five

Sex differences in cognitive aging: a 4-year longitudinal study in marmosets.

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://zenodo.org/record/5497707
下载链接
链接失效反馈
官方服务:
资源简介:
Longitudinal studies are essential to understand healthy and pathological neurocognitive aging such as Alzheimer’s Disease, but longitudinal designs are rare in both humans and non-human primate models of aging because of the difficulty of tracking cognitive change in long-lived primates. Common marmosets ( Callithrix jacchus ) are uniquely suited for aging studies due to their naturally short lifespan (10- 12 years), sophisticated cognitive and social abilities and Alzheimer Disease-like neuropathology. We report the first longitudinal study of cognitive aging in marmosets (N=28) as they transitioned from middle- (~ 5 years) to old age (~ 9 years). We characterized aging trajectories using reversal learning with different stimuli each year. Marmosets initially improved on cognitive performance due to practice, but worsened in the final year, suggesting the onset of age-related decline. Cognitive impairment emerged earlier in females than males and was more prominent for discrimination than for reversal learning. Sex differences in cognitive aging could not be explained by differences in motivation or motor abilities, which improved or remained stable across aging. Likewise, males and females did not differ in aging trajectories of overall behavior or reactivity to a social stressor, with the exception of a progressive decline in the initiation of social behavior in females. Patterns of cognitive aging were highly variable across marmosets of both sexes, suggesting the potential for pathological aging for some individuals. Future work will link individual cognitive trajectories to neuropathology in order to better understand the relationships between neuropathologic burden and vulnerability to age-related cognitive decline in each sex.
创建时间:
2021-10-19
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作