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 audi...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 13 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Frontiers Media S.A.
4/13/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150300123&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150300123 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 4/13/2021 vid: 13 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 150300123 10.3389/fnagi.2021.659817 150300123 ppct: 19 formats: tig: atl: Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential. aug: au: Doan, Dieu Ni Thi Ku, Boncho Choi, Jungmi Oh, Miae Kim, Kahye Cha, Wonseok Kim, Jaeuk U. affil: Korea Institute of Oriental Medicine, Daejeon, South Korea sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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