Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study
Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here...
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creator | Seedat, Zelekha A. Rier, Lukas Gascoyne, Lauren E. Cook, Harry Woolrich, Mark W. Quinn, Andrew J. Roberts, Timothy P. L. Furlong, Paul L. Armstrong, Caren St. Pier, Kelly Mullinger, Karen J. Marsh, Eric D. Brookes, Matthew J. Gaetz, William |
description | Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy.
In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas. |
doi_str_mv | 10.1002/hbm.26118 |
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In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.26118</identifier><identifier>PMID: 36259549</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Brain ; Brain Mapping - methods ; Child ; Convulsions & seizures ; Data acquisition ; Decision analysis ; Decision making ; Drug Resistant Epilepsy - surgery ; Electroencephalography ; Electroencephalography - methods ; Epilepsy ; Epilepsy - diagnostic imaging ; Epilepsy - surgery ; Etiology ; hidden Markov model ; Humans ; interictal activity ; Kurtosis ; Localization ; Magnetic fields ; Magnetic resonance imaging ; Magnetoencephalography ; Magnetoencephalography - methods ; Mapping ; Markov chains ; Mathematical models ; Methods ; Neurological diseases ; Patients ; Pediatrics ; Performance assessment ; Philadelphia ; Probability distribution ; Statistical models ; Tomography</subject><ispartof>Human brain mapping, 2023-01, Vol.44 (1), p.66-81</ispartof><rights>2022 The Authors. published by Wiley Periodicals LLC.</rights><rights>2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>2022. This article 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><citedby>FETCH-LOGICAL-c4438-93793b759f27dc4f6b2631917627dcf4d2d5ef62421c0b3752c2c7b60f8632423</citedby><cites>FETCH-LOGICAL-c4438-93793b759f27dc4f6b2631917627dcf4d2d5ef62421c0b3752c2c7b60f8632423</cites><orcidid>0000-0002-8164-0274 ; 0000-0002-8687-8185 ; 0000-0001-5453-2289</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783449/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783449/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1411,11541,27901,27902,45550,45551,46027,46451,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36259549$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Seedat, Zelekha A.</creatorcontrib><creatorcontrib>Rier, Lukas</creatorcontrib><creatorcontrib>Gascoyne, Lauren E.</creatorcontrib><creatorcontrib>Cook, Harry</creatorcontrib><creatorcontrib>Woolrich, Mark W.</creatorcontrib><creatorcontrib>Quinn, Andrew J.</creatorcontrib><creatorcontrib>Roberts, Timothy P. L.</creatorcontrib><creatorcontrib>Furlong, Paul L.</creatorcontrib><creatorcontrib>Armstrong, Caren</creatorcontrib><creatorcontrib>St. Pier, Kelly</creatorcontrib><creatorcontrib>Mullinger, Karen J.</creatorcontrib><creatorcontrib>Marsh, Eric D.</creatorcontrib><creatorcontrib>Brookes, Matthew J.</creatorcontrib><creatorcontrib>Gaetz, William</creatorcontrib><title>Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy.
In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.</description><subject>Brain</subject><subject>Brain Mapping - methods</subject><subject>Child</subject><subject>Convulsions & seizures</subject><subject>Data acquisition</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Drug Resistant Epilepsy - surgery</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Epilepsy</subject><subject>Epilepsy - diagnostic imaging</subject><subject>Epilepsy - surgery</subject><subject>Etiology</subject><subject>hidden Markov model</subject><subject>Humans</subject><subject>interictal activity</subject><subject>Kurtosis</subject><subject>Localization</subject><subject>Magnetic fields</subject><subject>Magnetic resonance imaging</subject><subject>Magnetoencephalography</subject><subject>Magnetoencephalography - methods</subject><subject>Mapping</subject><subject>Markov chains</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Neurological diseases</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Performance assessment</subject><subject>Philadelphia</subject><subject>Probability distribution</subject><subject>Statistical models</subject><subject>Tomography</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kctO3DAUQK2qVXm0i_5AZambsgj4FTtmUYkiKEiMumnXluM4E0NiBzuZKn-PhwFUKnXl19HRtQ4AnzA6xgiRk64ejgnHuHoD9jGSokBY0rfbPS8LyQTeAwcp3SKEcYnwe7BHOSllyeQ-MCs9js6v4bWfbHRm0j3UZnIbNy3QeWhH19sxLXBOW0rDzjWN9XCl413YwCE0tj-FZ3DQa2-nYL2xY6f7sI567BaYprlZPoB3re6T_fi0HoLflxe_zq-Km58_rs_PbgrDGK0KSYWktShlS0RjWMtrwimWWPDtuWUNaUrbcsIINqimoiSGGFFz1Fac5lt6CL7tvONcD7Yx1k9R92qMbtBxUUE79frFu06tw0ZJUVHGZBZ8fRLEcD_bNKnBJWP7Xnsb5qSIIJwhUSGW0S__oLdhjj5_L1OlQAjJR-poR5kYUoq2fRkGI7VNp3I69Zgus5__nv6FfG6VgZMd8CcnWf5vUlffVzvlA3azo1A</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Seedat, Zelekha A.</creator><creator>Rier, Lukas</creator><creator>Gascoyne, Lauren E.</creator><creator>Cook, Harry</creator><creator>Woolrich, Mark W.</creator><creator>Quinn, Andrew J.</creator><creator>Roberts, Timothy P. L.</creator><creator>Furlong, Paul L.</creator><creator>Armstrong, Caren</creator><creator>St. Pier, Kelly</creator><creator>Mullinger, Karen J.</creator><creator>Marsh, Eric D.</creator><creator>Brookes, Matthew J.</creator><creator>Gaetz, William</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><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>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8164-0274</orcidid><orcidid>https://orcid.org/0000-0002-8687-8185</orcidid><orcidid>https://orcid.org/0000-0001-5453-2289</orcidid></search><sort><creationdate>202301</creationdate><title>Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study</title><author>Seedat, Zelekha A. ; Rier, Lukas ; Gascoyne, Lauren E. ; Cook, Harry ; Woolrich, Mark W. ; Quinn, Andrew J. ; Roberts, Timothy P. 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L.</au><au>Furlong, Paul L.</au><au>Armstrong, Caren</au><au>St. Pier, Kelly</au><au>Mullinger, Karen J.</au><au>Marsh, Eric D.</au><au>Brookes, Matthew J.</au><au>Gaetz, William</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2023-01</date><risdate>2023</risdate><volume>44</volume><issue>1</issue><spage>66</spage><epage>81</epage><pages>66-81</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy.
In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36259549</pmid><doi>10.1002/hbm.26118</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-8164-0274</orcidid><orcidid>https://orcid.org/0000-0002-8687-8185</orcidid><orcidid>https://orcid.org/0000-0001-5453-2289</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Brain Brain Mapping - methods Child Convulsions & seizures Data acquisition Decision analysis Decision making Drug Resistant Epilepsy - surgery Electroencephalography Electroencephalography - methods Epilepsy Epilepsy - diagnostic imaging Epilepsy - surgery Etiology hidden Markov model Humans interictal activity Kurtosis Localization Magnetic fields Magnetic resonance imaging Magnetoencephalography Magnetoencephalography - methods Mapping Markov chains Mathematical models Methods Neurological diseases Patients Pediatrics Performance assessment Philadelphia Probability distribution Statistical models Tomography |
title | Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study |
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