Steady as she goes! Daily fluctuations in cognitive ability are associated with risk of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that results in progressive loss of cognitive function. It is critical to identify individuals who are at the highest risk of developing AD as early as possible so that appropriate treatment plans can be developed. Neuropsychological tests of psychomotor speed, attention, and memory are frequently used to assess AD risk, and these cognitive functions are usually measured in a single session at regular but wide intervals (e.g., once per year). This assessment paradigm provides only a snapshot of cognitive performance and ignores fluctuationsin performance that may occur from day to day. A large literature indicates that daily variations in cognition are associated with numerous factors, including fatigue, stress, and motivation. In a study recently published in Neuropsychologyopens in new window, Andrew J. Aschenbrenner, Jason Hassenstab, John C. Morris, Carlos Cruchaga, and Joshua J. Jackson investigated whether variability in cognitive performance across distinct testing occasions is associated with risk of AD, defined as possessing a genetic risk factor, the apolipoprotein E (APOE) ε4 allele.

Participants for this study were recruited from an ongoing study of memory and aging at the Knight Alzheimer Disease Research Center at Washington University School of Medicine in St. Louis. A total of 280 cognitively healthy older adults completed a series of brief cognitive tests four times per day for 1 week (a total of 28 possible cognitive assessments). Participants were stratified in terms of their APOE genotype (no ε4 alleles = low risk; 1 or more ε4 alleles = high risk), and the researchers examined the participants’ differences in average cognitive performance as well as the variability of their performance over the week using an advanced statistical technique known as mixed effects location scale models. Across the 28 assessments, high-risk participants performed worse on measures of processing speed and working memory ability, consistent with prior research. In addition to showing changes in mean performance, high-risk participants had more variability in their performance across the different assessments, meaning that relative to a low-risk participant, they had more days on which they performed noticeably better or worse. This increased variability was present only on a measure of processing speed and not on measures of working memory or episodic memory. The researchers argue that the unique sensitivity of the processing speed task is due to early changes in the efficiency of attentional control processes in early AD.

This study is unique in that it is the first to demonstrate variability differences across different days in a preclinical AD sample using an innovative, high-frequency cognitive assessment paradigm. Not only do the study results show that the magnitude of cognitive variability (which in some cases was quite large) conveys additional information about AD risk, they also highlight the potential limitations of single-shot assessments, in that any single observation may not adequately capture an individual’s true ability if the individual is experiencing a particularly good or bad cognitive day. Given that these cognitive tests can be delivered remotely and that the high-frequency assessment paradigm is well-tolerated by the majority of research participants, the researchers argue that continued joint investigations of mean cognitive performance and variability are needed to fully understand the cognitive changes associated with AD.

This article is in the Developmental Psychology topic area.

Aschenbrenner, A. J., Hassenstab, J., Morris, J. C., Cruchaga, C., & Jackson, J. J. (2024). Relationships between hourly cognitive variability and risk of Alzheimer’s disease revealed with mixed-effects location scale models. Neuropsychology, 38(1), 69–80. https://doi.org/10.1037/neu0000905opens in new window

Andrew J. Aschenbrenner, PhD, is an assistant professor in neurology at the Washington University School of Medicine in St. Louis with expertise in the development and analysis of cognitive tests to understand patterns of cognitive change in Alzheimer’s disease. He is currently using high-frequency measurement burst designs to understand how cognitive variability contributes to an individual’s Alzheimer’s disease risk.

Jason Hassenstab, PhD, is a professor in neurology and psychological and brain sciences at the Washington University School of Medicine in St. Louis. His lab focuses on using digital technology to improve cognitive assessment in Alzheimer’s disease and other neurodegenerative disorders.

John C. Morris, MD, is a professor in neurology and the director of the Knight Alzheimer Disease Research Center at the Washington University School of Medicine in St. Louis. His specific research interests include improving the diagnosis of early-stage Alzheimer’s disease and identifying preclinical stages of Alzheimer’s disease using biomarker and imaging metrics.

Carlos Cruchaga, PhD, is a professor in psychiatry and the director of the NeuroGenomics and Informatics Center at the Washington University School of Medicine in St. Louis. He is interested in using human genomic data to understand the biological processes that lead to Alzheimer’s disease and other neurodegenerative disorders.

Joshua J. Jackson, PhD, is the Rosenzweig Associate Professor of Personality Science in the Department of Psychological and Brain Sciences of the Washington University School of Medicine in St. Louis. His research focuses on the assessment and development of individual differences across different time scales.