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An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features

OBJECTIVE: Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) fu...

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Autores principales: Ren, Zhe, Zhao, Yibo, Han, Xiong, Yue, Mengyan, Wang, Bin, Zhao, Zongya, Wen, Bin, Hong, Yang, Wang, Qi, Hong, Yingxing, Zhao, Ting, Wang, Na, Zhao, Pan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878185/
https://www.ncbi.nlm.nih.gov/pubmed/36711136
http://dx.doi.org/10.3389/fnins.2022.1060814
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author Ren, Zhe
Zhao, Yibo
Han, Xiong
Yue, Mengyan
Wang, Bin
Zhao, Zongya
Wen, Bin
Hong, Yang
Wang, Qi
Hong, Yingxing
Zhao, Ting
Wang, Na
Zhao, Pan
author_facet Ren, Zhe
Zhao, Yibo
Han, Xiong
Yue, Mengyan
Wang, Bin
Zhao, Zongya
Wen, Bin
Hong, Yang
Wang, Qi
Hong, Yingxing
Zhao, Ting
Wang, Na
Zhao, Pan
author_sort Ren, Zhe
collection PubMed
description OBJECTIVE: Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) functional connectivity features of the electroencephalogram (EEG). METHODS: PWEs who met the inclusion and exclusion criteria were divided into a cognitively normal (CON) group (n = 55) and a CI group (n = 76). The 23 clinical features and 684 PLV(EEG) features at the time of patient visit were screened and ranked using the Fisher score. Adaptive Boosting (AdaBoost) and Gradient Boosting Decision Tree (GBDT) were used as algorithms to construct diagnostic models of CI in PWEs either with pure clinical features, pure PLV(EEG) features, or combined clinical and PLV(EEG) features. The performance of these models was assessed using a five-fold cross-validation method. RESULTS: GBDT-built model with combined clinical and PLV(EEG) features performed the best with accuracy, precision, recall, F1-score, and an area under the curve (AUC) of 90.11, 93.40, 89.50, 91.39, and 0.95%. The top 5 features found to influence the model performance based on the Fisher scores were the magnetic resonance imaging (MRI) findings of the head for abnormalities, educational attainment, PLV(EEG) in the beta (β)-band C3-F4, seizure frequency, and PLV(EEG) in theta (θ)-band Fp1-Fz. A total of 12 of the top 5% of features exhibited statistically different PLV(EEG) features, while eight of which were PLV(EEG) features in the θ band. CONCLUSION: The model constructed from the combined clinical and PLV(EEG) features could effectively identify CI in PWEs and possess the potential as a useful objective evaluation method. The PLV(EEG) in the θ band could be a potential biomarker for the complementary diagnosis of CI comorbid with epilepsy.
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spelling pubmed-98781852023-01-27 An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features Ren, Zhe Zhao, Yibo Han, Xiong Yue, Mengyan Wang, Bin Zhao, Zongya Wen, Bin Hong, Yang Wang, Qi Hong, Yingxing Zhao, Ting Wang, Na Zhao, Pan Front Neurosci Neuroscience OBJECTIVE: Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) functional connectivity features of the electroencephalogram (EEG). METHODS: PWEs who met the inclusion and exclusion criteria were divided into a cognitively normal (CON) group (n = 55) and a CI group (n = 76). The 23 clinical features and 684 PLV(EEG) features at the time of patient visit were screened and ranked using the Fisher score. Adaptive Boosting (AdaBoost) and Gradient Boosting Decision Tree (GBDT) were used as algorithms to construct diagnostic models of CI in PWEs either with pure clinical features, pure PLV(EEG) features, or combined clinical and PLV(EEG) features. The performance of these models was assessed using a five-fold cross-validation method. RESULTS: GBDT-built model with combined clinical and PLV(EEG) features performed the best with accuracy, precision, recall, F1-score, and an area under the curve (AUC) of 90.11, 93.40, 89.50, 91.39, and 0.95%. The top 5 features found to influence the model performance based on the Fisher scores were the magnetic resonance imaging (MRI) findings of the head for abnormalities, educational attainment, PLV(EEG) in the beta (β)-band C3-F4, seizure frequency, and PLV(EEG) in theta (θ)-band Fp1-Fz. A total of 12 of the top 5% of features exhibited statistically different PLV(EEG) features, while eight of which were PLV(EEG) features in the θ band. CONCLUSION: The model constructed from the combined clinical and PLV(EEG) features could effectively identify CI in PWEs and possess the potential as a useful objective evaluation method. The PLV(EEG) in the θ band could be a potential biomarker for the complementary diagnosis of CI comorbid with epilepsy. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9878185/ /pubmed/36711136 http://dx.doi.org/10.3389/fnins.2022.1060814 Text en Copyright © 2023 Ren, Zhao, Han, Yue, Wang, Zhao, Wen, Hong, Wang, Hong, Zhao, Wang and Zhao. 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
Ren, Zhe
Zhao, Yibo
Han, Xiong
Yue, Mengyan
Wang, Bin
Zhao, Zongya
Wen, Bin
Hong, Yang
Wang, Qi
Hong, Yingxing
Zhao, Ting
Wang, Na
Zhao, Pan
An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title_full An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title_fullStr An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title_full_unstemmed An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title_short An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features
title_sort objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-eeg functional connectivity features
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878185/
https://www.ncbi.nlm.nih.gov/pubmed/36711136
http://dx.doi.org/10.3389/fnins.2022.1060814
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