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Challenges and opportunities in characterizing cognitive aging across species

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With the prolonged lifespan brought about by medical advances, attention is increasingly focused on problems that arise from such longevity, including diseases that tend to occur in elderly populations. Despite progress on health related issues, little progress has been made with respect to the treatment of ...

With the prolonged lifespan brought about by medical advances, attention is increasingly focused on problems that arise from such longevity, including diseases that tend to occur in elderly populations. Despite progress on health related issues, little progress has been made with respect to the treatment of cognitive decline that can accompany the aging process. In humans, age-related impairments can become apparent in middle-age and progress with advancing age. However, not all cognitive and behavioral processes are equally vulnerable to senescence. An important aspect of defining normal cognitive aging is to emphasize the distinction between age-related deficits not specifically associated with disease, and those that do result from neurological diseases (such as Alzheimer’s disease). It is important to recognize that within the population of disease free elderly; there is considerable variability in cognition. This fact suggests that understanding the biological or physiological aging of specific brain systems, rather than using chronological age alone as the defining variable, is key to understanding individual differences and successful trajectories in cognitive aging.



This Research Topic, focused on “Challenges and opportunities in characterizing cognitive aging across species” is the result of the McKnight Brain Research Foundation working group meetings dedicated to examining a library of age-sensitive tasks that can be used to translate findings across different levels of analysis and across different species. The manuscripts in this issue identify tests that are useful in both humans and animal experimental models, based on specific cognitive processes, which in most cases depend upon defined neural systems. The aim of this work is to allow researchers to draw more accurate conclusions about human cognitive aging from work in other model species. Additionally, this work highlights gaps in linking human and animal laboratory models, and recommends approaches for filling those gaps.


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