Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients
The aim of the present study was to analyze resting-state brain activity in patients with Parkinson’s disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreate...
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| Veröffentlicht in: | Annals of biomedical engineering 2011-12, Vol.39 (12), p.2935-2944 |
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| creator | Gómez, Carlos Olde Dubbelink, Kim T. E. Stam, Cornelis J. Abásolo, Daniel Berendse, Henk W. Hornero, Roberto |
| description | The aim of the present study was to analyze resting-state brain activity in patients with Parkinson’s disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel–Ziv complexity (
LZC
), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects’ recordings. We found significant group differences (
p
-values |
| doi_str_mv | 10.1007/s10439-011-0416-0 |
| format | Article |
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LZC
), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects’ recordings. We found significant group differences (
p
-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of
LZC
results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed.
LZC
results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of
LZC
to detect an abnormal type of dynamics associated with PD.</description><identifier>ISSN: 0090-6964</identifier><identifier>EISSN: 1573-9686</identifier><identifier>DOI: 10.1007/s10439-011-0416-0</identifier><identifier>PMID: 21969108</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Accuracy ; Aged ; Biochemistry ; Biological and Medical Physics ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Brain ; Cerebral Cortex - physiopathology ; Classical Mechanics ; Classification ; Complexity ; Conversion ; Discriminant Analysis ; Female ; Humans ; Magnetoencephalography - methods ; Male ; Middle Aged ; Parkinson Disease - classification ; Parkinson Disease - physiopathology ; Parkinson's disease ; Patients ; ROC Curve ; Sensitivity and Specificity ; Temporal logic</subject><ispartof>Annals of biomedical engineering, 2011-12, Vol.39 (12), p.2935-2944</ispartof><rights>Biomedical Engineering Society 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c478t-6971c9260652ad47231ef418f4bc8fcb35c1888375196b10a2cae4160af4aa2e3</citedby><cites>FETCH-LOGICAL-c478t-6971c9260652ad47231ef418f4bc8fcb35c1888375196b10a2cae4160af4aa2e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10439-011-0416-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10439-011-0416-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21969108$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gómez, Carlos</creatorcontrib><creatorcontrib>Olde Dubbelink, Kim T. E.</creatorcontrib><creatorcontrib>Stam, Cornelis J.</creatorcontrib><creatorcontrib>Abásolo, Daniel</creatorcontrib><creatorcontrib>Berendse, Henk W.</creatorcontrib><creatorcontrib>Hornero, Roberto</creatorcontrib><title>Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients</title><title>Annals of biomedical engineering</title><addtitle>Ann Biomed Eng</addtitle><addtitle>Ann Biomed Eng</addtitle><description>The aim of the present study was to analyze resting-state brain activity in patients with Parkinson’s disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel–Ziv complexity (
LZC
), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects’ recordings. We found significant group differences (
p
-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of
LZC
results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed.
LZC
results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of
LZC
to detect an abnormal type of dynamics associated with PD.</description><subject>Accuracy</subject><subject>Aged</subject><subject>Biochemistry</subject><subject>Biological and Medical Physics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Biophysics</subject><subject>Brain</subject><subject>Cerebral Cortex - physiopathology</subject><subject>Classical Mechanics</subject><subject>Classification</subject><subject>Complexity</subject><subject>Conversion</subject><subject>Discriminant Analysis</subject><subject>Female</subject><subject>Humans</subject><subject>Magnetoencephalography - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Parkinson Disease - classification</subject><subject>Parkinson Disease - physiopathology</subject><subject>Parkinson's disease</subject><subject>Patients</subject><subject>ROC Curve</subject><subject>Sensitivity and Specificity</subject><subject>Temporal logic</subject><issn>0090-6964</issn><issn>1573-9686</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkctuEzEUhi1ERUPbB2CDRmzoZso5tsdjL6OQXqQiUC9ry3E9kctkJvhMENn1NXg9ngSP0oKEBKws-Xz-_Ns_Y68QThCgfkcIUpgSEEuQqEp4xiZY1aI0SqvnbAJgoFRGyX32kugeMqhF9YLtczTKIOgJs7N-tW7Dtzhsi2nn2i1FKvqmuAo0xG5ZXg9uCMWH-Vkx9UP8OmKxK-YutdtxtgzFJ5c-x4767sfDdyreRwqOxt0hhm6gQ7bXuJbC0eN6wG5P5zez8_Ly49nFbHpZelnrIYes0RuuQFXc3cmaCwyNRN3IhdeNX4jKo9Za1FVOvkBw3LuQnwyukc7xIA7Y2513nfovmxzeriL50LauC_2GbHZrw3mF_yeBC1FLlJk8_ieJqkZhJFaQ0Td_oPf9JuX_HH2YbcaoDOEO8qknSqGx6xRXLm0tgh0LtbtCbe7JjoXaUfz6UbxZrMLdrxNPDWaA7wDKo24Z0u-b_279CZHpqd4</recordid><startdate>20111201</startdate><enddate>20111201</enddate><creator>Gómez, Carlos</creator><creator>Olde Dubbelink, Kim T. 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E.</au><au>Stam, Cornelis J.</au><au>Abásolo, Daniel</au><au>Berendse, Henk W.</au><au>Hornero, Roberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients</atitle><jtitle>Annals of biomedical engineering</jtitle><stitle>Ann Biomed Eng</stitle><addtitle>Ann Biomed Eng</addtitle><date>2011-12-01</date><risdate>2011</risdate><volume>39</volume><issue>12</issue><spage>2935</spage><epage>2944</epage><pages>2935-2944</pages><issn>0090-6964</issn><eissn>1573-9686</eissn><abstract>The aim of the present study was to analyze resting-state brain activity in patients with Parkinson’s disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel–Ziv complexity (
LZC
), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects’ recordings. We found significant group differences (
p
-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of
LZC
results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed.
LZC
results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of
LZC
to detect an abnormal type of dynamics associated with PD.</abstract><cop>Boston</cop><pub>Springer US</pub><pmid>21969108</pmid><doi>10.1007/s10439-011-0416-0</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Accuracy Aged Biochemistry Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Brain Cerebral Cortex - physiopathology Classical Mechanics Classification Complexity Conversion Discriminant Analysis Female Humans Magnetoencephalography - methods Male Middle Aged Parkinson Disease - classification Parkinson Disease - physiopathology Parkinson's disease Patients ROC Curve Sensitivity and Specificity Temporal logic |
| title | Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson’s Disease Patients |
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