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Most people with mild dementia can continue to drive, but dementia is progressive and many patients and clinicians will be faced with questions about driving safety in the course of their illness. Determining when this happens is a complex decision, with risks of personal and public safety needing to be weighed against individual patient benefits of driving in terms of autonomy, independence and well-being. Decisions need to make reference to cognitive abilities, as well as other factors including physical comorbidity, vision, mobility, insight and history of driving errors and accidents. Deciding to stop driving, or being required to stop driving is often difficult for patients to accept and can be a particularly problematic consequence of a dementia diagnosis. Legal frameworks help in decision-making but may not provide sufficient detail to advise individual patients. We review the current guidelines and evidence relating to driving and dementia to help clinicians answer questions about driving safety and to consider the full range of assessment tools available.
\n \n\n \n \nMild cognitive impairment (MCI) is a term used to describe cognitive impairment in one or more cognitive domains that is greater than any expected age-related changes, but not of the magnitude to warrant a diagnosis of dementia. This review considers how early cognitive decline is diagnosed, focusing on the use of neuropsychological tests and neuroimaging, as well as the differential diagnosis. Potential treatments, including secondary prevention, post-diagnostic support and self-help are discussed. Finally, medico-legal matters such as driving, lasting power of attorney and employment are outlined.
\n \n\n \n \nBACKGROUND: Cognitive impairment and gait disorders in people over the age of 65 represent major public health issues because of their high frequency, their link to poor outcomes and high costs. Research has demonstrated that these two geriatric syndromes are closely related. METHODS AND RESULTS: We aim to review the evidence supporting the relationship between gait and cognitive impairment, particularly focusing on epidemiological and neuropsychological studies in patients with Mild cognitive impairment, Alzheimer's disease and Vascular dementia. The review demonstrates that gait and cognition are closely related, but our knowledge of their interrelationship is limited. Emerging evidence shows that gait analysis has the potential to contribute to diagnosis and prognosis of cognitive impairment. CONCLUSIONS: An integrated approach for evaluating these major geriatric syndromes, based on their close relationship, will not only increase our understanding of cognitive-motor interactions, but most importantly may be used to aid early diagnosis, prognosis and the development of new interventions.
\n \n\n \n \nBACKGROUND: Alzheimer's Disease (AD) and Vascular Dementia (VaD) are the most common causes of dementia in older people. Both diseases appear to have similar clinical symptoms, such as deficits in attention and executive function, but specific cognitive domains are affected. Current cohort studies have shown a close relationship between \u03b1\u03b2 deposits and age-related macular degeneration (Johnson et al., 2002; Ratnayaka et al., 2015). Additionally, a close link between the thinning of the retinal nerve fiber (RNFL) and AD patients has been described, while it has been proposed that AD patients suffer from a non-specific type of color blindness (Pache et al., 2003). METHODS: Our study included 103 individuals divided into three groups: A healthy control group (n = 35), AD (n = 32) according to DSM-IV-TR, NINCDS-ADRDA criteria, and VaD (n = 36) based on \u039d\u0399\u039dDS-AIREN, as well as Magnetic Resonance Imaging (MRI) results. The severity of patient's cognitive impairment, was measured with the Mini-Mental State Examination (MMSE) and was classified according to the Reisberg global deterioration scale (GDS). Visual perception was examined using the Ishihara plates: \"Ishihara Color Vision Test - 38 Plate.\" RESULTS: The three groups were not statistically different for demographic data (age, gender, and education). The Ishihara color blindness test has a sensitivity of 80.6% and a specificity of 87.5% to discriminate AD and VaD patients when an optimal (32.5) cut-off value of performance is used. CONCLUSIONS: Ishihara Color Vision Test - 38 Plate is a promising potential method as an easy and not time-consuming screening test for the differential diagnosis of dementia between AD and VaD.
\n \n\n \n \nIMPORTANCE: Neuropsychiatric symptoms, depressive symptoms in particular, are common in patients with dementia but whether depressive symptoms in adulthood increases the risk for dementia remains the subject of debate. OBJECTIVE: To characterize the trajectory of depressive symptoms over 28 years prior to dementia diagnosis to determine whether depressive symptoms carry risk for dementia. DESIGN, SETTING, AND PARTICIPANTS: Up to 10\u202f308 persons, aged 35 to 55 years, were recruited to the Whitehall II cohort study in 1985, with the end of follow-up in 2015. Data analysis for this study in a UK general community was conducted from October to December 2016. EXPOSURES: Depressive symptoms assessed on 9 occasions between 1985 and 2012 using the General Health Questionnaire. MAIN OUTCOMES AND MEASURES: Incidence of dementia (n\u2009=\u2009322) between 1985 and 2015. RESULTS: Of the 10\u202f189 persons included in the study, 6838 were men (67%) and 3351 were women (33%). Those reporting depressive symptoms in 1985 (mean follow-up, 27 years) did not have significantly increased risk for dementia (hazard ratio [HR], 1.21; 95% CI, 0.95-1.54) in Cox regression adjusted for sociodemographic covariates, health behaviors, and chronic conditions. However, those with depressive symptoms in 2003 (mean follow-up, 11 years) had an increased risk (HR, 1.72; 95% CI, 1.21-2.44). Those with chronic/recurring depressive symptoms (\u22652 of 3 occasions) in the early study phase (mean follow-up, 22 years) did not have excess risk (HR, 1.02; 95% CI, 0.72-1.44) but those with chronic/recurring symptoms in the late phase (mean follow-up, 11 years) did have higher risk for dementia (HR, 1.67; 95% CI, 1.11-2.49). Analysis of retrospective depressive trajectories over 28 years, using mixed models and a backward time scale, shows that in those with dementia, differences in depressive symptoms compared with those without dementia became apparent 11 years (difference, 0.61; 95% CI, 0.09-1.13; P\u2009=\u2009.02) before dementia diagnosis and became more than 9 times larger at the year of diagnosis (difference, 5.81; 95% CI, 4.81-6.81; P\u2009
\n \n\n \n \nWhite matter hyperintensities (WMH) are frequently divided into periventricular (PWMH) and deep (DWMH), and the two classes have been associated with different cognitive, microstructural, and clinical correlates. However, although this distinction is widely used in visual ratings scales, how to best anatomically define the two classes is still disputed. In fact, the methods used to define PWMH and DWMH vary significantly between studies, making results difficult to compare. The purpose of this study was twofold: first, to compare four current criteria used to define PWMH and DWMH in a cohort of healthy older adults (mean age: 69.58 \u00b1 5.33 years) by quantifying possible differences in terms of estimated volumes; second, to explore associations between the two WMH sub-classes with cognition, tissue microstructure and cardiovascular risk factors, analysing the impact of different criteria on the specific associations. Our results suggest that the classification criterion used for the definition of PWMH and DWMH should not be considered a major obstacle for the comparison of different studies. We observed that higher PWMH load is associated with reduced cognitive function, higher mean arterial pressure and age. Higher DWMH load is associated with higher body mass index. PWMH have lower fractional anisotropy than DWMH, which also have more heterogeneous microstructure. These findings support the hypothesis that PWMH and DWMH are different entities and that their distinction can provide useful information about healthy and pathological aging processes.
\n \n\n \n \nProton magnetic resonance spectroscopy (1H-MRS) has provided valuable information about the neurochemical profile of Alzheimer's disease (AD). However, its clinical utility has been limited in part by the lack of consistent information on how metabolite concentrations vary in the normal aging brain and in carriers of apolipoprotein E (APOE) \u03b54, an established risk gene for AD. We quantified metabolites within an 8cm3 voxel within the posterior cingulate cortex (PCC)/precuneus in 30 younger (20-40 years) and 151 cognitively healthy older individuals (60-85 years). All 1H-MRS scans were performed at 3T using the short-echo SPECIAL sequence and analyzed with LCModel. The effect of APOE was assessed in a sub-set of 130 volunteers. Older participants had significantly higher myo-inositol and creatine, and significantly lower glutathione and glutamate than younger participants. There was no significant effect of APOE or an interaction between APOE and age on the metabolite profile. Our data suggest that creatine, a commonly used reference metabolite in 1H-MRS studies, does not remain stable across adulthood within this region and therefore may not be a suitable reference in studies involving a broad age-range. Increases in creatine and myo-inositol may reflect age-related glial proliferation; decreases in glutamate and glutathione suggest a decline in synaptic and antioxidant efficiency. Our findings inform longitudinal clinical studies by characterizing age-related metabolite changes in a non-clinical sample.
\n \n\n \n \nObjectives\u00a0To investigate whether moderate alcohol consumption has a favourable or adverse association or no association with brain structure and function.Design\u00a0Observational cohort study with weekly alcohol intake and cognitive performance measured repeatedly over 30 years (1985-2015). Multimodal magnetic resonance imaging (MRI) was performed at study endpoint (2012-15).Setting\u00a0Community dwelling adults enrolled in the Whitehall II cohort based in the UK (the Whitehall II imaging substudy).Participants\u00a0550 men and women with mean age 43.0 (SD 5.4) at study baseline, none were \"alcohol dependent\" according to the CAGE screening questionnaire, and all safe to undergo MRI of the brain at follow-up. Twenty three were excluded because of incomplete or poor quality imaging data or gross structural abnormality (such as a brain cyst) or incomplete alcohol use, sociodemographic, health, or cognitive data.Main outcome measures\u00a0Structural brain measures included hippocampal atrophy, grey matter density, and white matter microstructure. Functional measures included cognitive decline over the study and cross sectional cognitive performance at the time of scanning.Results\u00a0Higher alcohol consumption over the 30 year follow-up was associated with increased odds of hippocampal atrophy in a dose dependent fashion. While those consuming over 30 units a week were at the highest risk compared with abstainers (odds ratio 5.8, 95% confidence interval 1.8 to 18.6; P\u22640.001), even those drinking moderately (14-21 units/week) had three times the odds of right sided hippocampal atrophy (3.4, 1.4 to 8.1; P=0.007). There was no protective effect of light drinking (1-<7 units/week) over abstinence. Higher alcohol use was also associated with differences in corpus callosum microstructure and faster decline in lexical fluency. No association was found with cross sectional cognitive performance or longitudinal changes in semantic fluency or word recall.Conclusions\u00a0Alcohol consumption, even at moderate levels, is associated with adverse brain outcomes including hippocampal atrophy. These results support the recent reduction in alcohol guidance in the UK and question the current limits recommended in the US.
\n \n\n \n \nEpisodic and spatial memory are commonly impaired in ageing and Alzheimer's disease. Volumetric and task-based functional magnetic resonance imaging (fMRI) studies suggest a preferential involvement of the medial temporal lobe (MTL), particularly the hippocampus, in episodic and spatial memory processing. The present study examined how these two memory types were related in terms of their associated resting-state functional architecture. 3T multiband resting state fMRI scans from 497 participants (60-82 years old) of the cross-sectional Whitehall II Imaging sub-study were analysed using an unbiased, data-driven network-modelling technique (FSLNets). Factor analysis was performed on the cognitive battery; the Hopkins Verbal Learning test and Rey-Osterreith Complex Figure\u00a0test factors were used to assess verbal and visuospatial memory respectively. We present a map of the macroscopic functional connectome for the Whitehall II Imaging sub-study, comprising 58 functionally distinct nodes clustered into five major resting-state networks. Within this map we identified distinct functional connections associated with verbal and visuospatial memory. Functional anticorrelation between the hippocampal formation and the frontal pole was significantly associated with better verbal memory in an age-dependent manner. In contrast, hippocampus-motor and parietal-motor functional connections were associated with visuospatial memory independently of age. These relationships were not driven by grey matter volume and were unique to the respective memory domain. Our findings provide new insights into current models of brain-behaviour interactions, and suggest that while both episodic and visuospatial memory engage MTL nodes of the default mode network, the two memory domains differ in terms of the associated functional connections between the MTL and other resting-state brain networks.
\n \n\n \n \nBoth 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\u2009\u00b1\u20095.1 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. \u00a9 2017 Wiley Periodicals, Inc.
\n \n\n \n \nDepression and dementia are both common conditions in older people, and they frequently occur together. Late life depression affects about 3.0-4.5% of adults aged 65 and older. Depression occurs in up to 20% of patients with Alzheimer\u2019s disease and up to 45% of patients with vascular dementia. Rather than a risk factor, depression with onset in later life is more likely to be either prodromal to dementia or a condition that unmasks pre-existing cognitive impairment by compromising cognitive reserve. Depression can be a psychological response to receiving a diagnosis of dementia. The distinction between depression and early dementia may be particularly difficult. Detailed histories obtained from patients and their relatives as well as longitudinal follow-up are important. Cognitive testing can be very helpful. It is preferable to use a neuropsychological test that is sensitive to subtle cognitive changes and assesses all cognitive domains, such as the Montreal Cognitive Assessment. Older people with depression are at raised risk of dementia and this risk is increased if they have had symptoms for a long time, if their symptoms are severe, where there are multiple (vascular) comorbidities, and where there are structural brain changes including hippocampal atrophy and white matter abnormalities.
\n \n\n \n \nBrain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
\n \n\n \n \nLoneliness is linked to increased risk for Alzheimer's disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely vs. not lonely individuals (ROC AUC\u202f=\u202f0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls - as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
\n \n\n \n \nSTUDY 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 \u00b1 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 \u00b1 0.2 hours (5%, N = 29), 6.2 \u00b1 0.3 hours (37%, N = 228), 7.0 \u00b1 0.2 hours (45%, N = 278), and 7.9 \u00b1 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.
\n \n\n \n \nOBJECTIVES: Poor 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. METHODS: Self-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 for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank. RESULTS: No cross-sectional sleep-hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses. CONCLUSIONS: Worse self-reported sleep is associated with higher rates of hippocampal volume 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.
\n \n\n \n \nBACKGROUND: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. OBJECTIVE: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. METHODS: We divided the UK Biobank cohort into a training (n=176\u2009611, 80%) and test sample (n=44\u2009151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. FINDINGS: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95%\u2009CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95%\u2009CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). CLINICAL IMPLICATIONS: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.
\n \n\n \n \nWe 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\u2009=\u20095.18, 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 Asessment [MoCA] scores of < 26 vs. \u2265 26). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (\u03b2\u2009=\u20093.36, 95% CI [0.42-6.30]), and faster cognitive decline in letter fluency (\u03b2\u2009=\u2009-0.07, 95% CI [-0.13--0.01]), and verbal reasoning (\u03b2\u2009=\u2009-0.05, 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 p\u2009
\n \n\n \n \nQuantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.
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