvious negative consequences for brain morphometry, in line with a view on sleep need as individualized. Tiredness and sleep problems seem to be more relevant for brain structural differences than sleep duration per se. However, the slightly lower performance on tests of general cognitive function warrants closer examination by experimental designs in natural settings. Significance statementShort habitual sleep is prevalent, with unknown consequences for brain health and cognitive performance. Here we show that daytime tiredness and sleep problems are more important variables for regional brain volumes than sleep duration. However, participants sleeping < 6 hours had slightly lower scores on tests of general cognitive function. This indicates that sleep need is individual, and that sleep duration per se may be a less relevant variable for brain health than daytime tiredness and sleep problems. The association between habitual short sleep and lower scores on tests of general cogntitive function must be further scrutinized in natural settings.

neuroscience↗

bioRxiv2022

Sleep duration and brain structure - phenotypic associations and genotypic covariance

The question of how much sleep is best for the brain attracts scientific and public interest, and there is concern that insuficient sleep leads to poorer brain health. However, it is unknown how much sleep is sufficient and how much is too much. We analyzed 51,295 brain magnetic resonnance images from 47,039 participants, and calculated the self-reported sleep duration associated with the largest regional volumes and smallest ventricles relative to intracranial volume (ICV) and thickest cortex. 6.8 hours of sleep was associated with the most favorable brain outcome overall. Critical values, defined by 95% confidence intervals, were 5.7 and 7.9 hours. There was regional variation, with for instance the hippocampus showing largest volume at 6.3 hours. Moderately long sleep (> 8 hours) was more strongly associated with smaller relative volumes, thinner cortex and larger ventricles than even very short sleep (< 5 hours), but effect sizes were modest. People with larger ICV reported longer sleep (7.5 hours), so not correcting for ICV yielded longer durations associated with maximal volume. Controlling for socioeconomic status, body mass index and depression symptoms did not alter the associations. Genetic analyses showed that genes related to longer sleep in short sleepers were related to shorter sleep in long sleepers. This may indicate a genetically controlled homeostatic regulation of sleep duration. Mendelian randomization analyses did not suggest sleep duration to have a causal impact on brain structure in the analyzed datasets. The findings challenge the notion that habitual short sleep is negatively related to brain structure. Significance statementAccording to consensus recommendations, adults should sleep between 7 and 9 hours to optimize their health. We found that sleeping less than the recommended amount was associated with greater regional brain volumes relative to intracranial volume, and very short sleep was only weakly related to smaller volumes. Genetic analyses did not show causal effects of sleep duration on brain structure. Taken together, the results suggest that habitual short sleep is not an important contributor to lower brain volumes in adults on a group level, and that large individual dfferences in sleep need likely exist.

neuroscience↗

bioRxiv2022

The midpoint of cortical thinning between late childhood and early adulthood differs across individuals and regions: Evidence from longitudinal modelling in a 12-wave sample

Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (N = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.

neuroscience↗

bioRxiv2021

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

The thickness and surface area of cortex are genetically distinct aspects of brain structure, and may be affected differently by age. However, their potential to differentially predict age and cognitive abilities has been largely overlooked, likely because they are typically aggregated into the commonly used measure of volume. In a large sample of healthy adults (N=647, aged 18-88), we investigated the brain-age and brain-cognition relationships of thickness, surface area, and volume, plus five additional morphological shape metrics. Cortical thickness was the metric most strongly associated with age cross-sectionally, as well as exhibiting the steepest longitudinal change over time (subsample N=261, aged 25-84). In contrast, surface area was the best single predictor of age-residualized cognitive abilities (fluid intelligence), and changes in surface area were most strongly associated with cognitive change over time. These findings were replicated in an independent dataset (N=1345, aged 18-93). Our results suggest that cortical thickness and surface area make complementary contributions the age-brain-cognition triangle, and highlight the importance of considering these volumetric components separately.

neuroscience↗

bioRxiv2021

Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

neuroscience↗

bioRxiv2020

Bridging brain and cognition: A multilayer network analysis of brain structural covariance and general intelligence in a developmental sample of struggling learners

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

neuroscience↗

bioRxiv2020

Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts

Socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. Here we show that relationships between SES, brain volumes and general cognitive ability differ significantly across European and US cohorts (4-97 years, N {approx} 500,000; 54,000 with brain imaging). Education was positively related to intracranial (ICV) and total brain gray matter (GM) volume. Income was related to ICV, but not GM. Relationships varied significantly across samples, and SES was more strongly related to brain and cognition in US than European cohorts. Differences in neuroanatomical volumes explained part of the SES-cognition relationships. SES was more strongly related to ICV than to GM, implying that SES-cognition relations in adulthood are less likely grounded in neuroprotective effects on GM volume in aging. Rather, a relationship may be established early in life. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.

neuroscience↗

bioRxiv2020

Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's Disease

Normal aging and Alzheimers Disease (AD) are accompanied by large-scale alterations in brain organization that undermine brain function. Although hemispheric asymmetry is a global organizing feature of cortex thought to promote brain efficiency, current descriptions of cortical thinning in aging and AD have largely overlooked cortical asymmetry. Consequently, the foundational question of whether and where the cerebral hemispheres change at different rates in aging and AD remains open. First, applying vertex-wise data-driven clustering in a longitudinal discovery sample (aged 20-89; 2577 observations; 1851 longitudinal) we identified cortical regions exhibiting similar age-trajectories of asymmetry across the adult lifespan. Next, we sought replication in 4 independent longitudinal aging cohorts. We show that higher-order regions of cortex that exhibit pronounced asymmetry at age ~20 also show asymmetry change in aging. Results revealed that both leftward and rightward asymmetry is progressively lost on a similar time-scale across adult life. Hence, faster thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This simple organizational principle showed high consistency across multiple aging cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Finally, we show that regions exhibiting gradual asymmetry-loss over healthy adult life exhibit faster asymmetry-change in AD. Overall, our results suggest a system-wide breakdown in the adaptive asymmetric organization of cortex across adult life which is further accelerated in AD, and may implicate thickness asymmetry as a viable marker for declining hemispheric specialization in aging and AD. SignificanceThe brain becomes progressively disorganized with age, and brain alterations accelerated in Alzheimers disease may occur gradually over the lifespan. Although hemispheric asymmetry aids efficient network organization, efforts to identify structural markers of age-related decline have largely overlooked cortical asymmetry. Here we show the hemisphere that is thicker when younger, thins faster. This leads to progressive system-wide loss of regional thickness asymmetry across life. In multiple aging cohorts, asymmetry-loss showed high reproducibility topologically across cortex and similar timing-of-change in aging. Asymmetry-change was further accelerated in AD. Our findings uncover a new principle of brain aging - thicker homotopic cortex thins faster - and suggest we may have unveiled a structural marker for a widely-hypothesized decline in hemispheric specialization in aging and AD.

neuroscience↗

bioRxiv2020

The genetic organization of subcortical volumetric change is stable throughout the lifespan

While development and aging of the cerebral cortex show a similar topographic organization and are mainly governed by the same genes, it is unclear whether the same is true for subcortical structures, which follow fundamentally different ontogenetic and phylogenetic principles than the cerebral cortex. To test the hypothesis that genetically governed neurodevelopmental processes can be traced in subcortical structures throughout life, we analyzed a longitudinal magnetic resonance imaging dataset (n = 974, age 4-89 years), identifying five clusters of longitudinal change in development. With some exceptions, these clusters followed placement along the cranial axis in embryonic brain development, suggesting continuity in the pattern of change from prenatal stages. Developmental change patterns were conserved through the lifespan and predicted general cognitive function in an age-invariant manner. The results were replicated in longitudinal data from the Lifebrain consortium (n = 756, age 19-83 years). Genetic contributions to longitudinal brain changes were calculated from the Vietnam Era Twin Study of Aging (n = 331 male twins, age 51-60 years), revealing that distinct sets of genes tended to govern change for each developmental cluster. This finding was confirmed with single nucleotide polymorphisms and cross-sectional MRI data from the UK Biobank (n = 20,588, age 40-69), demonstrating significantly higher co-heritability among structures belonging to the same developmental clusters. Together, these results suggest that coordination of subcortical change adheres to fundamental principles of lifespan continuity, genetic organization and age-invariant relationships to cognitive function. Significance statementHere we show that subcortical change during childhood development is organized in clusters. These clusters tend to follow the main gradient of embryonic brain development, and are stable across life. This means that subcortical regions changing together in childhood also change together throughout the rest of life, in accordance with a lifespan perspective on brain development and aging. Twin and single nucleotide polymorphism-based heritability analyses in middle-aged and older adults showed that volume and volume change of regions within each developmental cluster tended to be governed by the same sets of genes. Thus, volumetric changes across subcortical regions are tightly organized, and the coordinated change can be described in a lifespan perspective according to ontogenetic and genetic influences.

neuroscience↗

bioRxiv2020

Self-reported sleep problems are related to cortical thinning in aging but not memory decline and amyloid-β accumulation - results from the Lifebrain consortium

BackgroundOlder persons with poor sleep are more likely to develop neurodegenerative disease, but the causality underlying this association is unclear. To move towards explanation, we examine whether sleep quality and quantity are similarly associated with brain changes across the adult lifespan. MethodsAssociations between self-reported sleep parameters (Pittsburgh Sleep Quality Index;PSQI) and longitudinal cortical change were tested using five samples from the Lifebrain consortium (n=2205, 4363 MRIs, 18-92 years). Analyses were augmented by considering episodic memory change, gene expression from the Allen Human Brain Atlas, and amyloid-beta (A{beta}) accumulation (n=1980). ResultsPSQI components sleep problems and low sleep quality were related to thinning of the right lateral temporal cortex. The association with sleep problems emerged after 60 years, especially in regions with high expression of genes related to oligodendrocytes and S1 pyramidal neurons. BMI and symptoms of depression had negligible effects. Sleep problems were neither related to longitudinal change in episodic memory function nor to A{beta} accumulation, suggesting that sleep-related cortical changes were independent of AD neuropathology and cognitive decline. ConclusionWorse self-reported sleep in later adulthood was associated with more cortical thinning in regions of high expression of genes related to oligodendrocytes and S1 pyramidal neurons, but not to A{beta} accumulation or memory decline. The relationship to cortical brain change suggests that self-reported sleep parameters are relevant in lifespan studies, but small effect sizes, except for a few restricted regions, indicate that self-reported sleep is not a good biomarker of general cortical degeneration in healthy older adults.

neuroscience↗

bioRxiv2020

Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data

Dimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables, making subsequent analyses and their interpretation tractable. One popular technique is Exploratory Factor Analysis (EFA), used by cognitive neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA using structured residuals (EFAST), and (c) apply this technique to three large and varied brain imaging datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

neuroscience↗

bioRxiv2019

Self-reported sleep relates to hippocampal atrophy across the adult lifespan - results from the Lifebrain consortium

BackgroundPoor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan.\n\nMethodsSelf-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18-90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants, covering up to 11 years. Cross-sectional analyses were repeated in a sample of 21390 participants from the UK Biobank.\n\nResultsThe relationship between self-reported sleep and age differed across sleep items. Sleep duration, efficiency, problems, and use of medication worsened monotonously with age, whereas subjective sleep quality, sleep latency, and daytime tiredness improved. Women reported worse sleep in general than men, but the relationship to age was similar. No cross-sectional sleep - hippocampal volume relationships was found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing on average 0.22% greater annual loss than low scorers. Simulations showed that longitudinal effects were too small to be detected as age-interactions in cross-sectional analyses.\n\nConclusionsWorse self-reported sleep is associated with higher rates of hippocampal decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation.

neuroscience↗

bioRxiv2018

The neurocognitive architecture of fluid ability in children and adolescents

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge, and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modelled the neurocognitive architecture of fluid intelligence in two cohorts: CALM (N = 551, aged 5 - 17 years) and NKI-RS (N = 335, aged 6 - 17 years). We used multivariate Structural Equation Modelling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7 - 12 years. This age-effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.

neuroscience↗

bioRxiv2018

Domain-general enhancements of metacognitive ability through adaptive training

The metacognitive ability to introspect about self-performance varies substantially across individuals. Given that effective monitoring of performance is deemed important for effective behavioural control, intervening to improve metacognition may have widespread benefits, for example in educational and clinical settings. However, it is unknown whether and how metacognition can be systematically improved through training independently of task performance, or whether metacognitive improvements generalize across different task domains. Across 8 sessions, here we provided feedback to two groups of participants in a perceptual discrimination task: an experimental group (N = 29) received feedback on their metacognitive judgments, while an active control group (N = 32) received feedback on their decision performance only. Relative to the control group, adaptive training led to increases in metacognitive calibration (as assessed by Brier scores) which generalized both to untrained stimuli and an untrained task (recognition memory). Leveraging signal detection modeling we found that metacognitive improvements were driven both by changes in metacognitive efficiency (meta-d/d) and confidence level, and that later increases in metacognitive efficiency were positively mediated by earlier shifts in confidence. Our results reveal a striking malleability of introspection and indicate the potential for a domain-general enhancement of metacognitive abilities.

animal behavior and cognition↗

bioRxiv2018

Motor learning decline with age is related to differences in the explicit memory system

The ability to adapt ones movements to changes in the environment is fundamental in everyday life, but this ability changes across the lifespan. Although often regarded as an implicit process, recent research has also linked motor adaptation with explicit learning processes. To understand how these processes contribute to differences in motor adaptation with age, we combined a visuomotor learning paradigm with cognitive tasks that measure implicit and explicit processes, and structural brain imaging. In a large population-based cohort from the Cambridge Centre for Ageing and Neuroscience (n=322, aged 18-89 years) we first confirmed that the degree of adaptation to an angular perturbation of visual feedback declined with age. There were no associations between adaptation and sensory attenuation, which has been previously hypothesised to contribute to implicit motor learning. However, interactions between age and scores on two independent memory tasks showed that explicit memory performance was a progressively stronger determinant of motor learning with age. Similarly, interactions between age and grey matter volume in the medial temporal lobe, amygdala and hippocampus showed that grey matter volume in these regions became a stronger determinant of adaptation in older adults. The convergent behavioural and structural imaging results suggest that age-related differences in the explicit memory system is a contributor to the decline in motor adaptation in older age. These results may reflect the more general compensatory reliance on cognitive strategies to maintain motor performance with age.\n\nSIGNIFICANCE STATEMENTThe central nervous system has a remarkable capacity to learn new motor skills and adapt to new environmental dynamics. This capacity is impaired with age, and in many brain disorders. We find that explicit memory performance and its associated medial temporal brain regions deteriorate with age, but the association between this brain system and individual differences in motor learning becomes stronger in older adults. We propose that these results reflect an increased reliance on cognition in order to maintain adaptive motor skill performance. This difference in learning strategy has implications for interventions to improve motor skills in older adults.

neuroscience↗