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Contains fulltext : 248857.pdf (Publisher’s version ) (Open Access) 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.

Contains fulltext : 292515.pdf (Publisher’s version ) (Open Access) 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.

We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96 SD=5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Assessment, MoCA). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β=3.36, 95% CI [0.42-6.30]), and faster 25-year cognitive decline in letter fluency (β=-0.07, 95% CI [-0.13–-0.01]), and verbal reasoning (β=-0.05, 95% CI [-0.11–-0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected pF3,608=2.14, p=0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.

Highlights • Heterogeneous measures of WMHs are used in research and clinical practice. • Location and image intensity should be considered in the assessment of WMHs. • T1-hypointense WMHs were found to be associated with poorer cognitive performance. • Sub-classes of WMHs provide promising results for translation into the clinic., White matter hyperintensities (WMHs) on T2-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T1-weighted images (T1w) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and T1w-hypointense/nonT1w-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular T1w-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in T1w reveals specific associations with cognitive performance.

While all individuals are susceptible to age-related cognitive decline, significant inter- and intra-individual variability exists. However, the sources of this variation remain poorly understood. Here, we examined the association between 30-year trajectories of cognitive decline and multimodal indices of brain microstructure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort using 3T brain magnetic resonance images acquired at older age (mean age = 69.52 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 4.9 years) and late-life (mean age = 67.7 4.9). Using non-negative matrix factorization, we identified 10 brain microstructural components that integrate measures of cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two modes of variance that describe the association between cognition and brain microstructure. The first describes variations in 5 microstructural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning abilities, but a relative maintenance of lexical and semantic fluency from mid-to-late life. The second describes variations in 5 microstructural components that are associated with low mid-life performance in lexical fluency, semantic fluency and short-term memory performance, but a retention of abilities in multiple domains from mid-to-late life. The extent to which a subject loads onto a latent variables predicts their future cognitive performance 3.2 years later (mean age = 70.87 4.9). This data-driven approach highlights a complex pattern of brain-behavior relationships, wherein the same individuals express both decline and maintenance in function across cognitive domains and in brain structural features.

Contains fulltext : 228447.pdf (Publisher’s version ) (Open Access) 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. 17 p.

Funder: Norges Forskningsr��d; FundRef: http://dx.doi.org/10.13039/501100005416, Funder: Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg; FundRef: http://dx.doi.org/10.13039/501100012319, Funder: NIHR Biomedical Research Centre, Oxford; FundRef: http://dx.doi.org/10.13039/501100013373, Funder: Knut and Alice Wallenberg Foundation; FundRef: http://dx.doi.org/10.13039/501100004063, Funder: ICREA Academia Award, 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. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ��� 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ��� 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. 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.

Highlights • We harmonised measures of WMHs across two studies on healthy ageing. • Specific pre-processing strategies can increase comparability across studies. • Modelling of biological differences is crucial to provide calibrated measures., Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age., Using machine learning, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort, and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. The study presents a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve in older age.

Background Aortic stiffness is closely linked with cardiovascular diseases (CVDs), but recent studies suggest that it is also a risk factor for cognitive decline and dementia. However, the brain changes underlying this risk are unclear. We examined whether aortic stiffening during a 4-year follow-up in mid-to-late life was associated with brain structure and cognition in the Whitehall II Imaging Sub-study. Methods and findings The Whitehall II Imaging cohort is a randomly selected subset of the ongoing Whitehall II Study, for which participants have received clinical follow-ups for 30 years, across 12 phases. Aortic pulse wave velocity (PWV) was measured in 2007–2009 (Phase 9) and at a 4-year follow-up in 2012–2013 (Phase 11). Between 2012 and 2016 (Imaging Phase), participants received a multimodal 3T brain magnetic resonance imaging (MRI) scan and cognitive tests. Participants were selected if they had no clinical diagnosis of dementia and no gross brain structural abnormalities. Voxel-based analyses were used to assess grey matter (GM) volume, white matter (WM) microstructure (fractional anisotropy (FA) and diffusivity), white matter lesions (WMLs), and cerebral blood flow (CBF). Cognitive outcomes were performance on verbal memory, semantic fluency, working memory, and executive function tests. Of 542 participants, 444 (81.9%) were men. The mean (SD) age was 63.9 (5.2) years at the baseline Phase 9 examination, 68.0 (5.2) at Phase 11, and 69.8 (5.2) at the Imaging Phase. Voxel-based analysis revealed that faster rates of aortic stiffening in mid-to-late life were associated with poor WM microstructure, viz. lower FA, higher mean, and radial diffusivity (RD) in 23.9%, 11.8%, and 22.2% of WM tracts, respectively, including the corpus callosum, corona radiata, superior longitudinal fasciculus, and corticospinal tracts. Similar voxel-wise associations were also observed with follow-up aortic stiffness. Moreover, lower mean global FA was associated with faster rates of aortic stiffening (B = −5.65, 95% CI −9.75, −1.54, Bonferroni-corrected p < 0.0125) and higher follow-up aortic stiffness (B = −1.12, 95% CI −1.95, −0.29, Bonferroni-corrected p < 0.0125). In a subset of 112 participants who received arterial spin labelling scans, faster aortic stiffening was also related to lower cerebral perfusion in 18.4% of GM, with associations surviving Bonferroni corrections in the frontal (B = −10.85, 95% CI −17.91, −3.79, p < 0.0125) and parietal lobes (B = −12.75, 95% CI −21.58, −3.91, p < 0.0125). No associations with GM volume or WMLs were observed. Further, higher baseline aortic stiffness was associated with poor semantic fluency (B = −0.47, 95% CI −0.76 to −0.18, Bonferroni-corrected p < 0.007) and verbal learning outcomes (B = −0.36, 95% CI −0.60 to −0.12, Bonferroni-corrected p < 0.007). As with all observational studies, it was not possible to infer causal associations. The generalisability of the findings may be limited by the gender imbalance, high educational attainment, survival bias, and lack of ethnic and socioeconomic diversity in this cohort. Conclusions Our findings indicate that faster rates of aortic stiffening in mid-to-late life were associated with poor brain WM microstructural integrity and reduced cerebral perfusion, likely due to increased transmission of pulsatile energy to the delicate cerebral microvasculature. Strategies to prevent arterial stiffening prior to this point may be required to offer cognitive benefit in older age. Trial registration ClinicalTrials.gov NCT03335696, Sana Suri and colleagues investigate differences in brain structure and connectivity associated with aortic stiffening in older adults., Author summary Why was this study done? Stiffening of large arteries such as the aorta has been closely linked to the onset of heart diseases, but recent studies show that this may also increase risk for dementia. Understanding the brain changes which underlie this risk could help identify strategies to prevent or delay dementia in old age. What did the researchers do and find? We investigated 542 community-dwelling older adults who received 2 measurements of aortic stiffness, first at approximately 64 years old and again at approximately 68 years old. Shortly after that, at mean age 69 years, they underwent cognitive tests and brain magnetic resonance imaging (MRI) scans to assess the size, connections, and blood supply of different brain regions. We observed that a faster rate of aortic stiffening during the 4 years in mid-to-late life was linked to lower blood flow and poorer markers of brain connectivity across several brain regions. We also found that lower memory performance in older age was most closely linked to the first measures of aortic stiffness, taken at baseline, rather than the later measures of stiffness. What do these findings mean? Faster stiffening of the aorta in mid-to-older ages may influence brain health, specifically, the brain’s delicate blood vessels and the connectivity between different brain regions. However, in order to benefit memory in older age, strategies to prevent or slow down arterial stiffening may be required even earlier in the life span. Although this was a large study, it was conducted in a relatively well-educated and largely male sample, so future research on more representative samples are needed to allow us to generalise the findings to the wider population.

This cohort study examines trajectories of cognitive and social activities from midlife to late life and evaluates whether these trajectories are associated with brain structure, functional connectivity, and cognition., Key Points Question Are longitudinal trajectories of cognitive and social activities associated with brain structure and function? Findings In this cohort study of 574 adults followed up for a mean of 15 years, the level of cognitive activities was associated with measures of cognitive function but not magnetic resonance imaging measures of brain structure or functional connectivity after corrections for multiple comparisons. Meaning The findings suggest that a life course approach may better delineate the association between leisure activities and cognitive and brain health, with this study identifying cognitive activities as potential targets for intervention studies., Importance Prior neuroimaging studies have found that late-life participation in cognitive (eg, reading) and social (eg, visiting friends and family) leisure activities are associated with magnetic resonance imaging (MRI) markers of the aging brain, but little is known about the neural and cognitive correlates of changes in leisure activities during the life span. Objectives To examine trajectories of cognitive and social activities from midlife to late life and evaluate whether these trajectories are associated with brain structure, functional connectivity, and cognition. Design, Setting, and Participants This prospective cohort included participants enrolled in the Whitehall II study and its MRI substudy based in the UK. Participants provided information on their leisure activities at 5 times during calendar years 1997 to 1999, 2002 to 2004, 2006, 2007 to 2009, and 2011 to 2013 and underwent MRI and cognitive battery testing from January 1, 2012, to December 31, 2016. Data analysis was performed from October 7, 2017, to July 15, 2019. Main Outcome and Measures Growth curve models and latent class growth analysis were used to identify longitudinal trajectories of cognitive and social activities. Multiple linear regression was used to evaluate associations between activity trajectories and gray matter, white matter microstructure, functional connectivity, and cognition. Results A total of 574 individuals (468 [81.5%] men; mean [SD] age, 69.9 [4.9] years; median Montreal Cognitive Assessment score, 28 [interquartile range, 26–28]) were included in the present analysis. During a mean (SD) of 15 (4.2) years, cognitive and social activity levels increased during midlife before reaching a plateau in late life. Both baseline (global cognition: unstandardized β [SE], 0.955 [0.285], uncorrected P = .001; executive function: β [SE], 1.831 [0.499], uncorrected P

Background: Trajectories of depressive symptoms over the lifespan vary between people, but it is unclear whether these differences exhibit distinct characteristics in brain structure and function. Methods: In order to compare indices of white matter microstructure and cognitive characteristics of groups with different trajectories of depressive symptoms, we examined 774 participants of the Whitehall II Imaging Sub-study, who had completed the depressive subscale of the General Health Questionnaire up to nine times over 25 years. Twenty-seven years after the first examination, participants underwent magnetic resonance imaging to characterize white matter hyperintensities (WMH) and microstructure and completed neuropsychological tests to assess cognition. Twenty-nine years after the first examination, participants completed a further cognitive screening test. Outcomes: Using K-means cluster modelling, we identified five trajectory groups of depressive symptoms: consistently low scorers (“low”; n = 505, 62·5%), a subgroup with an early peak in depression scores (“early”; n = 123, 15·9%), intermediate scorers (“middle”; n = 89, 11·5%), a late symptom subgroup with an increase in symptoms towards the end of the follow-up period (“late”; n = 29, 3·7%), and consistently high scorers (“high”; n = 28, 3·6%). The late, but not the consistently high scorers, showed higher mean diffusivity, larger volumes of WMH and impaired executive function. In addition, the late subgroup had higher Framingham Stroke Risk scores throughout the follow-up period, indicating a higher load of vascular risk factors. Interpretation: Our findings suggest that tracking depressive symptoms in the community over time may be a useful tool to identify phenotypes that show different etiologies and cognitive and brain outcomes.

Background and Objectives A healthy brain is central to physical and mental well-being. In this multi-site, qualitative study, we investigated views and attitudes of adult participants in brain research studies on the brain and personalized brain health as well as interest in maintaining a healthy brain. Design and Methods We conducted individual interviews with 44 adult participants in brain research cohorts of the Lifebrain consortium in Spain, Norway, Germany, and the United Kingdom. The interviews were audio recorded, transcribed, and coded using a cross-country codebook. The interview data were analyzed using qualitative content analysis. Results Most participants did not focus on their own brain health and expressed uncertainty regarding how to maintain it. Those actively focusing on brain health often picked one specific strategy like diet or memory training. The participants were interested in taking brain health tests to learn about their individual risk of developing brain diseases, and were willing to take measures to maintain their brain health if personalized follow-up was provided and the measures had proven impact. The participants were interested in more information on brain health. No differences in responses were identified between age groups, sex, or countries. Discussion and Implications Concise, practical, personalized, and evidence-based information about the brain may promote brain health. Based on our findings, we have launched an ongoing global brain health survey to acquire more extensive, quantitative, and representative data on public perception of personalized brain health.

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.HIGHLIGHTSWe harmonised measures of WMHs across two studies on healthy ageingSpecific pre-processing strategies can increase comparability across studiesModelling of biological differences is crucial to provide calibrated measures

Background: Subjective cognitive complaints are common but it is unclear whether they indicate an underlying pathological process or reflect affective symptoms. Method: 800 community-dwelling older adults were drawn from the Whitehall II cohort. Subjective cognitive complaint inquiry for memory and concentration, a range of neuropsychological tests and multi-modal MRI were performed in 2012-2016. Subjective complaints were again elicited after one year. Group differences in grey and white matter, between those with and without subjective complaints, were assessed using voxel-based morphometry and tract-based spatial statistics, respectively. Mixed effects models assessed whether cognitive decline or depressive symptoms (over a 25-year period) were associated with later subjective complaints. Analyses were controlled for potential confounders and multiple comparisons. Results: Mean age of the sample at scanning was 69.8 years (±5.1, range: 60.3-84.6). Subjective memory complaints were common (41%) and predicted further similar complaints later (mean 1.4±1.4 years). There were no group differences in grey matter density or white matter integrity. Subjective complaints were not cross-sectionally or longitudinally associated with objectively assessed cognition. However, those with subjective complaints reported higher depressive symptoms (“poor concentration”: odds ratio=1.12, 95%CI 1.07-1.18; “poor memory”: odds ratio=1.18, 1.12-1.24). Conclusions: In our sample subjective complaints were consistent over time and reflected depressive symptoms but not markers of neurodegenerative brain damage or concurrent or future objective cognitive impairment. Clinicians assessing patients presenting with memory complaints should be vigilant for affective disorders. These results question the rationale for including subjective complaints in a spectrum with Mild Cognitive Impairment diagnostic criteria.

BackgroundAortic stiffness is closely linked with cardiovascular diseases, but recent studies suggest that it is also a risk factor for cognitive decline and dementia. However, the brain changes underlying this risk are unclear. We examined whether aortic stiffening in the transition from mid to late-life affects brain structure and cognition.Methods and FindingsAortic pulse wave velocity was measured in 2007-09 (Phase 9) and at a 4-year follow-up in 2012-13 (Phase 11) in the Whitehall II Imaging Sub-study cohort. Between 2012-2016 (Imaging Phase), participants received a multi-modal 3T brain magnetic resonance imaging (MRI) scans and cognitive tests. Participants were selected if they had no clinical diagnosis of dementia and no gross brain structural abnormalities. Voxel-based analyses were used to assess grey matter volume, white matter microstructure (fractional anisotropy and diffusivity), cerebral blood flow, and white matter lesions. Cognitive outcomes were performance on verbal memory, semantic fluency, working memory and executive function tests. Of 544 participants, 445 (81.8%) were men. The mean (SD) age was 63.9 (5.2) years at the baseline Phase 9 examination, 67.9 (5.3) years at Phase 11 and 69.8 (5.2) years at the Imaging Phase. Voxel-based analysis revealed that accelerated aortic stiffening in mid-to-late life was associated with poor white matter integrity, viz. lower fractional anisotropy in 4.2% of white matter and higher radial diffusivity in 6.7% of white matter, including the corpus callosum, corona radiata, superior longitudinal fasciculus and corticospinal tracts. Accelerated aortic stiffening was also related to lower cerebral perfusion in 1.1% of grey matter including the parietal, frontal, and occipital cortices. No associations with grey matter volume or white matter lesions were observed. Further, higher baseline aortic stiffness was associated with poor semantic fluency (B=-0.48, 95%CI −0.77 to −0.19, pConclusionsFaster aortic stiffening in mid-to-late life is associated with poor brain white matter microstructural integrity and reduced cerebral perfusion, likely due to increased transmission of pulsatile energy to the delicate cerebral microvasculature. Strategies to prevent arterial stiffening prior to this point may be required to offer cognitive benefit in older age.

Study Objectives To examine the association between sleep duration trajectories over 28 years and measures of cognition, gray matter volume, and white matter microstructure. We hypothesize that consistently meeting sleep guidelines that recommend at least 7 hours of sleep per night will be associated with better cognition, greater gray matter volumes, higher fractional anisotropy, and lower radial diffusivity values. Methods We studied 613 participants (age 42.3 ± 5.03 years at baseline) who self-reported sleep duration at five time points between 1985 and 2013, and who had cognitive testing and magnetic resonance imaging administered at a single timepoint between 2012 and 2016. We applied latent class growth analysis to estimate membership into trajectory groups based on self-reported sleep duration over time. Analysis of gray matter volumes was carried out using FSL Voxel-Based-Morphometry and white matter microstructure using Tract Based Spatial Statistics. We assessed group differences in cognitive and MRI outcomes using nonparametric permutation testing. Results Latent class growth analysis identified four trajectory groups, with an average sleep duration of 5.4 ± 0.2 hours (5%, N = 29), 6.2 ± 0.3 hours (37%, N = 228), 7.0 ± 0.2 hours (45%, N = 278), and 7.9 ± 0.3 hours (13%, N = 78). No differences in cognition, gray matter, and white matter measures were detected between groups. Conclusions Our null findings suggest that current sleep guidelines that recommend at least 7 hours of sleep per night may not be supported in relation to an association between sleep patterns and cognitive function or brain structure.

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 theLifebrainconsortium (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 (Aβ) 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 Aβ 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 Aβ 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.

There is little doubt that the brain changes with time, and all research in psychiatry is predicated on holding age constant in comparing groups of patients or estimating the effect sizes of causal factors. Nevertheless, relatively little is known about the mechanisms that are responsible for translating time into ageing. This chapter tries, after an overview of the principal mechanisms involved in biological ageing, to summarize the age-related changes observable in brains in vivo and to demonstrate the types of investigations that may cast light on such mechanisms in the future. A useful heuristic device to order the multiple potential causes of ageing is the chronic stress–allostatic load model, widely employed in epidemiology, public health medicine, and health psychology. In vivo imaging provides a method to test the translation of intermediate stress markers, such as vascular risk, metabolic syndrome, or allostatic load, into predictors of age-related brain changes.

Cardiovascular health in midlife is an established risk factor for cognitive function later in life. Knowing mechanisms of this association may allow preventative steps to be taken to preserve brain health and cognitive performance in older age. In this study, we investigated the association of the Framingham stroke-risk score, a validated multifactorial predictor of 10-year risk of stroke, with brain measures and cognitive performance in stroke-free individuals. We used a large (N = 800) longitudinal cohort of community-dwelling adults of the Whitehall II imaging sub-study with no obvious structural brain abnormalities, who had Framingham stroke risk measured five times between 1991 and 2013 and MRI measures of structural integrity, and cognitive function performed between 2012 and 2016 [baseline mean age 47.9 (5.2) years, range 39.7–62.7 years; MRI mean age 69.81 (5.2) years, range 60.3–84.6 years; 80.6% men]. Unadjusted linear associations were assessed between the Framingham stroke-risk score in each wave and voxelwise grey matter density, fractional anisotropy and mean diffusivity at follow-up. These analyses were repeated including socio-demographic confounders as well as stroke risk in previous waves to examine the effect of residual risk acquired between waves. Finally, we used structural equation modelling to assess whether stroke risk negatively affects cognitive performance via specific brain measures. Higher unadjusted stroke risk measured at each of the five waves over 20 years prior to the MRI scan was associated with lower voxelwise grey and white matter measures. After adjusting for socio-demographic variables, higher stroke risk from 1991 to 2009 was associated with lower grey matter volume in the medial temporal lobe. Higher stroke risk from 1997 to 2013 was associated with lower fractional anisotropy along the corpus callosum. In addition, higher stroke risk from 2012 to 2013, sequentially adjusted for risk measured in 1991–94, 1997–98 and 2002–04 (i.e. ‘residual risks’ acquired from the time of these examinations onwards), was associated with widespread lower fractional anisotropy, and lower grey matter volume in sub-neocortical structures. Structural equation modelling suggested that such reductions in brain integrity were associated with cognitive impairment. These findings highlight the importance of considering cerebrovascular health in midlife as important for brain integrity and cognitive function later in life (ClinicalTrials.gov Identifier: NCT03335696)., Higher stroke-risk score over 20 years before the magnetic resonance imaging scan was associated with whole-brain grey matter volume and white matter microstructure in community-dwelling older adults. Structural equation models suggest that brain integrity was associated with cognitive function. Results highlight the need for targeting modifiable risk factors in midlife., Graphical Abstract Graphical Abstract

Highlights • Alcohol consumption has previously been associated with hippocampal volume. • 1H-MRS was used to quantify hippocampal metabolite concentrations in older adults. • Alcohol consumption was negetively correlated with hippocampal creatine levels. • Future MRS studies may wish to account for drinking variables in their analyses. • Creatine may not be an apt denominator for metabolite ratios in age-related studies., Besides its well established susceptibility to ageing, the hippocampus has also been shown to be affected by alcohol consumption. Proton spectroscopy (1H-MRS) of the hippocampus, particularly at high-field 7T MRI, may further our understanding of these associations. Here, we aimed to examine how hippocampal metabolites varied with age and alcohol consumption. Hippocampal metabolite spectra were acquired in 37 older adults using 7T 1H-MRS, from which we determined the absolute concentration of N-acetylaspartate (NAA), creatine, choline, myo-inositol, glutamate and glutamine. Thirty participants (mean age = 70.4 ± 4.7 years) also had self-reported data on weekly alcohol consumption. Total choline inversely correlated with age, although this did not survive multiple comparisons correction. Crucially, adults with a higher weekly alcohol consumption had significantly lower levels of creatine, suggesting a deficit in their hippocampal metabolism. These findings add to an increasing body of evidence linking alcohol to hippocampal function.

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.MethodsSelf-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.ResultsThe 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.ConclusionsWorse 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.

Key Points Question Is midlife cardiovascular risk associated with cerebral blood flow in older age, and does this association vary with age? Findings In this longitudinal cohort study of 116 older adults without dementia, higher cardiovascular risk scores during a 20-year period were significantly associated with lower cerebral blood flow to the medial temporal, parietal, and occipital cortices. The association varied during the life span such that cardiovascular risk in midlife but not in later life was significantly associated with cerebral hypoperfusion in older age. Meaning Because cerebral hypoperfusion is an early mechanism in Alzheimer disease and vascular dementia, these findings may inform the development of dementia prevention strategies aimed at managing cardiovascular health., This longitudinal cohort study examines the association of midlife cardiovascular risk factors with cerebral perfusion at older ages among adults without dementia., Importance Poor cardiovascular health is an established risk factor for dementia, but little is known about its association with brain physiology in older adults. Objective To examine the association of cardiovascular risk factors, measured repeatedly during a 20-year period, with cerebral perfusion at older ages. Design, Setting, and Participants In this longitudinal cohort study, individuals were selected from the Whitehall II Imaging Substudy. Participants were included if they had no clinical diagnosis of dementia, had no gross brain structural abnormalities on magnetic resonance imaging scans, and had received pseudocontinuous arterial spin labeling magnetic resonance imaging. Cardiovascular risk was measured at 5-year intervals across 5 phases from September 1991 to October 2013. Arterial spin labeling scans were acquired between April 2014 and December 2014. Data analysis was performed from June 2016 to September 2018. Exposures Framingham Risk Score (FRS) for cardiovascular disease, comprising age, sex, high-density lipoprotein cholesterol level, total cholesterol level, systolic blood pressure, use of antihypertensive medications, cigarette smoking, and diabetes, was assessed at 5 visits. Main Outcomes and Measures Cerebral blood flow (CBF; in milliliters per 100 g of tissue per minute) was quantified with pseudocontinuous arterial spin labeling magnetic resonance imaging. Results Of 116 adult participants, 99 (85.3%) were men. At the first examination, mean (SD) age was 47.1 (5.0) years; at the last examination, mean (SD) age was 67.4 (4.9) years. Mean (SD) age at MRI scan was 69.3 (5.0) years. Log-FRS increased with time (B = 0.058; 95% CI, 0.044 to 0.072; P