{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T20:09:34Z","timestamp":1777320574638,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010353","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000}}],"reference-count":102,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["785907"],"award-info":[{"award-number":["785907"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["945539"],"award-info":[{"award-number":["945539"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["800858"],"award-info":[{"award-number":["800858"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","award":["the Initiative and Networking Fund of the Helmholtz Association"],"award-info":[{"award-number":["the Initiative and Networking Fund of the Helmholtz Association"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","award":["Helmholtz Portfolio theme Supercomputing and Modeling for the Human Brain"],"award-info":[{"award-number":["Helmholtz Portfolio theme Supercomputing and Modeling for the Human Brain"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["G:(DE-82)EXS-PF-JARA-SDS005"],"award-info":[{"award-number":["G:(DE-82)EXS-PF-JARA-SDS005"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["G:(DE-82)EXS-SF-neuroIC002"],"award-info":[{"award-number":["G:(DE-82)EXS-SF-neuroIC002"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (\u2272 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (\u2273 10\n                    <jats:sup>6<\/jats:sup>\n                    neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (\u2018spikes\u2019) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.\n                  <\/jats:p>\n                  <jats:p>Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.<\/jats:p>\n                  <jats:p>\n                    The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool\n                    <jats:monospace>LFPykernels<\/jats:monospace>\n                    serves as a reference implementation of the framework.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010353","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T13:46:59Z","timestamp":1660312019000},"page":"e1010353","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":24,"title":["Brain signal predictions from multi-scale networks using a linearized framework"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1321-5970","authenticated-orcid":true,"given":"Espen","family":"Hagen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3319-0547","authenticated-orcid":true,"given":"Steinn H.","family":"Magnusson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9080-8502","authenticated-orcid":true,"given":"Torbj\u00f8rn V.","family":"Ness","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4721-1599","authenticated-orcid":true,"given":"Geir","family":"Halnes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1963-6370","authenticated-orcid":true,"given":"Pooja N.","family":"Babu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8140-2866","authenticated-orcid":true,"given":"Charl","family":"Linssen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abigail","family":"Morrison","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5425-5012","authenticated-orcid":true,"given":"Gaute T.","family":"Einevoll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"issue":"3247","key":"pcbi.1010353.ref001","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1126\/science.125.3247.549","article-title":"Tungsten Microelectrode for Recording from Single Units","volume":"125","author":"DH Hubel","year":"1957","journal-title":"Science"},{"issue":"1","key":"pcbi.1010353.ref002","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/BF01797193","article-title":"\u00dcber das Elektrenkephalogramm des Menschen","volume":"87","author":"H Berger","year":"1929","journal-title":"Archiv f\u00fcr Psychiatrie und Nervenkrankheiten"},{"key":"pcbi.1010353.ref003","doi-asserted-by":"crossref","DOI":"10.1093\/acprof:oso\/9780195050387.001.0001","volume-title":"Electric fields of the brain: The neurophysics of EEG","author":"PL Nunez","year":"2006"},{"issue":"2","key":"pcbi.1010353.ref004","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1103\/RevModPhys.65.413","article-title":"Magnetoencephalography\u2014theory, instrumentation, and applications to noninvasive studies of the working human brain","volume":"65","author":"M H\u00e4m\u00e4l\u00e4inen","year":"1993","journal-title":"Reviews of Modern Physics"},{"issue":"11","key":"pcbi.1010353.ref005","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1038\/nrn3599","article-title":"Modelling and analysis of local field potentials for studying the function of cortical circuits","volume":"14","author":"GT Einevoll","year":"2013","journal-title":"Nature Reviews Neuroscience"},{"issue":"4","key":"pcbi.1010353.ref006","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.neuron.2019.03.027","article-title":"The Scientific Case for Brain Simulations","volume":"102","author":"GT Einevoll","year":"2019","journal-title":"Neuron"},{"key":"pcbi.1010353.ref007","volume-title":"Computational modeling methods for neuroscientists","author":"E de Schutter","year":"2010"},{"key":"pcbi.1010353.ref008","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511541612","volume-title":"The Neuron Book","author":"NT Carnevale","year":"2006"},{"key":"pcbi.1010353.ref009","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-1634-6","volume-title":"The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System","author":"JM Bower","year":"1998"},{"key":"pcbi.1010353.ref010","doi-asserted-by":"crossref","unstructured":"Akar NA, Cumming B, Karakasis V, Kusters A, Klijn W, Peyser A, et al. 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