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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
Main Authors: Demont-Guignard, Sophie, Benquet, Pascal, Gerber, Urs, Wendling, Fabrice
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Language:English
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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
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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. 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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 &amp; 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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.</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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source IEEE Electronic Library (IEL) Journals
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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