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Updated: Apr 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Learning impairments identified early in life are predictive of future impairments associated with aging
Rikki Hullinger1, Corinna Burger2
1Neuroscience Training Program, University of Wisconsin-Madison, USA.
Abstract:
The Morris water maze (MWM) behavioral paradigm is commonly used to measure spatial learning and memory in rodents. It is widely accepted that performance in the MWM declines with age. However, young rats ubiquitously perform very well on established versions of the water maze, suggesting that more challenging tasks may be required to reveal subtle differences in young animals. Therefore, we have used a one-day water maze and novel object recognition to test whether more sensitive paradigms of memory in young animals could identify subtle cognitive impairments early in life that might become accentuated later with senescence. We have found that these two tasks reliably separate young rats into inferior and superior learners, are highly correlated, and that performance on these tasks early in life is predictive of performance at 12 months of age. Furthermore, we have found that repeated training in this task selectively improves the performance of inferior learners, suggesting that behavioral training from an early age may provide a buffer against age-related cognitive decline.
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