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Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and...

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Autores principales: Przybyszewski, Andrzej W., Śledzianowski, Albert, Chudzik, Artur, Szlufik, Stanisław, Koziorowski, Dariusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968124/
https://www.ncbi.nlm.nih.gov/pubmed/36850743
http://dx.doi.org/10.3390/s23042145
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author Przybyszewski, Andrzej W.
Śledzianowski, Albert
Chudzik, Artur
Szlufik, Stanisław
Koziorowski, Dariusz
author_facet Przybyszewski, Andrzej W.
Śledzianowski, Albert
Chudzik, Artur
Szlufik, Stanisław
Koziorowski, Dariusz
author_sort Przybyszewski, Andrzej W.
collection PubMed
description Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are 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, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.
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spelling pubmed-99681242023-02-27 Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms Przybyszewski, Andrzej W. Śledzianowski, Albert Chudzik, Artur Szlufik, Stanisław Koziorowski, Dariusz Sensors (Basel) Review Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are 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, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases. MDPI 2023-02-14 /pmc/articles/PMC9968124/ /pubmed/36850743 http://dx.doi.org/10.3390/s23042145 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Przybyszewski, Andrzej W.
Śledzianowski, Albert
Chudzik, Artur
Szlufik, Stanisław
Koziorowski, Dariusz
Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title_full Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title_fullStr Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title_full_unstemmed Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title_short Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms
title_sort machine learning and eye movements give insights into neurodegenerative disease mechanisms
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968124/
https://www.ncbi.nlm.nih.gov/pubmed/36850743
http://dx.doi.org/10.3390/s23042145
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