Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Version 1 : Received: 25 October 2022 / Approved: 2 November 2022 / Online: 2 November 2022 (04:12:08 CET)

A peer-reviewed article of this Preprint also exists.

Przybyszewski, A.W.; Śledzianowski, A.; Chudzik, A.; Szlufik, S.; Koziorowski, D. Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms. Sensors 2023, 23, 2145. Przybyszewski, A.W.; Śledzianowski, A.; Chudzik, A.; Szlufik, S.; Koziorowski, D. Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms. Sensors 2023, 23, 2145.

Abstract

Humans are a vision-dominated species, and what we see depends on where we look. Therefore, eye movements (EM) are essential to our interactions with the environment, and experimental findings show EM is affected in neurodegenerative disorders (ND). It could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to determine if changes in EM-evoked responses can tell us about ND, such as Alzheimer’s (AD) and Parkinson’s Disease (PD) progression in different stages. In the present review, we have analyzed the results of neurological, psychological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with Machine Learning (ML) methods. Described predictive algorithms are using various approaches, including Granular Computing, Naive Bayes, Decision Trees/Tables, Logistic Regression, C-/LinearSVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are also navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes. In conclusion: EM parameters with clinical symptoms are powerful precision instruments that, in addition to predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

Keywords

Alzheimer’s Disease; Parkinson’s Disease; eye movements; Rough Set; Machine Learning

Subject

Medicine and Pharmacology, Neuroscience and Neurology

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