Abstract
Young onset Alzheimer’s disease (YOAD) is defined as symptom onset before the age of 65 years and is particularly associated with phenotypic heterogeneity. Atypical presentations, such as the clinic-radiological visual syndrome posterior cortical atrophy (PCA), often lead to delays in accurate diagnosis. Eyetracking has been used to demonstrate basic oculomotor impairments in individuals with dementia. In the present study, we aim to explore the relationship between eyetracking metrics and standard tests of visual cognition in individuals with YOAD. Fifty-seven participants were included: 36 individuals with YOAD (n = 26 typical AD; n = 10 PCA) and 21 age-matched healthy controls. Participants completed three eyetracking experiments: fixation, pro-saccade, and smooth pursuit tasks. Summary metrics were used as outcome measures and their predictive value explored looking at correlations with visuoperceptual and visuospatial metrics. Significant correlations between eyetracking metrics and standard visual cognitive estimates are reported. A machine-learning approach using a classification method based on the smooth pursuit raw eyetracking data discriminates with approximately 95% accuracy patients and controls in cross-validation tests. Results suggest that the eyetracking paradigms of a relatively simple and specific nature provide measures not only reflecting basic oculomotor characteristics but also predicting higher order visuospatial and visuoperceptual impairments. Eyetracking measures can represent extremely useful markers during the diagnostic phase and may be exploited as potential outcome measures for clinical trials.
Highlights
Alzheimer’s disease (AD) is the most common major neurodegenerative dementia type [1]
We explored the relationship between eyetracking and standard visual cognitive tests in individuals with young onset AD
Within the Young onset Alzheimer’s disease (YOAD) patients, typical AD (tAD) and posterior cortical atrophy (PCA) were matched in terms of disease duration [tAD: 5.0 (2.8) years and PCA: 5.6 (3.4) years, Wilcoxon Mann–Whitney U-test, z = −0.50, p = 0.62] and Mini-Mental State Examination (MMSE) scores [tAD: 20.1 (0.8) and PCA: 23.1 (1.5), Wilcoxon Mann–Whitney U-test, z = −1.78, p = 0.07]
Summary
Alzheimer’s disease (AD) is the most common major neurodegenerative dementia type [1]. Crutcher and colleagues [44] and Richmond and colleagues [45] used a visual paired-comparison task and showed that eye movement metrics, such as number of fixations and fixation duration, can be indicative of short-term memory difficulties in a group of patients with mild cognitive impairment (MCI) as compared to age-matched controls. Modern eyetracking systems have excellent recording frames of up to 1,000 Hz, enabling the building of very large datasets (time series of x and y coordinates) in a relatively short amount of time (e.g., a 1,000 Hz system generates 600,000 x–y data coordinates for a 10-min recording session) Such qualities represent incentives to fully explore the possible contribution of eyetracking metrics to an accurate and sensitive diagnosis and as outcome markers for clinical trials. Our secondary hypothesis was that machine-learning classifiers would offer the discriminative power [47] for the diagnosis of young onset AD among healthy controls based on oculomotor profiles during a discrete task
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