Home > Research > Publications & Outputs > Eye Movement Latency Coefficient of Variation a...

Electronic data

  • Vision_Accepted_April_2023_

    Accepted author manuscript, 451 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Eye Movement Latency Coefficient of Variation as a Predictor of Cognitive Impairment: An Eye Tracking Study of Cognitive Impairment

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
Article number38
<mark>Journal publication date</mark>1/05/2023
<mark>Journal</mark>Vision
Issue number2
Volume7
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Studies demonstrated impairment in the control of saccadic eye movements in Alzheimer’s disease (AD) and people with mild cognitive impairment (MCI) when conducting the pro-saccade and antisaccade tasks. Research showed that changes in the pro and antisaccade latencies may be particularly sensitive to dementia and general executive functioning. These tasks show potential for diagnostic use, as they provide a rich set of potential eye tracking markers. One such marker, the coefficient of variation (CV), is so far overlooked. For biological markers to be reliable, they must be able to detect abnormalities in preclinical stages. MCI is often viewed as a predecessor to AD, with certain classifications of MCI more likely than others to progress to AD. The current study examined the potential of CV scores on pro and antisaccade tasks to distinguish participants with AD, amnestic MCI (aMCI), non-amnesiac MCI (naMCI), and older controls. The analyses revealed no significant differences in CV scores across the groups using the pro or antisaccade task. Antisaccade mean latencies were able to distinguish participants with AD and the MCI subgroups. Future research is needed on CV measures and attentional fluctuations in AD and MCI individuals to fully assess this measure’s potential to robustly distinguish clinical groups with high sensitivity and specificity.