Unveiling the Hidden Truth of Practice Effects in Aging and Alzheimer’s Disease

Published on July 28, 2022

Imagine taking a test over and over again. At first, you might improve your performance as you get the hang of it, but eventually, your progress might plateau or even decline. This phenomenon, known as practice effects (PE), can make it challenging to accurately assess changes in cognitive abilities in aging individuals and those with neurodegenerative diseases like Alzheimer’s. However, recent research suggests that analyzing PE over time can provide valuable insights into the cognitive status and risk for decline associated with aging, mild cognitive impairment (MCI), and dementia. By understanding how PE relates to age- and disease-related changes in learning and memory, scientists hope to uncover the hidden remaining abilities that can enhance our understanding of cognitive aging. To investigate these dynamics, a study examined verbal recall performance in a group of older adults over three years. The findings revealed that participants with MCI or dementia had lower recall performance compared to cognitively unimpaired individuals, along with reduced PE gains. Additionally, dynamic modeling of PE showed distinct patterns across different diagnostic groups. These results emphasize the importance of considering PE when evaluating cognitive changes and offer new avenues for further research to enhance diagnostic accuracy and treatment strategies. If you’re curious about the nuances of practice effects in aging and Alzheimer’s disease, check out the full article!

Standardized tests of learning and memory are sensitive to changes associated with both aging and superimposed neurodegenerative diseases. Unfortunately, repeated behavioral test administration can be confounded by practice effects (PE), which may obscure declines in level of abilities and contribute to misdiagnoses. Growing evidence, however, suggests PE over successive longitudinal measurements may differentially predict cognitive status and risk for progressive decline associated with aging, mild cognitive impairment (MCI), and dementia. Thus, when viewed as a reflection of neurocognitive plasticity, PE may reveal residual abilities that can add to our understanding of age- and disease-related changes in learning and memory. The present study sought to evaluate differences in PE and verbal recall in a clinically characterized aging cohort assessed on multiple occasions over 3 years. Participants included 256 older adults recently diagnosed as cognitively unimpaired (CU; n = 126), or with MCI of amnestic (n = 65) or non-amnestic MCI (n = 2085), and multi-domain amnestic dementia of the Alzheimer’s type (DAT; n = 45). We applied a continuous time structural equation modeling (ctsem) approach to verbal recall performance on the Hopkins Verbal Learning Test in order to distinguish PE from individual occasion performance, coupled random changes, age trends, and differing measurement quality. Diagnoses of MCI and dementia were associated with lower recall performance on all trials, reduced PE gain per occasion, and differences in non-linear dynamic parameters. Practice self-feedback is a dynamic measure of the decay or acceleration in PE process changes over longitudinal occasions. As with PE and mean recall, estimated practice self-feedback followed a gradient from positive in CU participants to null in participants with diagnosed MCI and negative for those with dementia diagnoses. Evaluation of sensitivity models showed this pattern of variation in PE was largely unmodified by differences in age, sex, or educational attainment. These results show dynamic modeling of PE from longitudinal performance on standardized learning and memory tests can capture multiple aspects of behavioral changes in MCI and dementia. The present study provides a new perspective for modeling longitudinal change in verbal learning in clinical and cognitive aging research.

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