IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients

Przybyszewski, Andrzej W.; Nowacki, J.; Drabik, Aldona; Szlufik, Stanisław; Koziorowski, Dariusz

doi:10.1007/978-3-030-42058-1_46

Cited by 4 publications

(8 citation statements)

References 14 publications

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“…We have estimated PD disease progression by all our (general) attributes, by only motor-related attributes, and by only the cognitive symptoms. The general and motor attribute predicted disease progression (UPDRS changes) well, but the correlation with cognitive attributes was much weaker [41]. The cognitive attributes are not changing with disease progression in all PD patients, and it is the opposite of Alzheimer's disease, mainly related to cognitive changes (MMSE changes).…”

Section: Discussionmentioning

confidence: 87%

“…Another critical issue is related to disease progression and related changes in different parts of the brain or symptoms related to the motor/cognitive and emotional systems [41]. We have estimated PD disease progression by all our (general) attributes, by only motor-related attributes, and by only the cognitive symptoms.…”

Section: Discussionmentioning

confidence: 99%

“…In [41] two BMT (only on medication) groups of patients were tested: the first one, less advanced, was tested three times every half year (visit 1, visit 2, visit 3). In the second BMT group, more advanced patients were tested only once.…”

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 99%

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Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

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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.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Another critical issue is related to disease progression and related changes in different parts of the brain or symptoms related to the motor/cognitive and emotional systems [ 41 ]. We have estimated PD progression by all of our (general) attributes, by only motor-related attributes, and by only cognitive symptoms.…”

Section: Discussionmentioning

confidence: 99%

“…In [ 41 ], two BMT groups of patients (only on medication) were tested. The first one, less advanced, was tested three times every half year (visit 1, visit 2, visit 3).…”

Section: Eye Movements and Neurodegenerative Diseasesmentioning

confidence: 99%

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski

Śledzianowski

Chudzik

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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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Screening, Prediction and Remission of Depressive Disorder Using the Fuzzy Probability Function and Petri Net

Chiang,

Chen

2024

Studies in Big Data

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IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients

Cited by 4 publications

References 14 publications

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Screening, Prediction and Remission of Depressive Disorder Using the Fuzzy Probability Function and Petri Net

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