EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease
Background Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related co...
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| Veröffentlicht in: | The Egyptian Journal of Neurology, Psychiatry and Neurosurgery Psychiatry and Neurosurgery, 2023-04, Vol.59 (1), p.54-9, Article 54 |
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| container_title | The Egyptian Journal of Neurology, Psychiatry and Neurosurgery |
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| creator | Elkholy, Mostafa M. Aboubakr, Hossam H. Abd ElMonem, Noha A. Soliman, Rasha H. Masoud, Mohammed M. |
| description | Background
Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA).
Results
PD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA |
| doi_str_mv | 10.1186/s41983-023-00656-0 |
| format | Article |
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Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA).
Results
PD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA < 26) had significantly lower global relative power, more marked in alpha and beta frequency bands compared to PD patients with normal cognition. Alpha and beta relative power in frontal and temporal regions showed significant correlation with different cognitive domains of MoCA score.
Conclusions
QEEG measures especially spectral relative power could be used as adjunct to neuropsychological assessment in evaluation of PD-related cognitive decline.</description><identifier>ISSN: 1687-8329</identifier><identifier>ISSN: 1110-1083</identifier><identifier>EISSN: 1687-8329</identifier><identifier>DOI: 10.1186/s41983-023-00656-0</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biomarkers ; Cognition & reasoning ; Cognitive decline ; Connectivity analysis ; Medicine ; Medicine & Public Health ; Neurology ; Neuropsychology ; Neurosurgery ; Parkinson's disease ; Psychiatry ; QEEG ; Spectral analysis</subject><ispartof>The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 2023-04, Vol.59 (1), p.54-9, Article 54</ispartof><rights>The Author(s) 2023</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c380t-f89ce226d13b7d29ad784b391ed20908a2d7d13d63a5aab0d5c5c4fc73eda4c33</cites><orcidid>0000-0002-4954-5883</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Elkholy, Mostafa M.</creatorcontrib><creatorcontrib>Aboubakr, Hossam H.</creatorcontrib><creatorcontrib>Abd ElMonem, Noha A.</creatorcontrib><creatorcontrib>Soliman, Rasha H.</creatorcontrib><creatorcontrib>Masoud, Mohammed M.</creatorcontrib><title>EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease</title><title>The Egyptian Journal of Neurology, Psychiatry and Neurosurgery</title><addtitle>Egypt J Neurol Psychiatry Neurosurg</addtitle><description>Background
Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA).
Results
PD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA < 26) had significantly lower global relative power, more marked in alpha and beta frequency bands compared to PD patients with normal cognition. Alpha and beta relative power in frontal and temporal regions showed significant correlation with different cognitive domains of MoCA score.
Conclusions
QEEG measures especially spectral relative power could be used as adjunct to neuropsychological assessment in evaluation of PD-related cognitive decline.</description><subject>Biomarkers</subject><subject>Cognition & reasoning</subject><subject>Cognitive decline</subject><subject>Connectivity analysis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neurology</subject><subject>Neuropsychology</subject><subject>Neurosurgery</subject><subject>Parkinson's disease</subject><subject>Psychiatry</subject><subject>QEEG</subject><subject>Spectral analysis</subject><issn>1687-8329</issn><issn>1110-1083</issn><issn>1687-8329</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNp9UUtLAzEQXkTBUvsHPC14Xs1jN4-jlFoLBT3Yc5hNsiW1TWqyFfrvTbuinjwMM5nvkYGvKG4xusdYsIdUYylohUguxBpWoYtihJnglaBEXv6Zr4tJSq5FNcEYcYlHxWo2m5dpb3UfYVuCN6UO3uen-3T9sdxDhJ3tbUwlpAytvcuILVsXdhDfT3vny9c8Op-CL41LFpK9Ka462CY7-e7jYvU0e5s-V8uX-WL6uKw0FaivOiG1JYQZTFtuiATDRd1Sia0hSCIBxPCMGUahAWiRaXSj605zag3UmtJxsRh8TYCN2keXjzqqAE6dFyGuFcTe6a1VLdXCNgIEQ6KmNZddC13dCoYxWINJ9robvPYxfBxs6tUmHKLP5ysiUMM5kazJLDKwdAwpRdv9_IqROqWhhjRUTkOd01Aoi-ggSpns1zb-Wv-j-gKtMY2c</recordid><startdate>20230425</startdate><enddate>20230425</enddate><creator>Elkholy, Mostafa M.</creator><creator>Aboubakr, Hossam H.</creator><creator>Abd ElMonem, Noha A.</creator><creator>Soliman, Rasha H.</creator><creator>Masoud, Mohammed M.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>SpringerOpen</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88G</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2M</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4954-5883</orcidid></search><sort><creationdate>20230425</creationdate><title>EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease</title><author>Elkholy, Mostafa M. ; Aboubakr, Hossam H. ; Abd ElMonem, Noha A. ; Soliman, Rasha H. ; Masoud, Mohammed M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-f89ce226d13b7d29ad784b391ed20908a2d7d13d63a5aab0d5c5c4fc73eda4c33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomarkers</topic><topic>Cognition & reasoning</topic><topic>Cognitive decline</topic><topic>Connectivity analysis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neurology</topic><topic>Neuropsychology</topic><topic>Neurosurgery</topic><topic>Parkinson's disease</topic><topic>Psychiatry</topic><topic>QEEG</topic><topic>Spectral analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Elkholy, Mostafa M.</creatorcontrib><creatorcontrib>Aboubakr, Hossam H.</creatorcontrib><creatorcontrib>Abd ElMonem, Noha A.</creatorcontrib><creatorcontrib>Soliman, Rasha H.</creatorcontrib><creatorcontrib>Masoud, Mohammed M.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Psychology Database (Alumni)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Psychology Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>The Egyptian Journal of Neurology, Psychiatry and Neurosurgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Elkholy, Mostafa M.</au><au>Aboubakr, Hossam H.</au><au>Abd ElMonem, Noha A.</au><au>Soliman, Rasha H.</au><au>Masoud, Mohammed M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease</atitle><jtitle>The Egyptian Journal of Neurology, Psychiatry and Neurosurgery</jtitle><stitle>Egypt J Neurol Psychiatry Neurosurg</stitle><date>2023-04-25</date><risdate>2023</risdate><volume>59</volume><issue>1</issue><spage>54</spage><epage>9</epage><pages>54-9</pages><artnum>54</artnum><issn>1687-8329</issn><issn>1110-1083</issn><eissn>1687-8329</eissn><abstract>Background
Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA).
Results
PD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA < 26) had significantly lower global relative power, more marked in alpha and beta frequency bands compared to PD patients with normal cognition. Alpha and beta relative power in frontal and temporal regions showed significant correlation with different cognitive domains of MoCA score.
Conclusions
QEEG measures especially spectral relative power could be used as adjunct to neuropsychological assessment in evaluation of PD-related cognitive decline.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1186/s41983-023-00656-0</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4954-5883</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biomarkers Cognition & reasoning Cognitive decline Connectivity analysis Medicine Medicine & Public Health Neurology Neuropsychology Neurosurgery Parkinson's disease Psychiatry QEEG Spectral analysis |
| title | EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease |
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