High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation
•An approach for analysis of task-related high-gamma modulation in stereo-EEG using distribution of power differential clusters is described.•Stereo-EEG high-gamma language mapping effectively localized reference neuroanatomy (Neurosynth).•Stereo-EEG high-gamma language mapping also adequately class...
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| Veröffentlicht in: | Clinical neurophysiology 2020-12, Vol.131 (12), p.2851-2860 |
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| creator | Ervin, Brian Buroker, Jason Rozhkov, Leonid Holloway, Timothy Horn, Paul S. Scholle, Craig Byars, Anna W. Mangano, Francesco T. Leach, James L. Greiner, Hansel M. Holland, Katherine D. Arya, Ravindra |
| description | •An approach for analysis of task-related high-gamma modulation in stereo-EEG using distribution of power differential clusters is described.•Stereo-EEG high-gamma language mapping effectively localized reference neuroanatomy (Neurosynth).•Stereo-EEG high-gamma language mapping also adequately classified electrical stimulation mapping speech/language sites.
A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping.
SEEG signals, acquired from drug-resistant epilepsy patients during a visual naming task, were analyzed to find clusters of 50–150 Hz power modulations in time–frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated.
In 21 patients (9 females), aged 4.8–21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p |
| doi_str_mv | 10.1016/j.clinph.2020.09.023 |
| format | Article |
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A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping.
SEEG signals, acquired from drug-resistant epilepsy patients during a visual naming task, were analyzed to find clusters of 50–150 Hz power modulations in time–frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated.
In 21 patients (9 females), aged 4.8–21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p < 0.0001), high specificity (0.85), and fair sensitivity (0.66). Another SEEG HGM model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001), high specificity (0.74), but insufficient sensitivity. Time to largest power change reliably localized electrodes within Neurosynth language parcels, while, time to center-of-mass power change identified ESM sites.
SEEG HGM mapping can accurately localize neuroanatomic and ESM language sites.
Predictive modelling incorporating time, frequency, and magnitude of power change is a useful methodology for task-related HGM, which offers insights into discrepancies between HGM language maps and neuroanatomy or ESM.</description><identifier>ISSN: 1388-2457</identifier><identifier>EISSN: 1872-8952</identifier><identifier>DOI: 10.1016/j.clinph.2020.09.023</identifier><identifier>PMID: 33137575</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Cortical localization ; Epilepsy surgery ; High-gamma activation ; Intracranial electrodes ; Machine learning</subject><ispartof>Clinical neurophysiology, 2020-12, Vol.131 (12), p.2851-2860</ispartof><rights>2020 International Federation of Clinical Neurophysiology</rights><rights>Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c362t-7ccbf0635035c9475ea7c36592ad5d8fddeca6dad4c5c6c8fa3894b3432547913</citedby><cites>FETCH-LOGICAL-c362t-7ccbf0635035c9475ea7c36592ad5d8fddeca6dad4c5c6c8fa3894b3432547913</cites><orcidid>0000-0003-0873-9718</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.clinph.2020.09.023$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33137575$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ervin, Brian</creatorcontrib><creatorcontrib>Buroker, Jason</creatorcontrib><creatorcontrib>Rozhkov, Leonid</creatorcontrib><creatorcontrib>Holloway, Timothy</creatorcontrib><creatorcontrib>Horn, Paul S.</creatorcontrib><creatorcontrib>Scholle, Craig</creatorcontrib><creatorcontrib>Byars, Anna W.</creatorcontrib><creatorcontrib>Mangano, Francesco T.</creatorcontrib><creatorcontrib>Leach, James L.</creatorcontrib><creatorcontrib>Greiner, Hansel M.</creatorcontrib><creatorcontrib>Holland, Katherine D.</creatorcontrib><creatorcontrib>Arya, Ravindra</creatorcontrib><title>High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation</title><title>Clinical neurophysiology</title><addtitle>Clin Neurophysiol</addtitle><description>•An approach for analysis of task-related high-gamma modulation in stereo-EEG using distribution of power differential clusters is described.•Stereo-EEG high-gamma language mapping effectively localized reference neuroanatomy (Neurosynth).•Stereo-EEG high-gamma language mapping also adequately classified electrical stimulation mapping speech/language sites.
A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping.
SEEG signals, acquired from drug-resistant epilepsy patients during a visual naming task, were analyzed to find clusters of 50–150 Hz power modulations in time–frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated.
In 21 patients (9 females), aged 4.8–21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p < 0.0001), high specificity (0.85), and fair sensitivity (0.66). Another SEEG HGM model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001), high specificity (0.74), but insufficient sensitivity. Time to largest power change reliably localized electrodes within Neurosynth language parcels, while, time to center-of-mass power change identified ESM sites.
SEEG HGM mapping can accurately localize neuroanatomic and ESM language sites.
Predictive modelling incorporating time, frequency, and magnitude of power change is a useful methodology for task-related HGM, which offers insights into discrepancies between HGM language maps and neuroanatomy or ESM.</description><subject>Cortical localization</subject><subject>Epilepsy surgery</subject><subject>High-gamma activation</subject><subject>Intracranial electrodes</subject><subject>Machine learning</subject><issn>1388-2457</issn><issn>1872-8952</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAMgCME4v0PEMqRS0uaNE3LAQmh8ZCQuMA58hK3y2ib0bRD_HsyBhw52XI-2_FHyFnG0oxlxeUyNa3rV4uUM85SVqWMix1ymJWKJ2Ul-W7MRVkmPJfqgByFsGSMKZbzfXIgRCaUVPKQvD24ZpE00HVAO2-nFkbne9pC30zQIO1gtXJ9Qz_cuKBhxAF9MpvdX9Eb2vs1thR6aD9HZ2gEBw9mESuWWgdN78OmvobW2e-pJ2Svhjbg6U88Jq93s5fbh-Tp-f7x9uYpMaLgY6KMmdesEJIJaapcSQQVX2TFwUpb1taigcKCzY00hSlrEGWVz0UuuMxVlYljcrGdGz_0PmEYdeeCwTYehX4KemOEl1lRsIjmW9QMPoQBa70aXAfDp86Y3mjWS73VrDeaNat01Bzbzn82TPMO7V_Tr9cIXG8BjHeuHQ46GIe9QesGNKO23v2_4QtyXpFw</recordid><startdate>202012</startdate><enddate>202012</enddate><creator>Ervin, Brian</creator><creator>Buroker, Jason</creator><creator>Rozhkov, Leonid</creator><creator>Holloway, Timothy</creator><creator>Horn, Paul S.</creator><creator>Scholle, Craig</creator><creator>Byars, Anna W.</creator><creator>Mangano, Francesco T.</creator><creator>Leach, James L.</creator><creator>Greiner, Hansel M.</creator><creator>Holland, Katherine D.</creator><creator>Arya, Ravindra</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0873-9718</orcidid></search><sort><creationdate>202012</creationdate><title>High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation</title><author>Ervin, Brian ; Buroker, Jason ; Rozhkov, Leonid ; Holloway, Timothy ; Horn, Paul S. ; Scholle, Craig ; Byars, Anna W. ; Mangano, Francesco T. ; Leach, James L. ; Greiner, Hansel M. ; Holland, Katherine D. ; Arya, Ravindra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-7ccbf0635035c9475ea7c36592ad5d8fddeca6dad4c5c6c8fa3894b3432547913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Cortical localization</topic><topic>Epilepsy surgery</topic><topic>High-gamma activation</topic><topic>Intracranial electrodes</topic><topic>Machine learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ervin, Brian</creatorcontrib><creatorcontrib>Buroker, Jason</creatorcontrib><creatorcontrib>Rozhkov, Leonid</creatorcontrib><creatorcontrib>Holloway, Timothy</creatorcontrib><creatorcontrib>Horn, Paul S.</creatorcontrib><creatorcontrib>Scholle, Craig</creatorcontrib><creatorcontrib>Byars, Anna W.</creatorcontrib><creatorcontrib>Mangano, Francesco T.</creatorcontrib><creatorcontrib>Leach, James L.</creatorcontrib><creatorcontrib>Greiner, Hansel M.</creatorcontrib><creatorcontrib>Holland, Katherine D.</creatorcontrib><creatorcontrib>Arya, Ravindra</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical neurophysiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ervin, Brian</au><au>Buroker, Jason</au><au>Rozhkov, Leonid</au><au>Holloway, Timothy</au><au>Horn, Paul S.</au><au>Scholle, Craig</au><au>Byars, Anna W.</au><au>Mangano, Francesco T.</au><au>Leach, James L.</au><au>Greiner, Hansel M.</au><au>Holland, Katherine D.</au><au>Arya, Ravindra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation</atitle><jtitle>Clinical neurophysiology</jtitle><addtitle>Clin Neurophysiol</addtitle><date>2020-12</date><risdate>2020</risdate><volume>131</volume><issue>12</issue><spage>2851</spage><epage>2860</epage><pages>2851-2860</pages><issn>1388-2457</issn><eissn>1872-8952</eissn><abstract>•An approach for analysis of task-related high-gamma modulation in stereo-EEG using distribution of power differential clusters is described.•Stereo-EEG high-gamma language mapping effectively localized reference neuroanatomy (Neurosynth).•Stereo-EEG high-gamma language mapping also adequately classified electrical stimulation mapping speech/language sites.
A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping.
SEEG signals, acquired from drug-resistant epilepsy patients during a visual naming task, were analyzed to find clusters of 50–150 Hz power modulations in time–frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated.
In 21 patients (9 females), aged 4.8–21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p < 0.0001), high specificity (0.85), and fair sensitivity (0.66). Another SEEG HGM model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001), high specificity (0.74), but insufficient sensitivity. Time to largest power change reliably localized electrodes within Neurosynth language parcels, while, time to center-of-mass power change identified ESM sites.
SEEG HGM mapping can accurately localize neuroanatomic and ESM language sites.
Predictive modelling incorporating time, frequency, and magnitude of power change is a useful methodology for task-related HGM, which offers insights into discrepancies between HGM language maps and neuroanatomy or ESM.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>33137575</pmid><doi>10.1016/j.clinph.2020.09.023</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-0873-9718</orcidid></addata></record> |
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| subjects | Cortical localization Epilepsy surgery High-gamma activation Intracranial electrodes Machine learning |
| title | High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation |
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