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Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia. Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by audito...

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Autores principales: Doan, Dieu Ni Thi, Ku, Boncho, Choi, Jungmi, Oh, Miae, Kim, Kahye, Cha, Wonseok, Kim, Jaeuk U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077968/
https://www.ncbi.nlm.nih.gov/pubmed/33927610
http://dx.doi.org/10.3389/fnagi.2021.659817
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author Doan, Dieu Ni Thi
Ku, Boncho
Choi, Jungmi
Oh, Miae
Kim, Kahye
Cha, Wonseok
Kim, Jaeuk U.
author_facet Doan, Dieu Ni Thi
Ku, Boncho
Choi, Jungmi
Oh, Miae
Kim, Kahye
Cha, Wonseok
Kim, Jaeuk U.
author_sort Doan, Dieu Ni Thi
collection PubMed
description Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia. Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms. Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy. Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores. Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.
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spelling pubmed-80779682021-04-28 Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential Doan, Dieu Ni Thi Ku, Boncho Choi, Jungmi Oh, Miae Kim, Kahye Cha, Wonseok Kim, Jaeuk U. Front Aging Neurosci Neuroscience Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia. Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms. Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy. Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores. Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE. Frontiers Media S.A. 2021-04-13 /pmc/articles/PMC8077968/ /pubmed/33927610 http://dx.doi.org/10.3389/fnagi.2021.659817 Text en Copyright © 2021 Doan, Ku, Choi, Oh, Kim, Cha and Kim. 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
Doan, Dieu Ni Thi
Ku, Boncho
Choi, Jungmi
Oh, Miae
Kim, Kahye
Cha, Wonseok
Kim, Jaeuk U.
Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title_full Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title_fullStr Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title_full_unstemmed Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title_short Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential
title_sort predicting dementia with prefrontal electroencephalography and event-related potential
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077968/
https://www.ncbi.nlm.nih.gov/pubmed/33927610
http://dx.doi.org/10.3389/fnagi.2021.659817
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