Guy G. Potter

Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study

Aim: To estimate the prevalence of Alzheimer’s disease (AD) and other dementias in the USA using a nationally representative sample. Methods: The Aging, Demographics, and Memory Study sample was composed of 856 individuals aged 71 years and older from the nationally representative Health and Retirement Study (HRS) who were evaluated for dementia using a comprehensive in-home assessment. An expert consensus panel used this information to assign a diagnosis of normal cognition, cognitive impairment but not demented, or dementia (and dementia subtype). Using sampling weights derived from the HRS, we estimated the national prevalence of dementia, AD and vascular dementia by age and gender. Results: The prevalence of dementia among individuals aged 71 and older was 13.9%, comprising about 3.4 million individuals in the USA in 2002. The corresponding values for AD were 9.7% and 2.4 million individuals. Dementia prevalence increased with age, from 5.0% of those aged 71–79 years to 37.4% of those aged 90 and older. Conclusions: Dementia prevalence estimates from this first nationally representative population-based study of dementia in the USA to include subjects from all regions of the country can provide essential information for effective planning for the impending healthcare needs of the large and increasing number of individuals at risk for dementia as our population ages.

Diffusion tensor imaging of cerebral white matter integrity in cognitive aging

In this article we review recent research on diffusion tensor imaging (DTI) of white matter (WM) integrity and the implications for age-related differences in cognition. Neurobiological mechanisms defined from DTI analyses suggest that a primary dimension of age-related decline in WM is a decline in the structural integrity of myelin, particularly in brain regions that myelinate later developmentally. Research integrating behavioral measures with DTI indicates that WM integrity supports the communication among cortical networks, particularly those involving executive function, perceptual speed, and memory (i.e., fluid cognition). In the absence of significant disease, age shares a substantial portion of the variance associated with the relation between WM integrity and fluid cognition. Current data are consistent with one model in which age-related decline in WM integrity contributes to a decreased efficiency of communication among networks for fluid cognitive abilities. Neurocognitive disorders for which older adults are at risk, such as depression, further modulate the relation between WM and cognition, in ways that are not as yet entirely clear. Developments in DTI technology are providing a new insight into both the neurobiological mechanisms of aging WM and the potential contribution of DTI to understanding functional measures of brain activity. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Identification of MCI individuals using structural and functional connectivity networks.

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimers disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.

Prevalence of depression among older Americans: the Aging, Demographics and Memory Study.

BACKGROUND Previous studies have attempted to provide estimates of depression prevalence in older adults. The Aging, Demographics and Memory Study (ADAMS) is a population-representative study that included a depression assessment, providing an opportunity to estimate the prevalence of depression in late life in the U.S.A. METHODS The ADAMS sample was drawn from the larger Health and Retirement Study. A total of 851 of 856 ADAMS participants aged 71 and older had available depression data. Depression was measured using the Composite International Diagnostic Interview - Short Form (CIDI-SF) and the informant depression section of the Neuropsychiatric Inventory (NPI). We estimated the national prevalence of depression, stratified by age, race, sex, and cognitive status. Logistic regression analyses were performed to examine the association of depression and previously reported risk factors for the condition. RESULTS When combining symptoms of major or minor depression with reported treatment for depression, we found an overall depression prevalence of 11.19%. Prevalence was similar for men (10.19%) and women (11.44%). Whites and Hispanics had nearly three times the prevalence of depression found in African-Americans. Dementia diagnosis and pain severity were associated with increased depression prevalence, while black race was associated with lower rates of depression. CONCLUSIONS The finding of similar prevalence estimates for depression in men and women was not consistent with prior research that has shown a female predominance. Given the population-representativeness of our sample, similar depression rates between the sexes in ADAMS may result from racial, ethnic and socioeconomic diversity.

Contribution of depression to cognitive impairment and dementia in older adults.

Background:The objective of this review is to provide information for clinicians regarding current research and opinions on the association of depression to conditions of cognitive impairment and dementia. We also intend to integrate this current research and thinking into strategies for the assessment and treatment of depression in the context of cognitive impairment. Review Summary:Depression is highly prevalent in mild cognitive impairment and most dementias. It may be a risk factor for the subsequent development of dementia and in some conditions may be a prodromal symptom. It is important to detect and effectively treat depression because the comorbidity of depression and cognitive impairment is associated with greater cognitive and functional decline and higher rates of institutionalization. Depression often can be differentiated from Alzheimer disease and other dementias based on characteristics of clinical history and presentation. Screening of depression and cognitive impairment will help characterize the presence and severity of these conditions, but limitations in screening approaches may necessitate comprehensive assessment in complex cases where differential diagnosis is important to treatment planning. Conclusion:Although depression and cognitive impairment are important issues in the treatment of older adults, there are particular risks when they occur together. Appropriate assessment and screening can help guide the clinician to appropriate and timely interventions. Pharmacologic and nonpharmacologic treatment approaches are both efficacious in reducing depression in cognitive impairment and dementia.

Enriched white matter connectivity networks for accurate identification of MCI patients.

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimers disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities(λ(1), λ(2), and λ(3)), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using an SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.

Prevalence of Neuropsychiatric Symptoms and Their Association with Functional Limitations in Older Adults in the United States: The Aging, Demographics, and Memory Study

OBJECTIVES: To estimate the prevalence of neuropsychiatric symptoms and examine their association with functional limitations.

Incidence of dementia and cognitive impairment, not dementia in the United States.

Estimates of incident dementia, and cognitive impairment, not dementia (CIND) (or the related mild cognitive impairment) are important for public health and clinical care policy. In this paper, we report US national incidence rates for dementia and CIND.

Midlife activity predicts risk of dementia in older male twin pairs

This was a prospective study of dementia to elucidate mechanisms of disease risk factors amenable to modification and specifically to determine whether midlife cognitive and physical leisure activities are associated with delayed onset or reduced risk of dementia within older male twin pairs.

Persistent mild cognitive impairment in geriatric depression.

BACKGROUND Cognitive impairment often occurs with geriatric depression and impairments may persist despite remission of depression. Although clinical definitions of mild cognitive impairment (MCI) have typically excluded depression, a neuropsychological model of MCI in depression has utility for identifying individuals whose cognitive impairments may persist or progress to dementia. METHODS At baseline and 1-year follow-up, 67 geriatric patients with depression had a comprehensive clinical examination that included depression assessment and neuropsychological testing. We defined MCI by a neuropsychological algorithm and examined the odds of MCI classification at Year 1 for remitted depressed individuals with baseline MCI, and examined clinical, functional and genetic factors associated with MCI. RESULTS Fifty-four percent of the sample had MCI at baseline. Odds of MCI classification at Year 1 were four times greater among patients with baseline MCI than those without. Instrumental activities of daily living were associated with MCI at Year 1, while age and APOE genotype was not. CONCLUSIONS These results confirm previous observations that MCI is highly prevalent among older depressed adults and that cognitive impairment occurring during acute depression may persist after depression remits. Self-reported decline in functional activities may be a marker for persistent cognitive impairment, which suggests that assessments of both neuropsychological and functional status are important prognostic factors in the evaluation of geriatric depression.