A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patient...

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Autores principales:	Majd Abazid, Nesma Houmani, Jerome Boudy, Bernadette Dorizzi, Jean Mariani, Kiyoka Kinugawa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG signals graph theory brain network epoch-based entropy phase lag index coherence Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/2450386c292a42efb9d927022d59909c
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spelling	oai:doaj.org-article:2450386c292a42efb9d927022d59909c2021-11-25T17:30:57ZA Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG10.3390/e231115531099-4300https://doaj.org/article/2450386c292a42efb9d927022d59909c2021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1553https://doaj.org/toc/1099-4300This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.Majd AbazidNesma HoumaniJerome BoudyBernadette DorizziJean MarianiKiyoka KinugawaMDPI AGarticleEEG signalsgraph theorybrain networkepoch-based entropyphase lag indexcoherenceScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1553, p 1553 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG signals graph theory brain network epoch-based entropy phase lag index coherence Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	EEG signals graph theory brain network epoch-based entropy phase lag index coherence Science Q Astrophysics QB460-466 Physics QC1-999 Majd Abazid Nesma Houmani Jerome Boudy Bernadette Dorizzi Jean Mariani Kiyoka Kinugawa A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
description	This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.
format	article
author	Majd Abazid Nesma Houmani Jerome Boudy Bernadette Dorizzi Jean Mariani Kiyoka Kinugawa
author_facet	Majd Abazid Nesma Houmani Jerome Boudy Bernadette Dorizzi Jean Mariani Kiyoka Kinugawa
author_sort	Majd Abazid
title	A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
title_short	A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
title_full	A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
title_fullStr	A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
title_full_unstemmed	A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
title_sort	comparative study of functional connectivity measures for brain network analysis in the context of ad detection with eeg
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2450386c292a42efb9d927022d59909c
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A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

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