Pavisic, IM;
Firth, NF;
Parsons, S;
Martinez Rego, D;
Shakespeare, TJ;
Yong, KXX;
Slattery, CF;
... Primativo, S; + view all
(2017)
Eyetracking Metrics in Young Onset Alzheimer’s Disease: A Window into Cognitive Visual Functions.
Frontiers in Neurology
, 8
, Article 377. 10.3389/fneur.2017.00377.
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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.
| Type: | Article |
|---|---|
| Title: | Eyetracking Metrics in Young Onset Alzheimer’s Disease: A Window into Cognitive Visual Functions |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fneur.2017.00377 |
| Publisher version: | http://doi.org/10.3389/fneur.2017.00377 |
| Language: | English |
| Additional information: | © 2017 Pavisic, Firth, Parsons, Rego, Shakespeare, Yong, Slattery, Paterson, Foulkes, Macpherson, Carton, Alexander, Shawe-Taylor, Fox, Schott, Crutch and Primativo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Young onset Alzheimer’s disease, eye movements, eyetracking metrics, cognitive visual functions, machine learning, classification model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1570348 |
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