Search

BackgroundLoneliness is most prevalent during adolescence and late life and has been associated with mental health disorders as well as with cognitive decline during aging. Associations between longitudinal measures of loneliness and verbal episodic memory and brain structure should thus be investigated.MethodsWe sought to determine associations between loneliness and verbal episodic memory as well as loneliness and hippocampal volume trajectories across three longitudinal cohorts within the Lifebrain Consortium, including children, adolescents (N = 69, age range 10–15 at baseline examination) and older adults (N = 1468 over 60). We also explored putative loneliness correlates of cortical thinning across the entire cortical mantle.ResultsLoneliness was associated with worsening of verbal episodic memory in one cohort of older adults. Specifically, reporting medium to high levels of loneliness over time was related to significantly increased memory loss at follow-up examinations. The significance of the loneliness-memory change association was lost when eight participants were excluded after having developed dementia in any of the subsequent follow-up assessments. No significant structural brain correlates of loneliness were found, neither hippocampal volume change nor cortical thinning.ConclusionIn the present longitudinal European multicenter study, the association between loneliness and episodic memory was mainly driven by individuals exhibiting progressive cognitive decline, which reinforces previous findings associating loneliness with cognitive impairment and dementia.

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.

Contains fulltext : 252618.pdf (Publisher’s version ) (Open Access) The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 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 components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure. 16 p.

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.

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.

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

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.

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

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.

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.

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

While neuroendocrinology has afforded us a “window into brain metabolism,” which has been exploited extensively in stress research, modern in vivo imaging, in particular positron emission tomography, is able to detect very small (nano- to picomolar) signals in brain metabolism and pharmacology, and can localize such signals anatomically within the mm-range. In this chapter we provide an overview of the range of in vivo imaging results associated with stress research. We will put an emphasis on posttraumatic stress disorder as the most important acute stress-related syndrome in psychiatry. There is, of course, an overlap with chronic and the sequelae of everyday stress, which will be duly covered. The picture is made complex by the brain being an executive organ of the stress response, but also the target of the potential damaging effects of stress, reinforcing the “feed-back paradigm” that is very familiar from psycho-neuro-endocrinology.

Both sleep disturbances and decline in white matter microstructure are commonly observed in ageing populations, as well as in age‐related psychiatric and neurological illnesses. A relationship between sleep and white matter microstructure may underlie such relationships, but few imaging studies have directly examined this hypothesis. In a study of 448 community‐dwelling members of the Whitehall II Imaging Sub‐Study aged between 60 and 82 years (90 female, mean age 69.2 ± 5.1 years), we used the magnetic resonance imaging technique diffusion tensor imaging to examine the relationship between self‐reported sleep quality and white matter microstructure. Poor sleep quality at the time of the diffusion tensor imaging scan was associated with reduced global fractional anisotropy and increased global axial diffusivity and radial diffusivity values, with small effect sizes. Voxel‐wise analysis showed that widespread frontal‐subcortical tracts, encompassing regions previously reported as altered in insomnia, were affected. Radial diffusivity findings remained significant after additional correction for demographics, general cognition, health, and lifestyle measures. No significant differences in general cognitive function, executive function, memory, or processing speed were detected between good and poor sleep quality groups. The number of times participants reported poor sleep quality over five time‐points spanning a 16‐year period was not associated with white matter measures. In conclusion, these data demonstrate that current sleep quality is linked to white matter microstructure. Small effect sizes may limit the extent to which poor sleep is a promising modifiable factor that may maintain, or even improve, white matter microstructure in ageing. Hum Brain Mapp 38:5465–5473, 2017. © 2017 Wiley Periodicals, Inc.

Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.

Background Late-life sub-threshold depressive symptoms (i.e. depressive symptoms that do not meet the criteria for a diagnosis of major depressive disorder) are associated with impaired physical health and function, and increased risk of major depressive disorder. Magnetic resonance imaging (MRI) studies examining late-life major depressive disorder find structural brain changes in grey and white matter. However, the extent to which late-life sub-threshold depression is associated with similar hallmarks is not well established. Methods Participants with no history of major depressive disorder were selected from the Whitehall Imaging Sub-Study (n=358, mean age 69±5 years, 17% female). Depressive symptoms were measured using the Centre for Epidemiological Studies Depression Scale (CES-D) at three previous Whitehall II Study phases (2003–04, 2007–09 and 2012–13) and at the time of the MRI scan (2012–14). The relationships between current and cumulative depressive symptoms and MRI brain measures were explored using Voxel-Based Morphometry (VBM) for grey matter and Tract Based Spatial Statistics (TBSS) for white matter. Results Current sub-threshold depressive symptoms were associated with significant reductions in fractional anisotropy and increases in axial and radial diffusivity. There were no significant relationships between current depressive symptoms and grey matter measures, or cumulative depressive symptoms and MRI measures. Limitations The prevalence (10%) of sub-threshold depressive symptoms means that analyses may be underpowered to detect subtle differences in brain structure. Conclusions Current sub-threshold depressive symptoms are associated with changes in white matter microstructure, indicating that even mild depressive symptoms are associated with similar MRI hallmarks to those in major depressive disorder., Highlights • This study investigated sub-threshold depressive symptoms in a community sample. • Depressive symptoms were measured 4 times over 11 years. • Brain MRI variables examined grey matter volume and white matter microstructure. • Current depressive symptoms were associated with reduced white matter integrity. • Sub-threshold symptoms show similar MRI hallmarks to major depressive disorder.

The conceptual link between aging and psychological stress is the construct “allostatic load”—that is, the notion that a repeated and cumulative disturbance of homeostasis may lead to certain syndromes that are triggered by a dysregulation of the chronic stress-response. We summarize the markers of this hypothetical process at primary, secondary, and tertiary stages, with particular attention on brain mechanisms that are in this pathway or are even instrumental to such dysregulation and its pathological consequences. As cumulative stress and allostasis are highly correlated with chronological age, we present an example for the statistical isolation of such mechanisms from the generic effect of age.

Depression was previously thought of as a 'functional' illness, without demonstrable pathology, but the advent of sophisticated neuroimaging techniques has changed this perspective. This article reviews the latest structural and functional brain imaging studies, describing the neurobiological changes which underlie this common condition. © 2014 MA Healthcare Ltd.