Early detection of Parkinson&apos

[EN] 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 osc...

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Autores: Giménez-Aparisi, Guillem, Chornet-Lurbe, A., Diaz-Roman, M., Hao, Dongmei, Li, Guangfei, Guijarro Estelles, Enrique|||0000-0003-0606-2352, Ye Lin, Yiyao|||0000-0003-2929-181X
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/230500
Acesso em linha:https://riunet.upv.es/handle/10251/230500
Access Level:Acceso aberto
Palavra-chave:Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
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network_name_str España
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dc.title.none.fl_str_mv Early detection of Parkinson&apos
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&apos
spellingShingle Early detection of Parkinson&apos
Giménez-Aparisi, Guillem
Resting state electroencephalography
Parkinson
Eyes influence
Beta burst
Phase amplitude coupling
Dynamic features
title_short Early detection of Parkinson&apos
title_full Early detection of Parkinson&apos
title_fullStr Early detection of Parkinson&apos
title_full_unstemmed Early detection of Parkinson&apos
title_sort Early detection of Parkinson&apos
dc.creator.none.fl_str_mv Giménez-Aparisi, Guillem
Chornet-Lurbe, A.
Diaz-Roman, M.
Hao, Dongmei
Li, Guangfei
Guijarro Estelles, Enrique|||0000-0003-0606-2352
Ye Lin, Yiyao|||0000-0003-2929-181X
author Giménez-Aparisi, Guillem
author_facet Giménez-Aparisi, Guillem
Chornet-Lurbe, A.
Diaz-Roman, M.
Hao, Dongmei
Li, Guangfei
Guijarro Estelles, Enrique|||0000-0003-0606-2352
Ye Lin, Yiyao|||0000-0003-2929-181X
author_role author
author2 Chornet-Lurbe, A.
Diaz-Roman, M.
Hao, Dongmei
Li, Guangfei
Guijarro Estelles, Enrique|||0000-0003-0606-2352
Ye Lin, Yiyao|||0000-0003-2929-181X
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Electrónica
Escuela Técnica Superior de Ingeniería Industrial
Centro de Investigación e Innovación en Bioingeniería
Generalitat Valenciana
Universitat Politècnica de València
National Natural Science Foundation of China
Repositorio Institucional de la Universitat Politècnica de València Riunet
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 [EN] 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
2025-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/230500
url https://riunet.upv.es/handle/10251/230500
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Generalitat Valenciana https://doi.org/10.13039/501100003359 INVEST%2F2022%2F67 FORMACION EN ANALISIS DE DATOS E INTELIGENCIA ARTIFICIAL EN SALUD
Universitat Politècnica de València https://doi.org/10.13039/501100004233 PI2023-01 Inteligencia artificial para la predicción de la demencia en los pacientes con deterioro cognitivo l
National Natural Science Foundation of China https://doi.org/10.13039/501100001809 12402350
National Natural Science Foundation of China https://doi.org/10.13039/501100001809 H20240220
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Early detection of Parkinson&aposs disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyesGiménez-Aparisi, GuillemChornet-Lurbe, A.Diaz-Roman, M.Hao, DongmeiLi, GuangfeiGuijarro Estelles, Enrique|||0000-0003-0606-2352Ye Lin, Yiyao|||0000-0003-2929-181XResting state electroencephalographyParkinsonEyes influenceBeta burstPhase amplitude couplingDynamic features[EN] 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.This work was supported by the European Union-NextGenerationEU under the Investigo Program (INVEST/2022/67) , Polisabio (PI2023-01) , the Introduction and Cultivation program 2.0 of Beijing University of Technology (2024) , National Natural Science Foundation of China (NSFC) (12402350) , and the National Foreign Experts Program 2024 (H20240220) .ElsevierDepartamento de Ingeniería ElectrónicaEscuela Técnica Superior de Ingeniería IndustrialCentro de Investigación e Innovación en BioingenieríaGeneralitat ValencianaUniversitat Politècnica de ValènciaNational Natural Science Foundation of ChinaRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/230500reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengGeneralitat Valenciana https://doi.org/10.13039/501100003359 INVEST%2F2022%2F67 FORMACION EN ANALISIS DE DATOS E INTELIGENCIA ARTIFICIAL EN SALUDUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PI2023-01 Inteligencia artificial para la predicción de la demencia en los pacientes con deterioro cognitivo lNational Natural Science Foundation of China https://doi.org/10.13039/501100001809 12402350National Natural Science Foundation of China https://doi.org/10.13039/501100001809 H20240220open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2305002026-06-13T07:49:27Z
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