Paolo Ghisletta

Trajectories of brain aging in middle-aged and older adults: regional and individual differences.

The human brain changes with age. However, the rate and the trajectories of change vary among the brain regions and among individuals, and the reasons for these differences are unclear. In a sample of healthy middle-aged and older adults, we examined mean volume change and individual differences in the rate of change in 12 regional brain volumes over approximately 30 months. In addition to the baseline assessment, there were two follow-ups, 15 months apart. We observed significant average shrinkage of the hippocampus, entorhinal cortex, orbital-frontal cortex, and cerebellum in each of the intervals. Shrinkage of the hippocampus accelerated with time, whereas shrinkage of the caudate nucleus, prefrontal subcortical white matter, and corpus callosum emerged only at the second follow-up. Throughout both assessment intervals, the mean volumes of the lateral prefrontal and primary visual cortices, putamen, and pons did not change. Significant individual differences in shrinkage rates were observed in the lateral prefrontal cortex, the cerebellum, and all the white matter regions throughout the study, whereas additional regions (medial-temporal structures, the insula, and the basal ganglia) showed significant individual variation in change during the second follow-up. No individual variability was noted in the change of orbital frontal and visual cortices. In two white matter regions, we were able to identify factors associated with individual differences in brain shrinkage. In corpus callosum, shrinkage rate was greater in persons with hypertension, and in the pons, women and carriers of the ApoEepsilon4 allele exhibited declines not noted in the whole sample.

The Fate of Cognition in Very Old Age: Six-Year Longitudinal Findings in the Berlin Aging Study (BASE)

The authors report full-information longitudinal age gradients in 4 intellectual abilities on the basis of 6-year longitudinal changes in 132 individuals (mean age at T1 = 78.27, age range = 70-100) from the Berlin Aging Study. Relative to the cross-sectional parent sample (N = 516, mean age at T1 = 84.92 years), this sample was positively selected because of differential mortality and experimental attrition. Perceptual speed, memory, and fluency declined with age. In contrast, knowledge remained stable up to age 90, with evidence for decline thereafter. Age gradients were more negative in old old (n = 66, mean age at T1 = 83.04) than in old (n = 66, mean age at T1 = 73.77) participants. Rates of decline did not differ reliably between men and women or between participants with high versus low life-history status. They conclude that intellectual development after age 70 varies by distance to death, age, and intellectual ability domain.

An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals.

Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.

Social participation attenuates decline in perceptual speed in old and very old age

Does an engaged and active lifestyle in old age alleviate cognitive decline, does high cognitive functioning in old age increase the possibility of maintaining an engaged and active lifestyle, or both? The authors approach this conundrum by applying a structural equation model for testing dynamic hypotheses, the dual change score model (J. J. McArdle & F. Hamagami, 2001), to 3-occasion longitudinal data from the Berlin Aging Study (Time 1: n=516, age range=70-103 years). Results reveal that within a bivariate system of perceptual speed and social participation, with age and sociobiographical status as covariates, prior scores of social participation influence subsequent changes in perceptual speed, while the opposite does not hold. Results support the hypothesis that an engaged and active lifestyle in old and very old age may alleviate decline in perceptual speed.

Cognitive and sensory declines in old age: Gauging the evidence for a common cause

Resource accounts of behavioral aging postulate that age-associated impairments within and across intellectual and sensory domains reflect, in part, a common set of senescent alterations in the neurochemistry and neuroanatomy of the aging brain. Hence, these accounts predict sizeable correlations of between-person differences in rates of decline, both within and across intellectual and sensory domains. The authors examined reliability-adjusted variances and covariances in longitudinal change for 8 cognitive measures and for close visual acuity, distant visual acuity, and hearing in 516 participants in the Berlin Aging Study (ages 70 to 103 years at 1st measurement). Up to 6 longitudinal measurements were distributed over up to 13 years. Individual differences in rates of cognitive decline were highly correlated, with a single factor accounting for 60% of the variance in cognitive change. This amount increased to 65% when controlling for age at first measurement, distance to death, and risk of dementia. Contrary to expectations, the correlations between cognitive and sensory declines were only moderate in size, underscoring the need to delineate both domain-general and function-specific mechanisms of behavioral senescence.

On the power of multivariate latent growth curve models to detect correlated change.

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris (1985) were used to evaluate the power to detect slope covariances. Even with large samples (N = 500) and several longitudinal occasions (4 or 5), statistical power to detect covariance of slopes was moderate to low unless growth curve reliability at study onset was above .90. Studies using LGCMs may fail to detect slope correlations because of low power rather than a lack of relationship of change between variables. The present findings allow researchers to make more informed design decisions when planning a longitudinal study and aid in interpreting LGCM results regarding correlated interindividual differences in rates of development.

Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change

We evaluated the statistical power of single-indicator latent growth curve models to detect individual differences in change (variances of latent slopes) as a function of sample size, number of longitudinal measurement occasions, and growth curve reliability. We recommend the 2 degree-of-freedom generalized test assessing loss of fit when both slope-related random effects, the slope variance and intercept-slope covariance, are fixed to 0. Statistical power to detect individual differences in change is low to moderate unless the residual error variance is low, sample size is large, and there are more than four measurement occasions. The generalized test has greater power than a specific test isolating the hypothesis of zero slope variance, except when the true slope variance is close to 0, and has uniformly superior power to a Wald test based on the estimated slope variance.

Longitudinal Cognition-Survival Relations in Old and Very Old Age 13-Year Data from the Berlin Aging Study

We use a statistical model that combines longitudinal and survival analyses to estimate the influence of level and change in cognition on age at death in old and very old individuals. Data are from the Berlin Aging Study, in which an initial sample of 516 elderly individuals with an age range of 70 to 103 years was assessed up to 11 times across a period of up to 13 years. Four cognitive ability domains were assessed by two variables each: perceptual speed (Digit Letter and Identical Pictures), episodic memory (Paired Associates and Memory for Text), fluency (Categories and Word Beginnings), and verbal knowledge (Vocabulary and Spot-a-Word). Longitudinal models on cognition controlled for dementia diagnosis and retest effects, while survival models on age at death controlled for age, sex, socioeconomic status, sensory and motor performance, and broad personality characteristics. Results indicate: (1) Individual differences in the level of and in the linear change in performance are present for all cognitive variables; (2) when analyzed independently of cognitive performance, all covariates, except broad personality factors, predict survival; (3) when cognitive performance is accounted for, age, sex, and motor performance do predict survival, while socioeconomic status and broad personality factors do not, and sensory performance does only at times; (4) when cognitive variables are analyzed independently of each other, both level and change in speed and fluency, as well as level in memory and knowledge predict survival; (5) when all cognitive variables are analyzed simultaneously using a two-stage procedure, none of them is significantly associated to survival. In agreement with others, our findings suggest that survival is related to cognitive development in old and very old age in a relatively global, rather than ability-specific, manner.

Static and Dynamic Longitudinal Structural Analyses of Cognitive Changes in Old Age

Background: Among the main data-analytical advances of recent decades are Latent Growth Models (LGM) and Multilevel Models (MLM) for the analysis of longitudinal data. Objective: We discuss the relative advantages and disadvantages of the two analytical methods and offer some practical guidelines concerning the choice between LGM and MLM based on (a) completeness and balance of the data, (b) theoretical functional form of change examined, (c) examination of the error structure, (d) theoretical relations among differential level effects and differential change effects, and (e) role of time-invariant covariates. Methods: To discuss LGM and MLM, we provide illustrations from applications to the Berlin Aging Study (BASE) and the Swiss Interdisciplinary Longitudinal Study on the Oldest Old (SWILSO-O). Results: As predicted by two-component theories of lifespan cognition, performance on a vocabulary test (indicator of broad crystallized intelligence) did not decline over time, while scores on a digit letter test (indicator of broad fluid intelligence) decreased over 6 years. Differential level effects were obtained on both variables, while average and differential change effects were obtained only for the digit letter test. In a second set of analyses, we tested the error-free effect that a broad fluid intelligence indicator exerted on the latent yearly change in a broad crystallized intelligence indicator, and vice versa. In both data sets we obtained strong evidence for a more reliable effect of the fluid indicator on the change in the crystallized indicator. This evidence provided support for the dedifferentiation hypothesis of cognitive abilities in very old age. Conclusions: New insights into cognitive aging phenomena can be gained with proper applications of LGM and MLM. We posit that the choice between LGM and MLM and their specification rests on theoretical and empirical motives to be defined a priori.

Cognition in the Berlin Aging Study (BASE) : The first ten years

This paper summarizes and expands research on cognitive aging from the Berlin Aging Study (BASE), a longitudinal, multidisciplinary, and population-based investigation of old and very old individuals. First, we describe previously reported research on five key themes: (a) experimental and mortality-associated components of longitudinal selectivity; (b) comparisons between cross-sectional and cross-sectional/longitudinal convergence age gradients; (c) old-age dedifferentiation of inter-individual differences; (d) possible reasons for the age-based increase in the link between intellectual and sensory domains; and (e) limits to cognitive plasticity in very old age. Second, we make use of multilevel modeling to determine the magnitude and direction of retest effects. Retest effects are classified as either flat (step function from the first to the second measurement occasion) or growing (linear increase after the first measurement occasion). Five of the eight longitudinally administered cognitive tests are found to display significant retest effects of either or both types. Retest adjustment increased the linear negative and decreased the quadratic negative component of cross-sectional/longitudinal convergence gradients in measures of intellectual abilities.