As the United States' (US) elderly population becomes increasingly diverse, it is imperative that research studies address cognitive health in diverse populations of older Americans. White Matter Hyperintensities (WMH) are useful imaging findings that can be studied in elderly individuals and have been linked to an increased risk of neurological conditions, such as stroke, cognitive impairment, and dementia. We performed a systematic review of literature using PubMed sources to compile all the studies that investigated the prevalence of ethnic and racial differences of WMH burden amongst diverse groups in the US. We identified 23 unique articles that utilized 16 distinct cohorts of which 94 % were prospective, longitudinal studies that included community-based and family-based populations. The overall results were heterogenous in all aspects of data collection and analysis, limiting our ability to run meta-analyses and draw definitive conclusions. General observations suggest increased vascular risk on African American populations, contributing to greater WMH burden in that population. Overall, the findings of this study indicate a need for a standardized approach to investigating WMH in efforts to measure its clinical impact on diverse populations.

Objective

With the long-term goal of improving community health by screening for dementia, we tested the utility of integrating the Six-Item Screener (SIS) into our emergency department neurology consultations.

Methods

In this cross-sectional observational study, we measured SIS performance within 24 hours of hospital arrival in 100 consecutive English-speaking patients aged ≥45 years. Performance was compared to patient age, previously charted cognitive impairment, and proxies for in-hospital complexity: whether or not a patient was admitted to the hospital and the number of medical studies ordered.

Results

Those with poor SIS performance were older (p = 0.02) and more likely to have previously charted cognitive impairment (p < 0.01; sensitivity 86%, specificity 77%). Poor performers were more likely to be admitted to the hospital (p = 0.04; odds ratio 3.6) and were subjected to more tests once admitted (p < 0.01), relationships that persisted after accounting for age and history of cognitive impairment.

Conclusions

Poor performance on the SIS was associated with previously charted cognitive impairment, justifying future study of its ability to detect unrecognized dementia cases. Until then, its ability to inexpensively anticipate medically complex hospital admissions motivates broader emergency department use of the SIS.

OBJECTIVE:With the long-term goal of improving community health by screening for dementia, we tested the utility of integrating the Six-Item Screener (SIS) into our emergency department neurology consultations. METHODS:In this cross-sectional observational study, we measured SIS performance within 24 hours of hospital arrival in 100 consecutive English-speaking patients aged ≥45 years. Performance was compared to patient age, previously charted cognitive impairment, and proxies for in-hospital complexity: whether or not a patient was admitted to the hospital and the number of medical studies ordered. RESULTS:Those with poor SIS performance were older (p = 0.02) and more likely to have previously charted cognitive impairment (p < 0.01; sensitivity 86%, specificity 77%). Poor performers were more likely to be admitted to the hospital (p = 0.04; odds ratio 3.6) and were subjected to more tests once admitted (p < 0.01), relationships that persisted after accounting for age and history of cognitive impairment. CONCLUSIONS:Poor performance on the SIS was associated with previously charted cognitive impairment, justifying future study of its ability to detect unrecognized dementia cases. Until then, its ability to inexpensively anticipate medically complex hospital admissions motivates broader emergency department use of the SIS.

During the course of normal aging, biological changes occur in the brain that are associated with changes in cognitive ability. This review presents data from neuroimaging studies of primarily "normal" or healthy brain aging. As such, we focus on research in unimpaired or nondemented older adults, but also include findings from lifespan studies that include younger and middle aged individuals as well as from populations with prodromal or clinically symptomatic disease such as cerebrovascular or Alzheimer's disease. This review predominantly addresses structural MRI biomarkers, such as volumetric or thickness measures from anatomical images, and measures of white matter injury and integrity respectively from FLAIR or DTI, and includes complementary data from PET and cognitive or clinical testing as appropriate. The findings reveal highly consistent age-related differences in brain structure, particularly frontal lobe and medial temporal regions that are also accompanied by age-related differences in frontal and medial temporal lobe mediated cognitive abilities. Newer findings also suggest that degeneration of specific white matter tracts such as those passing through the genu and splenium of the corpus callosum may also be related to age-related differences in cognitive performance. Interpretation of these findings, however, must be tempered by the fact that comorbid diseases such as cerebrovascular and Alzheimer's disease also increase in prevalence with advancing age. As such, this review discusses challenges related to interpretation of current theories of cognitive aging in light of the common occurrence of these later-life diseases. Understanding the differences between "Normal" and "Healthy" brain aging and identifying potential modifiable risk factors for brain aging is critical to inform potential treatments to stall or reverse the effects of brain aging and possibly extend cognitive health for our aging society.

Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.

Longitudinal clinical-pathological studies have increasingly recognized the importance of mixed pathologies (the coexistence of one or more neurodegenerative and cerebrovascular disease pathologies) as important factors in the development of Alzheimer's disease (AD) and other forms of dementia. Older persons with AD pathology, often have concomitant cerebrovascular disease pathologies (macroinfarcts, microinfarcts, atherosclerosis, arteriolosclerosis, cerebral amyloid angiopathy) as well as other concomitant neurodegenerative disease pathologies (Lewy bodies, TDP-43, hippocampal sclerosis). These additional pathologies lower the threshold for clinical diagnosis of AD. Many of these findings from pathologic studies, especially for CVD, have been confirmed using sophisticated neuroimaging technologies. In vivo biomarker studies are necessary to provide an understanding of specific pathologic contributions and time course relationships along the spectrum of accumulating pathologies. In this review, we provide a clinical-pathological perspective on the role of multiple brain pathologies in dementia followed by a review of the available clinical and biomarker data on some of the mixed pathologies.

This paper presents a method for detection of cerebral white matter hyperintensities (WMH) based on run-time PD-, T1-, and T2-weighted structural magnetic resonance (MR) images of the brain along with labeled training examples. Unlike most prior approaches, the method is able to reliably detect WMHs in elderly brains in the absence of fluid-attenuated (FLAIR) images. Its success is due to the learning of probabilistic models of WMH spatial distribution and neighborhood dependencies from ground-truth examples of FLAIR-based WMH detections. These models are combined with a probabilistic model of the PD, T1, and T2 intensities of WMHs in a Markov Random Field (MRF) framework that provides the machinery for inferring the positions of WMHs in novel test images. The method is shown to accurately detect WMHs in a set of 114 elderly subjects from an academic dementia clinic. Experiments show that standard off-the-shelf MRF training and inference methods provide robust results, and that increasing the complexity of neighborhood dependency models does not necessarily help performance. The method is also shown to perform well when training and test data are drawn from distinct scanners and subject pools.

This paper presents a method for estimating uncertainty in MRI-based brain region delineations provided by fully-automated segmentation methods. In large data sets, the uncertainty estimates could be used to detect fully-automated method failures, identify low-quality imaging data, or endow downstream statistical analyses with per-subject uncertainty in derived morphometric measures. Region segmentation is formulated in a statistical inference framework; the probability that a given region-delineating surface accounts for observed image data is quantified by a distribution that takes into account a prior model of plausible region shape and a model of how the region appears in images. Region segmentation consists of finding the maximum \emph{a posteriori} (MAP) parameters of the delineating surface under this distribution, and segmentation uncertainty is quantified in terms of how sharply peaked the distribution is in the vicinity of the maximum. Uncertainty measures are estimated through Markov Chain Monte Carlo (MCMC) sampling of the distribution in the vicinity of the MAP estimate. Experiments on real and synthetic data show that the uncertainty measures automatically detect when the delineating surface of the entire brain is unclear due to poor image quality or artifact; the experiments cover multiple appearance models to demonstrate the generality of the method. The approach is also general enough to accommodate a wide range of shape models and brain regions.