Modelling and analysis of local field potentials for studying the function of cortical circuits
Key Points The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to about 500 Hz. Key reasons for this resurgence are that LFPs offer a unique window into key integrative synaptic processes and...
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Veröffentlicht in: | Nature reviews. Neuroscience 2013-11, Vol.14 (11), p.770-785 |
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description | Key Points
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to about 500 Hz.
Key reasons for this resurgence are that LFPs offer a unique window into key integrative synaptic processes and their potential use in neural prosthetics.
Multiple neural signal processes contribute to the LFP, and computational methods are needed to tease apart these contributions and to properly interpret the signal in terms of its underlying neural activity.
The biophysical origin of LFPs is well understood, and accurate LFP modelling schemes based on detailed neuron models have been established.
Computational models explain how LFPs generated by cortical neural populations depend on electrode position, the dendritic morphologies of synaptically activated neurons, and on the spatial distribution and temporal correlations of synaptic inputs.
New methods for current source density (CSD) analysis are better able to identify the three-dimensional position of neural sources.
Advanced spectral and information-theoretical methods allow separation of independent contributions to the LFP in time and frequency.
Spike–field relationships probe additional aspects of functional connectivity.
Fitting the predictions of biophysical models to extracellular potentials allows the estimation of key network parameters.
Simultaneous measurements of LFPs and other large-scale measures of neural activity (for example, functional MRI) can progress our understanding of how macroscopic and microscopic networks interact.
Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to ∼500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are nee |
doi_str_mv | 10.1038/nrn3599 |
format | Article |
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The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to about 500 Hz.
Key reasons for this resurgence are that LFPs offer a unique window into key integrative synaptic processes and their potential use in neural prosthetics.
Multiple neural signal processes contribute to the LFP, and computational methods are needed to tease apart these contributions and to properly interpret the signal in terms of its underlying neural activity.
The biophysical origin of LFPs is well understood, and accurate LFP modelling schemes based on detailed neuron models have been established.
Computational models explain how LFPs generated by cortical neural populations depend on electrode position, the dendritic morphologies of synaptically activated neurons, and on the spatial distribution and temporal correlations of synaptic inputs.
New methods for current source density (CSD) analysis are better able to identify the three-dimensional position of neural sources.
Advanced spectral and information-theoretical methods allow separation of independent contributions to the LFP in time and frequency.
Spike–field relationships probe additional aspects of functional connectivity.
Fitting the predictions of biophysical models to extracellular potentials allows the estimation of key network parameters.
Simultaneous measurements of LFPs and other large-scale measures of neural activity (for example, functional MRI) can progress our understanding of how macroscopic and microscopic networks interact.
Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to ∼500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.</description><identifier>ISSN: 1471-003X</identifier><identifier>EISSN: 1471-0048</identifier><identifier>EISSN: 1469-3178</identifier><identifier>DOI: 10.1038/nrn3599</identifier><identifier>PMID: 24135696</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114/2397 ; 631/1647/1453 ; 631/378/116 ; 692/698/1688/512 ; Action Potentials ; Algorithms ; Animal Genetics and Genomics ; Animals ; Behavioral Sciences ; Biological Techniques ; Biomedicine ; Cerebral Cortex - physiology ; Cognition ; Electroencephalography ; Electrophysiological Phenomena ; Evoked Potentials - physiology ; Humans ; Models, Neurological ; Nerve Net - cytology ; Nerve Net - physiology ; Neural Pathways - physiology ; Neurobiology ; Neurons ; Neurons - physiology ; Neurosciences ; Perception ; Physiological aspects ; review-article ; Signal processing ; Synapses - physiology</subject><ispartof>Nature reviews. Neuroscience, 2013-11, Vol.14 (11), p.770-785</ispartof><rights>Springer Nature Limited 2013</rights><rights>COPYRIGHT 2013 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Nov 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-b3596525804de1a38cd30c6a27d2a1a6f6ecf94cb418e68e815db88532a95b693</citedby><cites>FETCH-LOGICAL-c442t-b3596525804de1a38cd30c6a27d2a1a6f6ecf94cb418e68e815db88532a95b693</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nrn3599$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nrn3599$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24135696$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Einevoll, Gaute T.</creatorcontrib><creatorcontrib>Kayser, Christoph</creatorcontrib><creatorcontrib>Logothetis, Nikos K.</creatorcontrib><creatorcontrib>Panzeri, Stefano</creatorcontrib><title>Modelling and analysis of local field potentials for studying the function of cortical circuits</title><title>Nature reviews. Neuroscience</title><addtitle>Nat Rev Neurosci</addtitle><addtitle>Nat Rev Neurosci</addtitle><description>Key Points
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to about 500 Hz.
Key reasons for this resurgence are that LFPs offer a unique window into key integrative synaptic processes and their potential use in neural prosthetics.
Multiple neural signal processes contribute to the LFP, and computational methods are needed to tease apart these contributions and to properly interpret the signal in terms of its underlying neural activity.
The biophysical origin of LFPs is well understood, and accurate LFP modelling schemes based on detailed neuron models have been established.
Computational models explain how LFPs generated by cortical neural populations depend on electrode position, the dendritic morphologies of synaptically activated neurons, and on the spatial distribution and temporal correlations of synaptic inputs.
New methods for current source density (CSD) analysis are better able to identify the three-dimensional position of neural sources.
Advanced spectral and information-theoretical methods allow separation of independent contributions to the LFP in time and frequency.
Spike–field relationships probe additional aspects of functional connectivity.
Fitting the predictions of biophysical models to extracellular potentials allows the estimation of key network parameters.
Simultaneous measurements of LFPs and other large-scale measures of neural activity (for example, functional MRI) can progress our understanding of how macroscopic and microscopic networks interact.
Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to ∼500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.</description><subject>631/114/2397</subject><subject>631/1647/1453</subject><subject>631/378/116</subject><subject>692/698/1688/512</subject><subject>Action Potentials</subject><subject>Algorithms</subject><subject>Animal Genetics and Genomics</subject><subject>Animals</subject><subject>Behavioral Sciences</subject><subject>Biological Techniques</subject><subject>Biomedicine</subject><subject>Cerebral Cortex - physiology</subject><subject>Cognition</subject><subject>Electroencephalography</subject><subject>Electrophysiological Phenomena</subject><subject>Evoked Potentials - physiology</subject><subject>Humans</subject><subject>Models, Neurological</subject><subject>Nerve Net - cytology</subject><subject>Nerve Net - physiology</subject><subject>Neural Pathways - physiology</subject><subject>Neurobiology</subject><subject>Neurons</subject><subject>Neurons - physiology</subject><subject>Neurosciences</subject><subject>Perception</subject><subject>Physiological aspects</subject><subject>review-article</subject><subject>Signal processing</subject><subject>Synapses - physiology</subject><issn>1471-003X</issn><issn>1471-0048</issn><issn>1469-3178</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkU9PXCEUxUlTU61t_AbmJV20m7Hw-DOwNMZWE42bNumO8OAyYt7ACLzFfPvy4lSrcWEIgcDvHO7hInRE8AnBVH6POVKu1Dt0QNiSLDBm8v3jnv7ZRx9LucOYCLIUH9B-zwjlQokDpK-Tg3EMcdWZ6No047aE0iXfjcmasfMBRtdtUoVYgxlL51PuSp3cdtbUW-j8FG0NKc4am3INs8yGbKdQyye055sKPu_WQ_T7x_mvs4vF1c3Py7PTq4VlrK-LoVUveM8lZg6IodI6iq0w_dL1hhjhBVivmB0YkSAkSMLdICWnvVF8EIoeom8Pvpuc7icoVa9DsS2ZiZCmogkTjBOBJX4DyijDqr3U0C8v0Ls05fZHM8Xlkkos6BO1MiPoEH2q2djZVJ9SphSmlPFGnbxCteFgHWyK4EM7fyb4-iCwOZWSwetNDmuTt5pgPXdd77reyONdmdOwBvfI_WvzU-TSruIK8n85Xnj9BVEOsu0</recordid><startdate>20131101</startdate><enddate>20131101</enddate><creator>Einevoll, Gaute T.</creator><creator>Kayser, Christoph</creator><creator>Logothetis, Nikos K.</creator><creator>Panzeri, Stefano</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</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>7QG</scope><scope>7QP</scope><scope>7QR</scope><scope>7RV</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</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>M2M</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20131101</creationdate><title>Modelling and analysis of local field potentials for studying the function of cortical circuits</title><author>Einevoll, Gaute T. ; Kayser, Christoph ; Logothetis, Nikos K. ; Panzeri, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-b3596525804de1a38cd30c6a27d2a1a6f6ecf94cb418e68e815db88532a95b693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>631/114/2397</topic><topic>631/1647/1453</topic><topic>631/378/116</topic><topic>692/698/1688/512</topic><topic>Action Potentials</topic><topic>Algorithms</topic><topic>Animal Genetics and Genomics</topic><topic>Animals</topic><topic>Behavioral Sciences</topic><topic>Biological Techniques</topic><topic>Biomedicine</topic><topic>Cerebral Cortex - physiology</topic><topic>Cognition</topic><topic>Electroencephalography</topic><topic>Electrophysiological Phenomena</topic><topic>Evoked Potentials - physiology</topic><topic>Humans</topic><topic>Models, Neurological</topic><topic>Nerve Net - cytology</topic><topic>Nerve Net - physiology</topic><topic>Neural Pathways - physiology</topic><topic>Neurobiology</topic><topic>Neurons</topic><topic>Neurons - physiology</topic><topic>Neurosciences</topic><topic>Perception</topic><topic>Physiological aspects</topic><topic>review-article</topic><topic>Signal processing</topic><topic>Synapses - physiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Einevoll, Gaute T.</creatorcontrib><creatorcontrib>Kayser, Christoph</creatorcontrib><creatorcontrib>Logothetis, Nikos K.</creatorcontrib><creatorcontrib>Panzeri, Stefano</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>Animal Behavior Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech 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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>Medical Database</collection><collection>Psychology Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</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>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Nature reviews. Neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Einevoll, Gaute T.</au><au>Kayser, Christoph</au><au>Logothetis, Nikos K.</au><au>Panzeri, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling and analysis of local field potentials for studying the function of cortical circuits</atitle><jtitle>Nature reviews. Neuroscience</jtitle><stitle>Nat Rev Neurosci</stitle><addtitle>Nat Rev Neurosci</addtitle><date>2013-11-01</date><risdate>2013</risdate><volume>14</volume><issue>11</issue><spage>770</spage><epage>785</epage><pages>770-785</pages><issn>1471-003X</issn><eissn>1471-0048</eissn><eissn>1469-3178</eissn><abstract>Key Points
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to about 500 Hz.
Key reasons for this resurgence are that LFPs offer a unique window into key integrative synaptic processes and their potential use in neural prosthetics.
Multiple neural signal processes contribute to the LFP, and computational methods are needed to tease apart these contributions and to properly interpret the signal in terms of its underlying neural activity.
The biophysical origin of LFPs is well understood, and accurate LFP modelling schemes based on detailed neuron models have been established.
Computational models explain how LFPs generated by cortical neural populations depend on electrode position, the dendritic morphologies of synaptically activated neurons, and on the spatial distribution and temporal correlations of synaptic inputs.
New methods for current source density (CSD) analysis are better able to identify the three-dimensional position of neural sources.
Advanced spectral and information-theoretical methods allow separation of independent contributions to the LFP in time and frequency.
Spike–field relationships probe additional aspects of functional connectivity.
Fitting the predictions of biophysical models to extracellular potentials allows the estimation of key network parameters.
Simultaneous measurements of LFPs and other large-scale measures of neural activity (for example, functional MRI) can progress our understanding of how macroscopic and microscopic networks interact.
Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.
The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) — that is, extracellularly recorded potentials with frequencies of up to ∼500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>24135696</pmid><doi>10.1038/nrn3599</doi><tpages>16</tpages></addata></record> |
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subjects | 631/114/2397 631/1647/1453 631/378/116 692/698/1688/512 Action Potentials Algorithms Animal Genetics and Genomics Animals Behavioral Sciences Biological Techniques Biomedicine Cerebral Cortex - physiology Cognition Electroencephalography Electrophysiological Phenomena Evoked Potentials - physiology Humans Models, Neurological Nerve Net - cytology Nerve Net - physiology Neural Pathways - physiology Neurobiology Neurons Neurons - physiology Neurosciences Perception Physiological aspects review-article Signal processing Synapses - physiology |
title | Modelling and analysis of local field potentials for studying the function of cortical circuits |
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