Is Short Sleep Bad for the Brain? Brain Structure and Cognitive Function in Short Sleepers.

Many sleep less than recommended without experiencing daytime sleepiness. 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 using a cross-sectional and longitudinal sample of 47,029 participants of both sexes (20-89 years) from the Lifebrain consortium, Human Connectome project (HCP) and UK Biobank (UKB), with measures of self-reported sleep, including 51,295 MRIs of the brain and cognitive tests. A total of 740 participants who reported to sleep <6 h did not experience daytime sleepiness or sleep problems/disturbances interfering with falling or staying asleep. These short sleepers showed significantly larger regional brain volumes than both short sleepers with daytime sleepiness and sleep problems (n = 1742) and participants sleeping the recommended 7-8 h (n = 3886). However, both groups of short sleepers showed slightly lower general cognitive function (GCA), 0.16 and 0.19 SDs, respectively. Analyses using accelerometer-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 associations with brain morphometry and that sleepiness and sleep problems may be more related to brain structural differences than duration. However, the slightly lower performance on tests of general cognitive abilities warrants closer examination in natural settings.SIGNIFICANCE STATEMENT Short habitual sleep is prevalent, with unknown consequences for brain health and cognitive performance. Here, we show that daytime sleepiness and sleep problems are more strongly related to regional brain volumes than sleep duration. However, participants sleeping ≤6 h had slightly lower scores on tests of general cognitive function (GCA). This indicates that sleep need is individual and that sleep duration per se is very weakly if at all related brain health, while daytime sleepiness and sleep problems may show somewhat stronger associations. The association between habitual short sleep and lower scores on tests of general cognitive abilities must be further scrutinized in natural settings.

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates.

It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

Educational attainment does not influence brain aging.

Education has been related to various advantageous lifetime outcomes. Here, using longitudinal structural MRI data (4,422 observations), we tested the influential hypothesis that higher education translates into slower rates of brain aging. Cross-sectionally, education was modestly associated with regional cortical volume. However, despite marked mean atrophy in the cortex and hippocampus, education did not influence rates of change. The results were replicated across two independent samples. Our findings challenge the view that higher education slows brain aging.

Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.

Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.

Education and Income Show Heterogeneous Relationships to Lifespan Brain and Cognitive Differences Across European and US Cohorts.

Higher socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. We ask whether relationships between SES, brain volumes and cognitive ability differ across cohorts, by age and national origin. European and US cohorts covering the lifespan were studied (4-97 years, N = 500 000; 54 000 w/brain imaging). There was substantial heterogeneity across cohorts for all associations. Education was positively related to intracranial (ICV) and total gray matter (GM) volume. Income was related to ICV, but not GM. We did not observe reliable differences in associations as a function of age. SES was more strongly related to brain and cognition in US than European cohorts. Sample representativity varies, and this study cannot identify mechanisms underlying differences in associations across cohorts. Differences in neuroanatomical volumes partially explained 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. The relatively stronger SES-ICV associations rather are compatible with SES-brain volume relationships being established early in life, as ICV stabilizes in childhood. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.

The individuality paradigm: Automated longitudinal activity tracking of large cohorts of genetically identical mice in an enriched environment.

Personalized medicine intensifies interest in experimental paradigms that delineate sources of phenotypic variation. The paradigm of environmental enrichment allows for comparisons among differently housed laboratory rodents to unravel environmental effects on brain plasticity and related phenotypes. We have developed a new longitudinal variant of this paradigm, which allows to investigate the emergence of individuality, the divergence of individual behavioral trajectories under a constant genetic background and in a shared environment. We here describe this novel method, the "Individuality Paradigm," which allows to investigate mechanisms that drive individuality. Various aspects of individual activity are tracked over time to identify the contribution of the non-shared environment, that is the extent to which the experience of an environment differs between individual members of a population. We describe the design of this paradigm in detail, lay out its scientific potential beyond the published studies and discuss how it differs from other approaches to study individuality. The custom-built cage system, commercially marketed as "ColonyRack", allows mice to roam freely between 70 cages through connector tubes equipped with ring antennas that detect each animal's ID from an RFID transponder implanted in the animal's neck. The system has a total floor area of 2.74 m2 and its spatial resolution corresponds to the size of the individual cages. Spatiotemporally resolved antenna contacts yield longitudinal measures of individual behavior, including the powerful measure of roaming entropy (RE). The Individuality Paradigm provides a rodent model of the making of individuality and the impact of the 'non-shared' environment on life-course development.

Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2 * in healthy young adults.

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging-based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R1 ), and effective transverse relaxation rate (R2 *). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session-specific, day-specific, and residual sources of measurement error. For whole-brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R1 , and R2 * differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R1 and R2 * showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between-person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between-person differences. We conclude that R1 and R2 * might carry only limited person-specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between-person differences in behavior in those regions.

Hippocampal and Parahippocampal Gray Matter Structural Integrity Assessed by Multimodal Imaging Is Associated with Episodic Memory in Old Age.

Maintained structural integrity of hippocampal and cortical gray matter may explain why some older adults show rather preserved episodic memory. However, viable measurement models for estimating individual differences in gray matter structural integrity are lacking; instead, findings rely on fallible single indicators of integrity. Here, we introduce multitrait-multimethod methodology to capture individual differences in gray matter integrity, based on multimodal structural imaging in a large sample of 1522 healthy adults aged 60-88 years from the Berlin Aging Study II, including 333 participants who underwent magnetic resonance imaging. Structural integrity factors expressed the common variance of voxel-based morphometry, mean diffusivity, and magnetization transfer ratio for each of four regions of interest: hippocampus, parahippocampal gyrus, prefrontal cortex, and precuneus. Except for precuneus, the integrity factors correlated with episodic memory. Associations with hippocampal and parahippocampal integrity persisted after controlling for age, sex, and education. Our results support the proposition that episodic memory ability in old age benefits from maintained structural integrity of hippocampus and parahippocampal gyrus. Exploratory follow-up analyses on sex differences showed that this effect is restricted to men. Multimodal factors of structural brain integrity might help to improve our biological understanding of human memory aging.

Poor Self-Reported Sleep is Related to Regional Cortical Thinning in Aging but not Memory Decline-Results From the Lifebrain Consortium.

We examined whether sleep quality and quantity are associated with cortical and memory changes in cognitively healthy participants across the adult lifespan. Associations 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). In additional analyses, we tested coherence with cell-specific gene expression maps from the Allen Human Brain Atlas, and relations to changes in memory performance. "PSQI # 1 Subjective sleep quality" and "PSQI #5 Sleep disturbances" were related to thinning of the right lateral temporal cortex, with lower quality and more disturbances being associated with faster thinning. The association with "PSQI #5 Sleep disturbances" emerged after 60 years, especially in regions with high expression of genes related to oligodendrocytes and S1 pyramidal neurons. None of the sleep scales were related to a longitudinal change in episodic memory function, suggesting that sleep-related cortical changes were independent of cognitive decline. The relationship to cortical brain change suggests that self-reported sleep parameters are relevant in lifespan studies, but small effect sizes indicate that self-reported sleep is not a good biomarker of general cortical degeneration in healthy older adults.

A common polymorphism in the dopamine transporter gene predicts working memory performance and in vivo dopamine integrity in aging.

Dopamine (DA) integrity is suggested as a potential cause of individual differences in working memory (WM) performance among older adults. Still, the principal dopaminergic mechanisms giving rise to WM differences remain unspecified. Here, 61 single-nucleotide polymorphisms, located in or adjacent to various dopamine-related genes, were assessed for their links to WM performance in a sample of 1313 adults aged 61-80 years from the Berlin Aging Study II. Least Absolute Shrinkage and Selection Operator (LASSO) regression was conducted to estimate associations between polymorphisms and WM. Rs40184 in the DA transporter gene, SLC6A3, showed allelic group differences in WM, with T-carriers performing better than C homozygotes (p<0.01). This finding was replicated in an independent sample from the Cognition, Brain, and Aging study (COBRA; baseline: n = 181, ages: 64-68 years; 5-year follow up: n = 129). In COBRA, in vivo DA integrity was measured with 11C-raclopride and positron emission tomography. Notably, WM as well as in vivo DA integrity was higher for rs40184 T-carriers at baseline (p<0.05 for WM and caudate and hippocampal D2-receptor availability) and at the 5-year follow-up (p<0.05 for WM and hippocampal D2 availability). Our findings indicate that individual differences in DA transporter function contribute to differences in WM performance in old age, presumably by regulating DA availability.

Meta-analysis of generalized additive models in neuroimaging studies.

Analyzing data from multiple neuroimaging studies has great potential in terms of increasing statistical power, enabling detection of effects of smaller magnitude than would be possible when analyzing each study separately and also allowing to systematically investigate between-study differences. Restrictions due to privacy or proprietary data as well as more practical concerns can make it hard to share neuroimaging datasets, such that analyzing all data in a common location might be impractical or impossible. Meta-analytic methods provide a way to overcome this issue, by combining aggregated quantities like model parameters or risk ratios. Most meta-analytic tools focus on parametric statistical models, and methods for meta-analyzing semi-parametric models like generalized additive models have not been well developed. Parametric models are often not appropriate in neuroimaging, where for instance age-brain relationships may take forms that are difficult to accurately describe using such models. In this paper we introduce meta-GAM, a method for meta-analysis of generalized additive models which does not require individual participant data, and hence is suitable for increasing statistical power while upholding privacy and other regulatory concerns. We extend previous works by enabling the analysis of multiple model terms as well as multivariate smooth functions. In addition, we show how meta-analytic p-values can be computed for smooth terms. The proposed methods are shown to perform well in simulation experiments, and are demonstrated in a real data analysis on hippocampal volume and self-reported sleep quality data from the Lifebrain consortium. We argue that application of meta-GAM is especially beneficial in lifespan neuroscience and imaging genetics. The methods are implemented in an accompanying R package metagam, which is also demonstrated.

Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults.

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

Optimal planned missing data design for linear latent growth curve models.

Longitudinal data collection is a time-consuming and cost-intensive part of developmental research. Wu et al. (2016) discussed planned missing (PM) designs that are similar in efficiency to complete designs but require fewer observations per person. The authors reported optimal PM designs for linear latent growth curve models based on extensive Monte Carlo simulations. They called for further formal investigation of the question as to how much the proposed PM mechanisms influence study design efficiency to arrive at a better understanding of PM designs. Here, we propose an approximate solution to the design problem by comparing the asymptotic effective errors of PM designs. Effective error was previously used to find optimal longitudinal study designs for complete data designs; here, we extend the approach to planned missing designs. We show how effective error is a metric for comparing the efficiency of study designs with both planned and unplanned missing data, and how earlier simulation-based results for PM designs can be explained by an asymptotic solution. Our approach is computationally more efficient than Wu et al.'s approach and leads to a better understanding of how various design factors, such as the number of measurement occasions, their temporal arrangement, attrition rates, and PM design patterns interact and how they conjointly determine design efficiency. We provide R scripts to calculate effective errors in various scenarios of PM designs.

Identifying predictors of within-person variance in MRI-based brain volume estimates.

Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-person variance. To this end, we applied both classical statistical methods and machine-learning-inspired approaches to structural magnetic resonance imaging (sMRI) data of six participants aged 24-31 years gathered at 40-50 occasions distributed over 6-8 months from the Day2day study. We explored the within-person associations between 21 variables covering physiological, affective, social, and environmental factors and global measures of brain volume estimated by VBM8 and FreeSurfer. Time since the first scan was reliably associated with Freesurfer estimates of grey matter volume and total cortex volume, in line with a rate of annual brain volume shrinkage of about 1 percent. For the same two structural measures, time of day also emerged as a reliable predictor with an estimated diurnal volume decrease of, again, about 1 percent. Furthermore, we found weak predictive evidence for the number of steps taken on the previous day and testosterone levels. The results suggest a need to control for time-of-day effects in sMRI research. In particular, we recommend that researchers interested in assessing longitudinal change in the context of intervention studies or longitudinal panels make sure that, at each measurement occasion, (a) a given participant is measured at the same time of day; (b) all participants are measured at about the same time of day. Furthermore, the potential effects of physical activity, including moderate amounts of aerobic exercise, and testosterone levels on MRI-based measures of brain structure deserve further investigation.

Food for thought: association between dietary tyrosine and cognitive performance in younger and older adults.

The fact that tyrosine increases dopamine availability that, in turn, may enhance cognitive performance has led to numerous studies on healthy young participants taking tyrosine as a food supplement. As a result of this dietary intervention, participants show performance increases in working memory and executive functions. However, the potential association between habitual dietary tyrosine intake and cognitive performance has not been investigated to date. The present study aims at clarifying the association of episodic memory (EM), working memory (WM) and fluid intelligence (Gf), and tyrosine intake in younger and older adults. To this end, we acquired habitual tyrosine intake (food frequency questionnaire) from 1724 participants of the Berlin Aging Study II (1383 older adults, 341 younger adults) and modelled its relations to cognitive performance assessed in a broad battery of cognitive tasks using structural equation modeling. We observed a significant association between tyrosine intake and the latent factor capturing WM, Gf, and EM in the younger and the older sample. Due to partial strong factorial invariance between age groups for a confirmatory factor analysis on cognitive performance, we were able to compare the relationship between tyrosine and cognition between age groups and found no difference. Above and beyond previous studies on tyrosine food supplementation the present result extend this to a cross-sectional association between habitual tyrosine intake levels in daily nutrition and cognitive performance (WM, Gf, and EM). This corroborates nutritional recommendations that are thus far derived from single-dose administration studies.

White matter and memory in healthy adults: Coupled changes over two years.

Numerous cross-sectional studies have used diffusion tensor imaging (DTI) to link age-related differences in white matter (WM) anisotropy and concomitant decrements in cognitive ability. Due to a dearth of longitudinal evidence, the relationship between changes in diffusion properties of WM and cognitive performance remains unclear. Here we examine the relationship between two-year changes in WM organization and cognitive performance in healthy adults (N=96, age range at baseline=18-79 years). We used latent change score models (LCSM) to evaluate changes in age-sensitive cognitive abilities - fluid intelligence and associative memory. WM changes were assessed by fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) in WM regions that are considered part of established memory networks and exhibited individual differences in change. In modeling change, we postulated reciprocal paths between baseline measures and change factors, within and between WM and cognition domains, and accounted for individual differences in baseline age. Although baseline cross-sectional memory performance was positively associated with FA and negatively with RD, longitudinal effects told an altogether different story. Independent of age, longitudinal improvements in associative memory were significantly associated with linear reductions in FA and increases in RD. The present findings demonstrate the sensitivity of DTI-derived indices to changes in the brain and cognition and affirm the importance of longitudinal models for evaluating brain-cognition relations.

Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance.

In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.

Grey-matter structure in cortical and limbic regions correlates with general cognitive ability in old age.

According to the maintenance hypothesis (Nyberg et al., 2012), structural integrity of the brain's grey matter helps to preserve cognitive functioning into old age. A corollary of this hypothesis that can be tested in cross-sectional data is that grey-matter structural integrity and general cognitive ability are positively associated in old age. Building on Köhncke et al. (2021), who found that region-specific latent factors of grey-matter integrity are positively associated with episodic memory ability among older adults, we examine associations between general factors of grey-matter integrity and a general factor of cognitive ability in a cross-sectional sample of 1466 participants aged 60-88 years, 319 of whom contributed imaging data. Indicator variables based on T1-weighted images (voxel-based morphometry, VBM), magnetization-transfer imaging (MT), and diffusion tensor imaging-derived mean diffusivity (MD) had sufficient portions of variance in common to establish latent factors of grey-matter structure for a comprehensive set of regions of interest (ROI). Individual differences in grey-matter factors were positively correlated across neocortical and limbic areas, allowing for the definition of second-order, general factors for neocortical and limbic ROI, respectively. Both general grey-matter factors were positively correlated with general cognitive ability. For the basal ganglia, the three modality-specific indicators showed heterogenous loading patterns, and no reliable associations of the general grey-matter factor to general cognitive ability were found. To provide more direct tests of the maintenance hypothesis, we recommend applying the present structural modeling approach to longitudinal data, thereby enhancing the physiological validity of latent constructs of brain structure.

A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models.

We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic systems, often leading to improvements in convergence and computation time. It is applicable to models of a special form, where a subset of parameters enters the objective linearly. Recently, Kreiberg et al. (Struct Equ Model Multidiscip J 28(5):725-739, 2021. https://doi.org/10.1080/10705511.2020.1835484 ) have shown that this is also the case for factor analysis models. We generalize this result to all linear SEMs. To that end, we show that undirected effects (variances and covariances) and mean parameters enter the objective linearly, and therefore, in the least squares estimation of structural equation models, only the directed effects have to be obtained iteratively. For model classes without unknown directed effects, SNLLS can be used to analytically compute least squares estimates. To provide deeper insight into the nature of this result, we employ trek rules that link graphical representations of structural equation models to their covariance parametrization. We further give an efficient expression for the gradient, which is crucial to make a fast implementation possible. Results from our simulation indicate that SNLLS leads to improved convergence rates and a reduced number of iterations.