Dynamic modeling of practice effects across the healthy aging-Alzheimer's disease continuum

Abstract

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.

Keywords: Alzheimer’s disease (AD); aging; dementia; dynamic modeling; learning; mild cognitive impairment; practice effects; verbal memory.