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Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model
The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was t...
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Published in: | IEEE transactions on biomedical engineering 2009-12, Vol.56 (12), p.2782-2795 |
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creator | Demont-Guignard, Sophie Benquet, Pascal Gerber, Urs Wendling, Fabrice |
description | The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on ldquominimalrdquo but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number (~3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component (ldquospikerdquo) and the late slow component (ldquonegative waverdquo) was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network. |
doi_str_mv | 10.1109/TBME.2009.2028015 |
format | article |
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Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on ldquominimalrdquo but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number (~3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component (ldquospikerdquo) and the late slow component (ldquonegative waverdquo) was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.</description><identifier>ISSN: 0018-9294</identifier><identifier>EISSN: 1558-2531</identifier><identifier>DOI: 10.1109/TBME.2009.2028015</identifier><identifier>PMID: 19651549</identifier><identifier>CODEN: IEBEAX</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Action Potentials ; Bioengineering ; Biological system modeling ; Brain ; Brain - physiopathology ; Brain modeling ; CA1 ; Circuits ; computational modeling ; Computer Science ; Computer Simulation ; Diagnosis, Computer-Assisted ; Diagnosis, Computer-Assisted - methods ; Electrodes ; Electroencephalography ; Electroencephalography - methods ; Engineering Sciences ; Epilepsy ; Epilepsy - diagnosis ; Epilepsy - physiopathology ; Hippocampus ; Humans ; Life Sciences ; local field potentials (LFPs) ; Modeling and Simulation ; Models, Neurological ; Nerve Net ; Nerve Net - physiopathology ; Neural and Evolutionary Computing ; Neural networks ; Neurons ; population spikes ; Shape ; Signal and Image processing</subject><ispartof>IEEE transactions on biomedical engineering, 2009-12, Vol.56 (12), p.2782-2795</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c642t-853807b5c7c132817972f47b6ccb71887595bb5066be7c90f2427a773cc884853</citedby><cites>FETCH-LOGICAL-c642t-853807b5c7c132817972f47b6ccb71887595bb5066be7c90f2427a773cc884853</cites><orcidid>0000-0003-2428-9665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5184929$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,54796</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19651549$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://inserm.hal.science/inserm-00426352$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Demont-Guignard, Sophie</creatorcontrib><creatorcontrib>Benquet, Pascal</creatorcontrib><creatorcontrib>Gerber, Urs</creatorcontrib><creatorcontrib>Wendling, Fabrice</creatorcontrib><title>Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model</title><title>IEEE transactions on biomedical engineering</title><addtitle>TBME</addtitle><addtitle>IEEE Trans Biomed Eng</addtitle><description>The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on ldquominimalrdquo but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number (~3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component (ldquospikerdquo) and the late slow component (ldquonegative waverdquo) was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.</description><subject>Action Potentials</subject><subject>Bioengineering</subject><subject>Biological system modeling</subject><subject>Brain</subject><subject>Brain - physiopathology</subject><subject>Brain modeling</subject><subject>CA1</subject><subject>Circuits</subject><subject>computational modeling</subject><subject>Computer Science</subject><subject>Computer Simulation</subject><subject>Diagnosis, Computer-Assisted</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Electrodes</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Engineering Sciences</subject><subject>Epilepsy</subject><subject>Epilepsy - diagnosis</subject><subject>Epilepsy - physiopathology</subject><subject>Hippocampus</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>local field potentials (LFPs)</subject><subject>Modeling and Simulation</subject><subject>Models, Neurological</subject><subject>Nerve Net</subject><subject>Nerve Net - physiopathology</subject><subject>Neural and Evolutionary Computing</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>population spikes</subject><subject>Shape</subject><subject>Signal and Image processing</subject><issn>0018-9294</issn><issn>1558-2531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkktv1DAUhSMEotPCD0BIKGJRNqT4-m0WSEOVPqRpkaCsrcTjmXGbxMFOivrvcZhRgS7oxpZ1v3Our32y7BWgIwCkPlx9viiPMEIqLVgiYE-yGTAmC8wIPM1mCIEsFFZ0L9uP8TodqaT8ebYHijNgVM2yet5VzV10Mfer_LwbQmVssHWomrwsT_Ov1viwdN36d73sXWP7wZn8W-9ubPyYFNGtN0PMT4Jv8yq_tOMkvbTDTx9u8gu_tM2L7NmqaqJ9udsPsu8n5dXxWbH4cnp-PF8UhlM8FJIRiUTNjDBAsAShBF5RUXNjagFSCqZYXTPEeW2FUWiFKRaVEMQYKWlSH2Sftr79WLd2aew0TaP74Noq3GlfOf1vpXMbvfa3mmDKBKXJ4P3WYPNAdjZfaNdFG1qNEMWcMHwLCX-36xf8j9HGQbcuGts0VWf9GLXkIrkiTh8lBSEcJFLTDIf_JQknFJjkj4IYQFBESQLfPgCv_RjSp6cLMi4FYCoTBFvIBB9jsKv78QHpKWp6ipqeoqZ3UUuaN38_9x_FLlsJeL0FnLX2vsxA0hRJ8guywtUo</recordid><startdate>20091201</startdate><enddate>20091201</enddate><creator>Demont-Guignard, Sophie</creator><creator>Benquet, Pascal</creator><creator>Gerber, Urs</creator><creator>Wendling, Fabrice</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</general><general>Institute Of Electrical And Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7TK</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2428-9665</orcidid></search><sort><creationdate>20091201</creationdate><title>Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model</title><author>Demont-Guignard, Sophie ; Benquet, Pascal ; Gerber, Urs ; Wendling, Fabrice</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c642t-853807b5c7c132817972f47b6ccb71887595bb5066be7c90f2427a773cc884853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Action Potentials</topic><topic>Bioengineering</topic><topic>Biological system modeling</topic><topic>Brain</topic><topic>Brain - physiopathology</topic><topic>Brain modeling</topic><topic>CA1</topic><topic>Circuits</topic><topic>computational modeling</topic><topic>Computer Science</topic><topic>Computer Simulation</topic><topic>Diagnosis, Computer-Assisted</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Electrodes</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>Engineering Sciences</topic><topic>Epilepsy</topic><topic>Epilepsy - diagnosis</topic><topic>Epilepsy - physiopathology</topic><topic>Hippocampus</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>local field potentials (LFPs)</topic><topic>Modeling and Simulation</topic><topic>Models, Neurological</topic><topic>Nerve Net</topic><topic>Nerve Net - physiopathology</topic><topic>Neural and Evolutionary Computing</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>population spikes</topic><topic>Shape</topic><topic>Signal and Image processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Demont-Guignard, Sophie</creatorcontrib><creatorcontrib>Benquet, Pascal</creatorcontrib><creatorcontrib>Gerber, Urs</creatorcontrib><creatorcontrib>Wendling, Fabrice</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>IEEE transactions on biomedical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Demont-Guignard, Sophie</au><au>Benquet, Pascal</au><au>Gerber, Urs</au><au>Wendling, Fabrice</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model</atitle><jtitle>IEEE transactions on biomedical engineering</jtitle><stitle>TBME</stitle><addtitle>IEEE Trans Biomed Eng</addtitle><date>2009-12-01</date><risdate>2009</risdate><volume>56</volume><issue>12</issue><spage>2782</spage><epage>2795</epage><pages>2782-2795</pages><issn>0018-9294</issn><eissn>1558-2531</eissn><coden>IEBEAX</coden><abstract>The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on ldquominimalrdquo but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number (~3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component (ldquospikerdquo) and the late slow component (ldquonegative waverdquo) was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>19651549</pmid><doi>10.1109/TBME.2009.2028015</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-2428-9665</orcidid><oa>free_for_read</oa></addata></record> |
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language | eng |
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subjects | Action Potentials Bioengineering Biological system modeling Brain Brain - physiopathology Brain modeling CA1 Circuits computational modeling Computer Science Computer Simulation Diagnosis, Computer-Assisted Diagnosis, Computer-Assisted - methods Electrodes Electroencephalography Electroencephalography - methods Engineering Sciences Epilepsy Epilepsy - diagnosis Epilepsy - physiopathology Hippocampus Humans Life Sciences local field potentials (LFPs) Modeling and Simulation Models, Neurological Nerve Net Nerve Net - physiopathology Neural and Evolutionary Computing Neural networks Neurons population spikes Shape Signal and Image processing |
title | Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model |
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