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To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease
BACKGROUND: Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease. METHOD: Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273577/ https://www.ncbi.nlm.nih.gov/pubmed/34262424 http://dx.doi.org/10.3389/fnins.2021.654003 |
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author | Staal, Justine Mattace-Raso, Francesco Daniels, Hennie A. M. van der Steen, Johannes Pel, Johan J. M. |
author_facet | Staal, Justine Mattace-Raso, Francesco Daniels, Hennie A. M. van der Steen, Johannes Pel, Johan J. M. |
author_sort | Staal, Justine |
collection | PubMed |
description | BACKGROUND: Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease. METHOD: Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients. RESULTS: Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve). COMPARISON WITH EXISTING METHOD(S): The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers. CONCLUSION: The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD. |
format | Online Article Text |
id | pubmed-8273577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82735772021-07-13 To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease Staal, Justine Mattace-Raso, Francesco Daniels, Hennie A. M. van der Steen, Johannes Pel, Johan J. M. Front Neurosci Neuroscience BACKGROUND: Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease. METHOD: Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients. RESULTS: Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve). COMPARISON WITH EXISTING METHOD(S): The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers. CONCLUSION: The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD. Frontiers Media S.A. 2021-06-28 /pmc/articles/PMC8273577/ /pubmed/34262424 http://dx.doi.org/10.3389/fnins.2021.654003 Text en Copyright © 2021 Staal, Mattace-Raso, Daniels, van der Steen and Pel. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Neuroscience Staal, Justine Mattace-Raso, Francesco Daniels, Hennie A. M. van der Steen, Johannes Pel, Johan J. M. To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title | To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title_full | To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title_fullStr | To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title_full_unstemmed | To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title_short | To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease |
title_sort | to explore the predictive power of visuomotor network dysfunctions in mild cognitive impairment and alzheimer’s disease |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273577/ https://www.ncbi.nlm.nih.gov/pubmed/34262424 http://dx.doi.org/10.3389/fnins.2021.654003 |
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