Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes

Resting state electroencephalography (EEG) has been shown to provide relevant information for detecting neuropathological changes of the brain's electrical activity in neurodegenerative patients. Studies conducted on local field potential recordings have shown that exaggerated beta oscillations...

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Autores: Gimenez-Aparisi, G, Guijarro-Estelles, E, Chornet-Lurbe, A, Diaz-Roman, M, Hao, DM, Li, GF, Ye-Lin, Y
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Repositorio:r-FISABIO. Repositorio Institucional de Producción Científica
OAI Identifier:oai:fisabio.fundanetsuite.com:p18643
Acceso en línea:https://fisabio.portalinvestigacion.com/publicaciones/18643
Access Level:acceso abierto
Palabra clave:Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
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spelling Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyesGimenez-Aparisi, GGuijarro-Estelles, EChornet-Lurbe, ADiaz-Roman, MHao, DMLi, GFYe-Lin, YResting state electroencephalographyParkinsonEyes influenceBeta burstPhase amplitude couplingDynamic featuresResting state electroencephalography (EEG) has been shown to provide relevant information for detecting neuropathological changes of the brain's electrical activity in neurodegenerative patients. Studies conducted on local field potential recordings have shown that exaggerated beta oscillations and abnormally high beta-gamma phase amplitude coupling (PAC) are hallmark Parkinson's disease (PD) signatures. Extracting beta bursts from non-invasive magnetoencephalography has also been found to be feasible, as it provides a better signal-to-noise ratio than electroencephalography and is less affected by volume conduction. It is still unclear whether beta burst dynamic features and beta-gamma PAC from resting state EEG can be used to assess the progress of PD. In the present study, it has been proposed to assess the potential utility of beta burst dynamic and the beta-gamma PAC to discriminate PD patients from healthy subjects, as well as their relationship with clinical symptoms. Resting state EEG data have been analysed in both eyes closed (EC) and open (EO) and reactivity-to-eyes opening (REO) of a public database consisting of 20 healthy and 13 Parkinson patients. Beta burst events from EEG spectrograms were extracted to determine their dynamic features, i.e. burst duration, rate, peak frequency, spectral bandwidth and power as well as the normalized beta-gamma PAC. Permutation test while controlling the family-wise error rate was used to assess statistical significance. The results indicate that REO is more sensitive than EC and EO alone, and also that the higher variability of burst duration is linked to PD, while the lower burst rate is negatively correlated with clinical symptoms. PD patients had a higher periodicity of duration in the left frontal area, and a higher periodicity of peak frequency, spectral bandwidth and power of the bursts in the left central area than healthy subjects, together with a significant positive correlation with clinical symptoms. Beta-gamma PAC not only found abnormalities in the central regions but also in the frontal, fronto-central, parietal and occipital regions, suggesting impaired motor, working memory and visuospatial skills. It was also possible to extract beta burst dynamic features and the beta-gamma PAC from resting state EEG and that these provided reliable PD progress biomarkers. These advances are expected to help clinicians design patientpersonalised therapies and improve their quality of life.ELSEVIER SCI LTD2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fisabio.portalinvestigacion.com/publicaciones/18643Biomedical Signal Processing and ControlISSN: 17468094ISSNe: 17468108reponame:r-FISABIO. Repositorio Institucional de Producción Científicainstname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)Inglésinfo:eu-repo/semantics/openAccessoai:fisabio.fundanetsuite.com:p186432026-06-11T12:45:17Z
dc.title.none.fl_str_mv Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
title Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
spellingShingle Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
Gimenez-Aparisi, G
Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
title_short Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
title_full Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
title_fullStr Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
title_full_unstemmed Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
title_sort Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes
dc.creator.none.fl_str_mv Gimenez-Aparisi, G
Guijarro-Estelles, E
Chornet-Lurbe, A
Diaz-Roman, M
Hao, DM
Li, GF
Ye-Lin, Y
author Gimenez-Aparisi, G
author_facet Gimenez-Aparisi, G
Guijarro-Estelles, E
Chornet-Lurbe, A
Diaz-Roman, M
Hao, DM
Li, GF
Ye-Lin, Y
author_role author
author2 Guijarro-Estelles, E
Chornet-Lurbe, A
Diaz-Roman, M
Hao, DM
Li, GF
Ye-Lin, Y
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
topic Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
description Resting state electroencephalography (EEG) has been shown to provide relevant information for detecting neuropathological changes of the brain's electrical activity in neurodegenerative patients. Studies conducted on local field potential recordings have shown that exaggerated beta oscillations and abnormally high beta-gamma phase amplitude coupling (PAC) are hallmark Parkinson's disease (PD) signatures. Extracting beta bursts from non-invasive magnetoencephalography has also been found to be feasible, as it provides a better signal-to-noise ratio than electroencephalography and is less affected by volume conduction. It is still unclear whether beta burst dynamic features and beta-gamma PAC from resting state EEG can be used to assess the progress of PD. In the present study, it has been proposed to assess the potential utility of beta burst dynamic and the beta-gamma PAC to discriminate PD patients from healthy subjects, as well as their relationship with clinical symptoms. Resting state EEG data have been analysed in both eyes closed (EC) and open (EO) and reactivity-to-eyes opening (REO) of a public database consisting of 20 healthy and 13 Parkinson patients. Beta burst events from EEG spectrograms were extracted to determine their dynamic features, i.e. burst duration, rate, peak frequency, spectral bandwidth and power as well as the normalized beta-gamma PAC. Permutation test while controlling the family-wise error rate was used to assess statistical significance. The results indicate that REO is more sensitive than EC and EO alone, and also that the higher variability of burst duration is linked to PD, while the lower burst rate is negatively correlated with clinical symptoms. PD patients had a higher periodicity of duration in the left frontal area, and a higher periodicity of peak frequency, spectral bandwidth and power of the bursts in the left central area than healthy subjects, together with a significant positive correlation with clinical symptoms. Beta-gamma PAC not only found abnormalities in the central regions but also in the frontal, fronto-central, parietal and occipital regions, suggesting impaired motor, working memory and visuospatial skills. It was also possible to extract beta burst dynamic features and the beta-gamma PAC from resting state EEG and that these provided reliable PD progress biomarkers. These advances are expected to help clinicians design patientpersonalised therapies and improve their quality of life.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fisabio.portalinvestigacion.com/publicaciones/18643
url https://fisabio.portalinvestigacion.com/publicaciones/18643
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER SCI LTD
publisher.none.fl_str_mv ELSEVIER SCI LTD
dc.source.none.fl_str_mv Biomedical Signal Processing and Control
ISSN: 17468094
ISSNe: 17468108
reponame:r-FISABIO. Repositorio Institucional de Producción Científica
instname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
instname_str Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
reponame_str r-FISABIO. Repositorio Institucional de Producción Científica
collection r-FISABIO. Repositorio Institucional de Producción Científica
repository.name.fl_str_mv
repository.mail.fl_str_mv
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