ABSTRACT
Introduction
Mean cognitive performance is worse in amnestic mild cognitive impairment (aMCI) compared to control groups. However, studies on variability of cognitive performance in aMCI have yielded inconclusive results, with many differences in variability measures and samples from one study to another.
Methods
We examined variability in aMCI using an existing older adult sample (n = 91; 51 with aMCI, 40 with normal cognition for age), measured with an online self-administered computerized cognitive assessment (Cogniciti’s Brain Health Assessment). Our methodology extended past findings by using pure measures of variability (controlling for confounding effects of group performance or practice), and a clinically representative aMCI sample (reflecting the continuum of cognitive performance between normal cognition and aMCI).
Results
Between-group t-tests showed significantly greater between-person variability (interindividual variability or diversity) in overall cognitive performance in aMCI than controls, although the effect size was with a small to moderate effect size, d = 0.44. No significant group differences were found in within-person variability (intraindividual variability) across cognitive tasks (dispersion) or across trials of a response time task (inconsistency), which may be because we used a sample measuring the continuum of cognitive performance. Exploratory correlation analyses showed that a worse overall score was associated with greater inter- and intraindividual variability, and that variability measures were correlated with each other, indicating people with worse cognitive performance were more variable.
Discussion
The current study demonstrates that self-administered online tests can be used to remotely assess different types of variability in people at risk of Alzheimer`s. Our findings show small but significantly more interindividual differences in people with aMCI. This diversity is considered as “noise” in standard assessments of mean performance, but offers an interesting and cognitively informative “signal” in itself.
Acknowledgments
We would like to acknowledge the following individuals’ contributions: Michael Meagher assisted with preparation of the CABHI grant application and provided project management. Caitlin Johnston provided project management for data collection. Brintha Sivajohan, Komal Shaikh, Rebecca Trossman, and Rachel Downey conducted participant assessments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data Availability Statement
Data, code for statistical analyses, and study materials can be made available to researchers upon request. The analysis plan was pre-registered in advance (https://osf.io/k6gf2/?view_only=0820ced3b1524cd8be7c175b63d66b87).
Author Contributions CRediT Statement
AL: Conceptualization, Methodology, Data curation, Formal Analysis, Writing – Original Draft, Writing – Review and Editing, Funding Acquisition; TP: Conceptualization, Methodology, Investigation (data acquisition), Data curation, Project administration, Writing – Review and Editing; SG: Data curation, Formal Analysis, Writing – Review and Editing; KS: Investigation (data acquisition), Writing – Review and Editing; MF: Investigation, Writing – Review and Editing; BL: Investigation (data acquisition), Writing – Review and Editing, Supervision, Funding Acquisition; AT: Conceptualization, Methodology, Investigation (data acquisition), Writing – Review and Editing, Supervision, Funding Acquisition; NA: Conceptualization, Methodology, Writing – Review and Editing, Supervision, Funding Acquisition