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Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram
Objectives To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI. Methods Retrosp...
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Published in: | European radiology 2021-10, Vol.31 (10), p.7386-7394 |
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creator | Zhang, Jiahui Gao, Yuyuan He, Xuetao Feng, Shujun Hu, Jinlong Zhang, Qingxi Zhao, Jiehao Huang, Zhiheng Wang, Limin Ma, Guixian Zhang, Yuhu Nie, Kun Wang, Lijuan |
description | Objectives
To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI.
Methods
Retrospective analysis of patients with PD identified 36 PD-MCI and 35 PD with normal cognition (PD-NC). QEEG features of power spectrum and structural MR features of cortex based on surface-based morphometry (SBM) were extracted. Support vector machine (SVM) was established using combined features of structural MR and qEEG to identify PD-MCI. Feature importance evaluation algorithm of mean impact value (MIV) was established to sort the vital characteristics of qEEG and structural MR.
Results
Compared with PD-NC, PD-MCI showed a statistically significant difference in 5 leads and waves of qEEG and 7 cortical region features of structural MR. The SVM model based on these qEEG and structural MR features yielded an accuracy of 0.80 in the training set and had a high prediction accuracy of 0.80 in the test set (sensitivity was 0.78, specificity was 0.83, area under the receiver operating characteristic curve was 0.77), which was higher than the model built by the feature separately. QEEG features of theta wave in C3 had a marked impact on the model for classification according to the MIV algorithm.
Conclusions
PD-MCI is characterized by widespread structural and EEG abnormality. “Composite markers” could be valuable for the individualized diagnosis of PD-MCI by machine learning.
Key Points
• Explore the brain abnormalities in Parkinson’s disease with mild cognitive impairment by using the quantitative electroencephalogram and cortex structural MR simultaneously.
• Multimodal features based support vector machine for identifying Parkinson’s disease with mild cognitive impairment has an acceptable performance.
• Theta wave in C3 is the most influential feature of qEEG and cortex structure MR imaging in identifying Parkinson’s disease with mild cognitive impairment using support vector machine. |
doi_str_mv | 10.1007/s00330-020-07575-1 |
format | article |
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To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI.
Methods
Retrospective analysis of patients with PD identified 36 PD-MCI and 35 PD with normal cognition (PD-NC). QEEG features of power spectrum and structural MR features of cortex based on surface-based morphometry (SBM) were extracted. Support vector machine (SVM) was established using combined features of structural MR and qEEG to identify PD-MCI. Feature importance evaluation algorithm of mean impact value (MIV) was established to sort the vital characteristics of qEEG and structural MR.
Results
Compared with PD-NC, PD-MCI showed a statistically significant difference in 5 leads and waves of qEEG and 7 cortical region features of structural MR. The SVM model based on these qEEG and structural MR features yielded an accuracy of 0.80 in the training set and had a high prediction accuracy of 0.80 in the test set (sensitivity was 0.78, specificity was 0.83, area under the receiver operating characteristic curve was 0.77), which was higher than the model built by the feature separately. QEEG features of theta wave in C3 had a marked impact on the model for classification according to the MIV algorithm.
Conclusions
PD-MCI is characterized by widespread structural and EEG abnormality. “Composite markers” could be valuable for the individualized diagnosis of PD-MCI by machine learning.
Key Points
• Explore the brain abnormalities in Parkinson’s disease with mild cognitive impairment by using the quantitative electroencephalogram and cortex structural MR simultaneously.
• Multimodal features based support vector machine for identifying Parkinson’s disease with mild cognitive impairment has an acceptable performance.
• Theta wave in C3 is the most influential feature of qEEG and cortex structure MR imaging in identifying Parkinson’s disease with mild cognitive impairment using support vector machine.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07575-1</identifier><identifier>PMID: 33389038</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abnormalities ; Algorithms ; Cognition ; Cognitive ability ; Cognitive Dysfunction - diagnostic imaging ; Cortex ; Diagnostic Radiology ; EEG ; Electroencephalography ; Feature extraction ; Humans ; Imaging ; Impairment ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Machine learning ; Magnetic Resonance Imaging ; Markers ; Medical imaging ; Medicine ; Medicine & Public Health ; Morphometry ; Movement disorders ; Neuro ; Neurodegenerative diseases ; Neuroimaging ; Neuroradiology ; Parkinson Disease - complications ; Parkinson Disease - diagnostic imaging ; Parkinson's disease ; Radiology ; Retrospective Studies ; Sensitivity analysis ; Statistical analysis ; Support vector machines ; Ultrasound</subject><ispartof>European radiology, 2021-10, Vol.31 (10), p.7386-7394</ispartof><rights>European Society of Radiology 2021. corrected publication 2021</rights><rights>2021. European Society of Radiology.</rights><rights>European Society of Radiology 2021. corrected publication 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-9dec5c8a78a7814097acb16b21032cb61ff9b25f54c17888797e998d4a6c8fd53</citedby><cites>FETCH-LOGICAL-c375t-9dec5c8a78a7814097acb16b21032cb61ff9b25f54c17888797e998d4a6c8fd53</cites><orcidid>0000-0003-1727-2670</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33389038$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Jiahui</creatorcontrib><creatorcontrib>Gao, Yuyuan</creatorcontrib><creatorcontrib>He, Xuetao</creatorcontrib><creatorcontrib>Feng, Shujun</creatorcontrib><creatorcontrib>Hu, Jinlong</creatorcontrib><creatorcontrib>Zhang, Qingxi</creatorcontrib><creatorcontrib>Zhao, Jiehao</creatorcontrib><creatorcontrib>Huang, Zhiheng</creatorcontrib><creatorcontrib>Wang, Limin</creatorcontrib><creatorcontrib>Ma, Guixian</creatorcontrib><creatorcontrib>Zhang, Yuhu</creatorcontrib><creatorcontrib>Nie, Kun</creatorcontrib><creatorcontrib>Wang, Lijuan</creatorcontrib><title>Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI.
Methods
Retrospective analysis of patients with PD identified 36 PD-MCI and 35 PD with normal cognition (PD-NC). QEEG features of power spectrum and structural MR features of cortex based on surface-based morphometry (SBM) were extracted. Support vector machine (SVM) was established using combined features of structural MR and qEEG to identify PD-MCI. Feature importance evaluation algorithm of mean impact value (MIV) was established to sort the vital characteristics of qEEG and structural MR.
Results
Compared with PD-NC, PD-MCI showed a statistically significant difference in 5 leads and waves of qEEG and 7 cortical region features of structural MR. The SVM model based on these qEEG and structural MR features yielded an accuracy of 0.80 in the training set and had a high prediction accuracy of 0.80 in the test set (sensitivity was 0.78, specificity was 0.83, area under the receiver operating characteristic curve was 0.77), which was higher than the model built by the feature separately. QEEG features of theta wave in C3 had a marked impact on the model for classification according to the MIV algorithm.
Conclusions
PD-MCI is characterized by widespread structural and EEG abnormality. “Composite markers” could be valuable for the individualized diagnosis of PD-MCI by machine learning.
Key Points
• Explore the brain abnormalities in Parkinson’s disease with mild cognitive impairment by using the quantitative electroencephalogram and cortex structural MR simultaneously.
• Multimodal features based support vector machine for identifying Parkinson’s disease with mild cognitive impairment has an acceptable performance.
• Theta wave in C3 is the most influential feature of qEEG and cortex structure MR imaging in identifying Parkinson’s disease with mild cognitive impairment using support vector machine.</description><subject>Abnormalities</subject><subject>Algorithms</subject><subject>Cognition</subject><subject>Cognitive ability</subject><subject>Cognitive Dysfunction - diagnostic imaging</subject><subject>Cortex</subject><subject>Diagnostic Radiology</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Imaging</subject><subject>Impairment</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Markers</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Morphometry</subject><subject>Movement disorders</subject><subject>Neuro</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Neuroradiology</subject><subject>Parkinson Disease - complications</subject><subject>Parkinson Disease - diagnostic imaging</subject><subject>Parkinson's disease</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Sensitivity analysis</subject><subject>Statistical analysis</subject><subject>Support vector machines</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kcFuFSEUhklj017bvoALQ-LGzSgMcIGlaao2qbEx7ZowcGZKnYErzNTcna_h6_kkcr1VExcmEBLO9_-cw4_QM0peUULk60IIY6Qhbd1SSNHQA7SinLUNJYo_QSuimWqk1vwYPS3lnhCiKZdH6JgxpjRhaoUeLj3EOfTbEAd8bfPnEEuKP759L9iHArYA_hrmOzyF0WOXhhjm8AA4TBsb8lSluNvipezULk1diODxh0-1bofdnY0ewwhuzgmig82dHdOQ7XSKDns7Fjh7PE_Q7duLm_P3zdXHd5fnb64ax6SYG-3BCaes3C3KiZbWdXTdtZSw1nVr2ve6a0UvuKNSKSW1BK2V53btVO8FO0Ev976bnL4sUGYzheJgHG2EtBTTcinqLwkmK_riH_Q-LTnW7kwrJBdCSUkq1e4pl1MpGXqzyXXYvDWUmF0qZp-KqamYX6kYWkXPH62XbgL_R_I7hgqwPVBqKQ6Q_779H9ufR1CZ9g</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Zhang, Jiahui</creator><creator>Gao, Yuyuan</creator><creator>He, Xuetao</creator><creator>Feng, Shujun</creator><creator>Hu, Jinlong</creator><creator>Zhang, Qingxi</creator><creator>Zhao, Jiehao</creator><creator>Huang, Zhiheng</creator><creator>Wang, Limin</creator><creator>Ma, Guixian</creator><creator>Zhang, Yuhu</creator><creator>Nie, Kun</creator><creator>Wang, Lijuan</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1727-2670</orcidid></search><sort><creationdate>20211001</creationdate><title>Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram</title><author>Zhang, Jiahui ; Gao, Yuyuan ; He, Xuetao ; Feng, Shujun ; Hu, Jinlong ; Zhang, Qingxi ; Zhao, Jiehao ; Huang, Zhiheng ; Wang, Limin ; Ma, Guixian ; Zhang, Yuhu ; Nie, Kun ; Wang, Lijuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-9dec5c8a78a7814097acb16b21032cb61ff9b25f54c17888797e998d4a6c8fd53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abnormalities</topic><topic>Algorithms</topic><topic>Cognition</topic><topic>Cognitive ability</topic><topic>Cognitive Dysfunction - diagnostic imaging</topic><topic>Cortex</topic><topic>Diagnostic Radiology</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Imaging</topic><topic>Impairment</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Markers</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Morphometry</topic><topic>Movement disorders</topic><topic>Neuro</topic><topic>Neurodegenerative diseases</topic><topic>Neuroimaging</topic><topic>Neuroradiology</topic><topic>Parkinson Disease - complications</topic><topic>Parkinson Disease - diagnostic imaging</topic><topic>Parkinson's disease</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Sensitivity analysis</topic><topic>Statistical analysis</topic><topic>Support vector machines</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Jiahui</creatorcontrib><creatorcontrib>Gao, Yuyuan</creatorcontrib><creatorcontrib>He, Xuetao</creatorcontrib><creatorcontrib>Feng, Shujun</creatorcontrib><creatorcontrib>Hu, Jinlong</creatorcontrib><creatorcontrib>Zhang, Qingxi</creatorcontrib><creatorcontrib>Zhao, Jiehao</creatorcontrib><creatorcontrib>Huang, Zhiheng</creatorcontrib><creatorcontrib>Wang, Limin</creatorcontrib><creatorcontrib>Ma, Guixian</creatorcontrib><creatorcontrib>Zhang, Yuhu</creatorcontrib><creatorcontrib>Nie, Kun</creatorcontrib><creatorcontrib>Wang, Lijuan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>ProQuest Nursing and Allied Health Source</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</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)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Biological Science Journals</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Jiahui</au><au>Gao, Yuyuan</au><au>He, Xuetao</au><au>Feng, Shujun</au><au>Hu, Jinlong</au><au>Zhang, Qingxi</au><au>Zhao, Jiehao</au><au>Huang, Zhiheng</au><au>Wang, Limin</au><au>Ma, Guixian</au><au>Zhang, Yuhu</au><au>Nie, Kun</au><au>Wang, Lijuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-10-01</date><risdate>2021</risdate><volume>31</volume><issue>10</issue><spage>7386</spage><epage>7394</epage><pages>7386-7394</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI.
Methods
Retrospective analysis of patients with PD identified 36 PD-MCI and 35 PD with normal cognition (PD-NC). QEEG features of power spectrum and structural MR features of cortex based on surface-based morphometry (SBM) were extracted. Support vector machine (SVM) was established using combined features of structural MR and qEEG to identify PD-MCI. Feature importance evaluation algorithm of mean impact value (MIV) was established to sort the vital characteristics of qEEG and structural MR.
Results
Compared with PD-NC, PD-MCI showed a statistically significant difference in 5 leads and waves of qEEG and 7 cortical region features of structural MR. The SVM model based on these qEEG and structural MR features yielded an accuracy of 0.80 in the training set and had a high prediction accuracy of 0.80 in the test set (sensitivity was 0.78, specificity was 0.83, area under the receiver operating characteristic curve was 0.77), which was higher than the model built by the feature separately. QEEG features of theta wave in C3 had a marked impact on the model for classification according to the MIV algorithm.
Conclusions
PD-MCI is characterized by widespread structural and EEG abnormality. “Composite markers” could be valuable for the individualized diagnosis of PD-MCI by machine learning.
Key Points
• Explore the brain abnormalities in Parkinson’s disease with mild cognitive impairment by using the quantitative electroencephalogram and cortex structural MR simultaneously.
• Multimodal features based support vector machine for identifying Parkinson’s disease with mild cognitive impairment has an acceptable performance.
• Theta wave in C3 is the most influential feature of qEEG and cortex structure MR imaging in identifying Parkinson’s disease with mild cognitive impairment using support vector machine.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33389038</pmid><doi>10.1007/s00330-020-07575-1</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1727-2670</orcidid></addata></record> |
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subjects | Abnormalities Algorithms Cognition Cognitive ability Cognitive Dysfunction - diagnostic imaging Cortex Diagnostic Radiology EEG Electroencephalography Feature extraction Humans Imaging Impairment Internal Medicine Interventional Radiology Learning algorithms Machine learning Magnetic Resonance Imaging Markers Medical imaging Medicine Medicine & Public Health Morphometry Movement disorders Neuro Neurodegenerative diseases Neuroimaging Neuroradiology Parkinson Disease - complications Parkinson Disease - diagnostic imaging Parkinson's disease Radiology Retrospective Studies Sensitivity analysis Statistical analysis Support vector machines Ultrasound |
title | Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram |
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