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1.  Neuropsychological Test Performance and Cognitive Reserve in Healthy Aging and the Alzheimer’s Disease Spectrum: A Theoretically-Driven Factor Analysis 
Accurate measurement of cognitive function is critical for understanding the disease course of Alzheimer’s disease (AD). Detecting cognitive change over time can be confounded by level of premorbid intellectual function or cognitive reserve and lead to under or over diagnosis of cognitive impairment and AD. Statistical models of cognitive performance that include cognitive reserve can improve sensitivity to change and clinical efficacy. We used confirmatory factor analysis to test a four-factor model comprised of memory/language, processing speed/executive function, attention, and cognitive reserve factors in a group of cognitively healthy older adults and a group of participants along the spectrum of amnestic mild cognitive impairment to AD (aMCI-AD). The model showed excellent fit for the control group (χ2 = 100, df = 78, CFI = .962, RMSEA = .049) and adequate fit for the aMCI-AD group (χ2 = 1750, df = 78, CFI = .932, RMSEA = .085). Though strict invariance criteria were not met, invariance testing to determine if factor structures are similar across groups yielded acceptable absolute model fits and provide evidence in support of configural, metric, and scalar invariance. These results provide further support for the construct validity of cognitive reserve in healthy and memory impaired older adults.
doi:10.1017/S1355617712000859
PMCID: PMC3600814  PMID: 23039909
mild cognitive impairment; brain reserve; executive function; memory function; dementia; cognition
2.  Dynamic Associations of Change in Physical Activity and Change in Cognitive Function: Coordinated Analyses of Four Longitudinal Studies 
Journal of Aging Research  2012;2012:493598.
The present study used a coordinated analyses approach to examine the association of physical activity and cognitive change in four longitudinal studies. A series of multilevel growth models with physical activity included both as a fixed (between-person) and time-varying (within-person) predictor of four domains of cognitive function (reasoning, memory, fluency, and semantic knowledge) was used. Baseline physical activity predicted fluency, reasoning and memory in two studies. However, there was a consistent pattern of positive relationships between time-specific changes in physical activity and time-specific changes in cognition, controlling for expected linear trajectories over time, across all four studies. This pattern was most evident for the domains of reasoning and fluency.
doi:10.1155/2012/493598
PMCID: PMC3457643  PMID: 23029615
3.  Cognitively Stimulating Activities: Effects on Cognition across Four Studies with up to 21 Years of Longitudinal Data 
Journal of Aging Research  2012;2012:461592.
Engagement in cognitively stimulating activities has been considered to maintain or strengthen cognitive skills, thereby minimizing age-related cognitive decline. While the idea that there may be a modifiable behavior that could lower risk for cognitive decline is appealing and potentially empowering for older adults, research findings have not consistently supported the beneficial effects of engaging in cognitively stimulating tasks. Using observational studies of naturalistic cognitive activities, we report a series of mixed effects models that include baseline and change in cognitive activity predicting cognitive outcomes over up to 21 years in four longitudinal studies of aging. Consistent evidence was found for cross-sectional relationships between level of cognitive activity and cognitive test performance. Baseline activity at an earlier age did not, however, predict rate of decline later in life, thus not supporting the concept that engaging in cognitive activity at an earlier point in time increases one's ability to mitigate future age-related cognitive decline. In contrast, change in activity was associated with relative change in cognitive performance. Results therefore suggest that change in cognitive activity from one's previous level has at least a transitory association with cognitive performance measured at the same point in time.
doi:10.1155/2012/461592
PMCID: PMC3449118  PMID: 23024862
4.  Social Activity and Cognitive Functioning Over Time: A Coordinated Analysis of Four Longitudinal Studies 
Journal of Aging Research  2012;2012:287438.
Social activity is typically viewed as part of an engaged lifestyle that may help mitigate the deleterious effects of advanced age on cognitive function. As such, social activity has been examined in relation to cognitive abilities later in life. However, longitudinal evidence for this hypothesis thus far remains inconclusive. The current study sought to clarify the relationship between social activity and cognitive function over time using a coordinated data analysis approach across four longitudinal studies. A series of multilevel growth models with social activity included as a covariate is presented. Four domains of cognitive function were assessed: reasoning, memory, fluency, and semantic knowledge. Results suggest that baseline social activity is related to some, but not all, cognitive functions. Baseline social activity levels failed to predict rate of decline in most cognitive abilities. Changes in social activity were not consistently associated with cognitive functioning. Our findings do not provide consistent evidence that changes in social activity correspond to immediate benefits in cognitive functioning, except perhaps for verbal fluency.
doi:10.1155/2012/287438
PMCID: PMC3444000  PMID: 22991665
5.  A web-based normative calculator for the uniform data set (UDS) neuropsychological test battery 
Introduction
With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the neuropsychological tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.
Methods
Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all neuropsychological tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.
Results
For each neuropsychological test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.
Conclusions
An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.
doi:10.1186/alzrt94
PMCID: PMC3308021  PMID: 22078663
Alzheimer's disease; cognitive aging; MCI; memory; norms

Results 1-5 (5)