629 research outputs found

    Editorial: Alzheimer's Disease and the Fornix

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    This e-book focuses primarily on the role of the fornix as a functional, prognostic, and diagnostic marker of Alzheimer’s disease (AD), and the application of such a marker in clinical practice. Researchers have long been focused on the cortical pathology of AD, since the most important pathologic features are the senile plaques found in the cortex, and the neurofibrillary tangles and neuronal loss that start from the entorhinal cortex and the hippocampus. In addition to gray matter structures, histopathological studies indicate that the white matter is also altered in AD. The fornix is a white matter bundle that constitutes a core element of the limbic circuits, and is one of the most important anatomical structures related to memory. The fornices originate from the bilateral hippocampi, merge at the midline of the brain, again divide into the left and right side, and then into the precommissural and the postcommissural fibers, and terminate at the septal nuclei, nucleus accumbens (precommissural fornix), and hypothalamus (postcommissural fornix). These functional and anatomical features of the fornix have naturally captured researchers’ attention as possible diagnostic and prognostic markers of AD. Growing evidence indicates that the alterations seen in the fornix are potentially a good marker with which to predict future conversion from mild cognitive impairment to AD, and even from a cognitively normal state to AD. The degree of alteration is correlated with the degree of memory impairment, indicating the potential for the use of the fornix as a functional marker. Moreover, there have been attempts to stimulate the fornix to recover the cognitive function lost with AD. Our goal is to provide information about the status of current research and to facilitate further scientific and clinical advancement in this topic

    Systematic review of psychological approaches to the management of neuropsychiatric symptoms of dementia

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    Objective: The authors systematically reviewed the literature on psychological approaches to treating the neuropsychiatric symptoms of dementia.Method: Reports of studies that examined effects of any therapy derived from a psychological approach that satisfied prespecified criteria were reviewed. Data were extracted, the quality of each study was rated, and an overall rating was given to each study by using the Oxford Centre for Evidence-Based Medicine criteria.Results: A total of 1,632 studies were identified, and 162 satisfied the inclusion criteria for the review. Specific types of psychoeducation for caregivers about managing neuropsychiatric symptoms were effective treatments whose benefits lasted for months, but other caregiver interventions were not. Behavioral management techniques that are centered on individual patients' behavior or on caregiver behavior had similar benefits, as did cognitive stimulation. Music therapy and Snoezelen, and possibly sensory stimulation, were useful during the treatment session but had no longer-term effects; interventions that changed the visual environment looked promising, but more research is needed.Conclusions: Only behavior management therapies, specific types of caregiver and residential care staff education, and possibly cognitive stimulation appear to have lasting effectiveness for the management of dementia-associated neuropsychiatric symptoms. Lack of evidence regarding other therapies is not evidence of lack of efficacy. Conclusions are limited because of the paucity of high-quality research ( only nine level-1 studies were identified). More high-quality investigation is needed

    Modifiable Predictors of Dementia in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

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    Objective: Public health campaigns encouraging early help seeking have increased rates of mild cognitive impairment (MCI) diagnosis in Western countries, but we know little about how to treat or predict dementia outcomes in persons with the condition. Method: The authors searched electronic databases and references for longitudinal studies reporting potentially modifiable risk factors for incident dementia after MCI. Two authors independently evaluated study quality using a checklist. Meta-analyses were conducted of three or more studies. Results: There were 76 eligible articles. Diabetes and prediabetes increased risk of conversion from amnestic MCI to Alzheimer's dementia; risk in treated versus untreated diabetes was lower in one study. Diabetes was also associated with increased risk of conversion from any-type or nonamnestic MCI to all-cause dementia. Metabolic syndrome and prediabetes predicted all-cause dementia in people with amnestic and any-type MCI, respectively. Mediterranean diet decreased the risk of conversion to Alzheimer's dementia. The presence of neuropsychiatric symptoms or lower serum folate levels predicted conversion from any-type MCI to all-cause dementia, but less formal education did not. Depressive symptoms predicted conversion from any-type MCI to all-cause dementia in epidemiological but not clinical studies. Conclusions: Diabetes increased the risk of conversion to dementia. Other prognostic factors that are potentially manageable are prediabetes and the metabolic syndrome, neuropsychiatric symptoms, and low dietary folate. Dietary interventions and interventions to reduce neuropsychiatric symptoms, including depression, that increase risk of conversion to dementia may decrease new incidence of dementia

    Empirically Defining Trajectories of Late-Life Cognitive and Functional Decline

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    Background: Alzheimer’s disease (AD) is associated with variable cognitive and functional decline, and it is difficult to predict who will develop the disease and how they will progress. Objective: This exploratory study aimed to define latent classes from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database who had similar growth patterns of both cognitive and functional change using Growth Mixture Modeling (GMM), identify characteristics associated with those trajectories, and develop a decision tree using clinical predictors to determine which trajectory, as determined by GMM, individuals will most likely follow. Methods: We used ADNI early mild cognitive impairment (EMCI), late MCI (LMCI), AD dementia, and healthy control (HC) participants with known amyloid-β status and follow-up assessments on the Alzheimer’s Disease Assessment Scale - Cognitive Subscale or the Functional Activities Questionnaire (FAQ) up to 24 months postbaseline. GMM defined trajectories. Classification and Regression Tree (CART) used certain baseline variables to predict likely trajectory path. Results: GMM identified three trajectory classes (C): C1 (n = 162, 13.6%) highest baseline impairment and steepest pattern of cognitive/functional decline; C3 (n = 819, 68.7%) lowest baseline impairment and minimal change on both; C2 (n = 211, 17.7%) intermediate pattern, worsening on both, but less steep than C1. C3 had fewer amyloid- or apolipoprotein-E ɛ4 (APOE4) positive and more healthy controls (HC) or EMCI cases. CART analysis identified two decision nodes using the FAQ to predict likely class with 82.3% estimated accuracy. Conclusions: Cognitive/functional change followed three trajectories with greater baseline impairment and amyloid and APOE4 positivity associated with greater progression. FAQ may predict trajectory class

    Coordinated analysis of age, sex, and education effects on change in MMSE scores

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    Objectives. We describe and compare the expected performance trajectories of older adults on the Mini-Mental Status Examination (MMSE) across six independent studies from four countries in the context of a collaborative network of longitudinal studies of aging. A coordinated analysis approach is used to compare patterns of change conditional on sample composition differences related to age, sex, and education. Such coordination accelerates evaluation of particular hypotheses. In particular, we focus on the effect of educational attainment on cognitive decline.Method. Regular and Tobit mixed models were fit to MMSE scores from each study separately. The effects of age, sex, and education were examined based on more than one centering point.Results. Findings were relatively consistent across studies. On average, MMSE scores were lower for older individuals and declined over time. Education predicted MMSE score, but, with two exceptions, was not associated with decline in MMSE over time.Conclusion. A straightforward association between educational attainment and rate of cognitive decline was not supported. Thoughtful consideration is needed when synthesizing evidence across studies, as methodologies adopted and sample characteristics, such as educational attainment, invariably differ. © 2012 The Author

    Association of Depressive Symptoms With Postoperative Delirium and CSF Biomarkers for Alzheimer's Disease Among Hip Fracture Patients

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    OBJECTIVES: While there is growing evidence of an association between depressive symptoms and postoperative delirium, the underlying pathophysiological mechanisms remain unknown. The goal of this study was to explore the association between depression and postoperative delirium in hip fracture patients, and to examine Alzheimer's disease (AD) pathology as a potential underlying mechanism linking depressive symptoms and delirium. METHODS: Patients 65 years old or older (N = 199) who were undergoing hip fracture repair and enrolled in the study "A Strategy to Reduce the Incidence of Postoperative Delirium in Elderly Patients" completed the 15-item Geriatric Depression Scale (GDS-15) preoperatively. Cerebrospinal fluid (CSF) was obtained during spinal anesthesia and assayed for amyloid-beta (Aβ) 40, 42, total tau (t-tau), and phosphorylated tau (p-tau)181. RESULTS: For every one point increase in GDS-15, there was a 13% increase in odds of postoperative delirium, adjusted for baseline cognition (MMSE), age, sex, race, education and CSF AD biomarkers (OR = 1.13, 95%CI = 1.02-1.25). Both CSF Aꞵ42/t-tau (β = -1.52, 95%CI = -2.1 to -0.05) and Aꞵ42/p-tau181 (β = -0.29, 95%CI = -0.48 to -0.09) were inversely associated with higher GDS-15 scores, where lower ratios indicate greater AD pathology. In an analysis to identify the strongest predictors of delirium out of 18 variables, GDS-15 had the highest classification accuracy for postoperative delirium and was a stronger predictor of delirium than both cognition and AD biomarkers. CONCLUSIONS: In older adults undergoing hip fracture repair, depressive symptoms were associated with underlying AD pathology and postoperative delirium. Mild baseline depressive symptoms were the strongest predictor of postoperative delirium, and may represent a dementia prodrome

    Automated Generation of Radiologic Descriptions on Brain Volume Changes From T1-Weighted MR Images: Initial Assessment of Feasibility

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    Purpose: To examine the feasibility and potential difficulties of automatically generating radiologic reports (RRs) to articulate the clinically important features of brain magnetic resonance (MR) images.Materials and Methods: We focused on examining brain atrophy by using magnetization-prepared rapid gradient-echo (MPRAGE) images. The technology was based on multi-atlas whole-brain segmentation that identified 283 structures, from which larger superstructures were created to represent the anatomic units most frequently used in RRs. Through two layers of data-reduction filters, based on anatomic and clinical knowledge, raw images (~10 MB) were converted to a few kilobytes of human-readable sentences. The tool was applied to images from 92 patients with memory problems, and the results were compared to RRs independently produced by three experienced radiologists. The mechanisms of disagreement were investigated to understand where machine–human interface succeeded or failed.Results: The automatically generated sentences had low sensitivity (mean: 24.5%) and precision (mean: 24.9%) values; these were significantly lower than the inter-rater sensitivity (mean: 32.7%) and precision (mean: 32.2%) of the radiologists. The causes of disagreement were divided into six error categories: mismatch of anatomic definitions (7.2 ± 9.3%), data-reduction errors (11.4 ± 3.9%), translator errors (3.1 ± 3.1%), difference in the spatial extent of used anatomic terms (8.3 ± 6.7%), segmentation quality (9.8 ± 2.0%), and threshold for sentence-triggering (60.2 ± 16.3%).Conclusion: These error mechanisms raise interesting questions about the potential of automated report generation and the quality of image reading by humans. The most significant discrepancy between the human and automatically generated RRs was caused by the sentence-triggering threshold (the degree of abnormality), which was fixed to z-score >2.0 for the automated generation, while the thresholds by radiologists varied among different anatomical structures
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