This research is inspired by the profound demographic changes that have become increasingly acute in the past decades. With substantial gains in life expectancy, the World population is rapidly aging, and in the industrialized nations, older adults are progressively becoming the dominant part of the population. In parallel, the Western nations are experiencing a concomitant reduction in the incidence of dementia. Thus, the need for understanding normative aging comes to the fore.
In contrast to massive efforts directed to curing Alzheimer’s disease and other dementias, relatively little attention is paid to the plight of normative older adults. If in the future most humans will become older, non-demented yet somewhat cognitively impaired adults, understanding of normative cognitive aging and its biological underpinnings is essential for future public policy making and planning.
The problem of studying normative aging is that it requires painstaking long-term longitudinal studies that must consider many relevant variables. Such studies are expensive and therefore still relatively rare. Fortunately, several important longitudinal studies of normal aging have already generated a substantial amount of data that, under appropriate analytic scrutiny, can yield valuable insights into the age-related changes in the brain and their cognitive correlates. Therefore, the application of sophisticated multivariate dynamic models to analyzing the existing longitudinal data lies in the core of this project.
In summary, we propose to apply state-of-the-art statistical modeling techniques to longitudinal data that include multiple indicators of cognitive performance (fluid reasoning, verbal aptitude, working and episodic memory, perceptual speed), MRI-derived structural brain measures (regional volume and cortical thickness, white matter diffusion properties, regional iron and myelin content, functional connectivity and resting state activation variability) as well as vascular and metabolic risk indicators (lipid panel and metabolic panel blood data, blood pressure, and genetic risk variants) that are emerging as significant modifiers of individual aging trajectories. We will apply dynamic longitudinal multivariate statistical models, to examine age-related change over time and variance therein, lead-lag relationships between cognitive and brain variables, and the modifying influence of the metabolic/vascular risk on these bidirectional associations.
Our aim is to produce important insights into the normal aging process, its brain mechanisms, cognitive consequences, and important physiological and genetic modi fiers of individual aging trajectories. The results of this project will shape the general understanding of normative aging, thus eventually contributing to improvement of the well-being of the progressively aging population.
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