{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:11:02Z","timestamp":1775041862944,"version":"3.50.1"},"reference-count":53,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"tdm","delay-in-days":28,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Cleveland Clinic Neurological Institute Clinician Scientist Development Award"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2024,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free. <jats:italic>Approach.<\/jats:italic> DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures (\u2018modes\u2032) defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery. <jats:italic>Main results.<\/jats:italic> DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM\/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ. <jats:italic>Significance.<\/jats:italic> This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.<\/jats:p>","DOI":"10.1088\/1741-2552\/ad705f","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T22:53:56Z","timestamp":1723848836000},"page":"046061","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Epileptic network identification: insights from dynamic mode decomposition of sEEG data"],"prefix":"10.1088","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7392-4111","authenticated-orcid":true,"given":"Alejandro","family":"Nieto Ramos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1897-3359","authenticated-orcid":true,"given":"Balu","family":"Krishnan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas V","family":"Alexopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Bingaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imad","family":"Najm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan C","family":"Bulacio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8037-9705","authenticated-orcid":true,"given":"Demitre","family":"Serletis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"jnead705fbib1","author":"Brunton","year":"2022","edition":"2nd edn"},{"key":"jnead705fbib2","author":"Bancaud","year":"1965"},{"key":"jnead705fbib3","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.3171\/2021.6.JNS204413","article-title":"Determinants of seizure outcome after resective surgery following stereoelectroencephalography","volume":"136","author":"Bulacio","year":"2021","journal-title":"J. Neurosurg."},{"key":"jnead705fbib4","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.3171\/2014.7.JNS132306","article-title":"The stereotactic approach for mapping epileptic networks: a prospective study of 200 patients: Clinical article","volume":"121","author":"Serletis","year":"2014","journal-title":"J. Neurosurg."},{"key":"jnead705fbib5","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1007\/s10439-011-0281-x","article-title":"Complexity in neuronal noise depends on network interconnectivity","volume":"39","author":"Serletis","year":"2011","journal-title":"Ann. Biomed. Eng."},{"key":"jnead705fbib6","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/9\/5\/056008","article-title":"Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics","volume":"9","author":"Serletis","year":"2012","journal-title":"J. Neural Eng."},{"key":"jnead705fbib7","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Phil. Trans. R. Soc. A"},{"key":"jnead705fbib8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneumeth.2015.10.010","article-title":"Extracting spatial\u2013temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition","volume":"258","author":"Brunton","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"jnead705fbib9","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab8910","article-title":"Neural decoding of electrocorticographic signals using dynamic mode decomposition","volume":"17","author":"Shiraishi","year":"2020","journal-title":"J. Neural Eng."},{"key":"jnead705fbib10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1017\/S0022112010001217","article-title":"Dynamic mode decomposition of numerical and experimental data","volume":"656","author":"Schmid","year":"2010","journal-title":"J. Fluid Mech."},{"key":"jnead705fbib11","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s00162-010-0203-9","article-title":"Applications of the dynamic mode decomposition","volume":"25","author":"Schmid","year":"2011","journal-title":"Theor. Comput. Fluid Dyn."},{"key":"jnead705fbib12","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1017\/S0022112009992059","article-title":"Spectral analysis of nonlinear flows","volume":"641","author":"Rowley","year":"2009","journal-title":"J. Fluid Mech."},{"key":"jnead705fbib13","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1137\/17M1125236","article-title":"Ergodic theory, dynamic mode decomposition and computation of spectral properties of the koopman operator","volume":"16","author":"Arbabi","year":"2017","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"jnead705fbib14","doi-asserted-by":"publisher","DOI":"10.1063\/1.4772195","article-title":"Applied Koopmanism","volume":"22","author":"Budi\u0161i\u0107","year":"2012","journal-title":"Chaos"},{"key":"jnead705fbib15","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac7256","article-title":"Epileptic electroencephalography classification using embedded dynamic mode decomposition","volume":"19","author":"Hellar","year":"2022","journal-title":"J. Neural Eng."},{"key":"jnead705fbib16","doi-asserted-by":"publisher","first-page":"481","DOI":"10.3390\/math8040481","article-title":"Pattern recognition in epileptic EEG signals via dynamic mode decomposition","volume":"8","author":"Seo","year":"2020","journal-title":"Mathematics"},{"key":"jnead705fbib17","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.bbe.2020.11.002","article-title":"Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum","volume":"41","author":"Karabiber Cura","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"jnead705fbib18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2011\/879716","article-title":"Brainstorm: a user-friendly application for meg\/eeg analysis","volume":"2011","author":"Tadel","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"jnead705fbib19","doi-asserted-by":"publisher","first-page":"391","DOI":"10.3934\/jcd.2014.1.391","article-title":"On dynamic mode decomposition: Theory and applications","volume":"1","author":"Tu","year":"2014","journal-title":"J. Comput. Dyn."},{"key":"jnead705fbib20","volume":"vol 181","author":"Trefethen","year":"1997"},{"key":"jnead705fbib21","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1073\/pnas.18.3.255","article-title":"Dynamica systems of continuous spectra","volume":"18","author":"Koopman","year":"1932","journal-title":"Proc. Natl Acad. Sci."},{"key":"jnead705fbib22","author":"Kutz","year":"2016"},{"key":"jnead705fbib23","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.clinph.2015.03.020","article-title":"Ictal infraslow activity in stereoelectroencephalography: Beyond the \u201cDC shift\u201d","volume":"127","author":"Thompson","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"jnead705fbib24","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/12\/2\/026011","article-title":"Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients","volume":"12","author":"Guirgis","year":"2015","journal-title":"J. Neural Eng."},{"key":"jnead705fbib25","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1093\/brain\/awn111","article-title":"Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG","volume":"131","author":"Bartolomei","year":"2008","journal-title":"Brain"},{"key":"jnead705fbib26","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1002\/ana.25583","article-title":"Multiple oscillatory push-pull antagonisms constrain seizure propagation","volume":"86","author":"Jiang","year":"2019","journal-title":"Ann. Neurol."},{"key":"jnead705fbib27","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3389\/fncir.2019.00081","article-title":"Inhibitory network bistability explains increased interneuronal activity prior to seizure onset","volume":"13","author":"Rich","year":"2020","journal-title":"Front. Neural Circuits"},{"key":"jnead705fbib28","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1093\/brain\/awx306","article-title":"A fingerprint of the epileptogenic zone in human epilepsies","volume":"141","author":"Grinenko","year":"2018","journal-title":"Brain"},{"key":"jnead705fbib29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.117838","article-title":"Neurovascular networks in epilepsy: Correlating ictal blood perfusion with intracranial electrophysiology","volume":"231","author":"Krishnan","year":"2021","journal-title":"NeuroImage"},{"key":"jnead705fbib30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004209","article-title":"Computational modeling of seizure dynamics using coupled neuronal networks: Factors shaping epileptiform activity","volume":"11","author":"Naze","year":"2015","journal-title":"PLoS Comput. Biol."},{"key":"jnead705fbib31","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1051\/epjnbp\/2017001","article-title":"Capturing time-varying brain dynamics","volume":"5","author":"Lehnertz","year":"2017","journal-title":"EPJ Nonlinear Biomed. Phys."},{"key":"jnead705fbib32","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","article-title":"A quantitative description of membrane current and its application to conduction and excitation in nerve","volume":"117","author":"Hodgkin","year":"1952","journal-title":"J. Physiol."},{"key":"jnead705fbib33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1523\/ENEURO.0485-18.2019","article-title":"The epileptor model: a systematic mathematical analysis linked to the dynamics of seizures, refractory status epilepticus and depolarization block","volume":"7","author":"Houssaini","year":"2020","journal-title":"eNeuro"},{"key":"jnead705fbib34","doi-asserted-by":"publisher","DOI":"10.1063\/5.0021420","article-title":"FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena","volume":"30","author":"Gerster","year":"2020","journal-title":"Chaos"},{"key":"jnead705fbib35","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.neuroimage.2015.03.055","article-title":"An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data","volume":"117","author":"Schirner","year":"2015","journal-title":"NeuroImage"},{"key":"jnead705fbib36","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.neuroimage.2016.04.049","article-title":"The virtual epileptic patient: Individualized whole-brain models of epilepsy spread","volume":"145","author":"Jirsa","year":"2017","journal-title":"NeuroImage"},{"key":"jnead705fbib37","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1126\/science.1099745","article-title":"Neuronal oscillations in cortical networks","volume":"304","author":"Buzs\u00e1ki","year":"2004","journal-title":"Science"},{"key":"jnead705fbib38","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2023.109175","article-title":"Personalized whole brain modeling of status epilepticus","volume":"142","author":"Dollomaja","year":"2023","journal-title":"Epilepsy Behav."},{"key":"jnead705fbib39","doi-asserted-by":"publisher","first-page":"5317","DOI":"10.1016\/j.jcp.2012.04.023","article-title":"An improved algorithm for balanced POD through an analytic treatment of impulse response tails","volume":"231","author":"Tu","year":"2012","journal-title":"J. Comput. Phys."},{"key":"jnead705fbib40","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1137\/15M1023543","article-title":"Multiresolution dynamic mode decomposition","volume":"15","author":"Kutz","year":"2016","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"jnead705fbib41","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s00332-012-9130-9","article-title":"Variants of dynamic mode decomposition: boundary condition, koopman and fourier analyses","volume":"22","author":"Chen","year":"2012","journal-title":"J. Nonlinear Sci."},{"key":"jnead705fbib42","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1137\/M1124176","article-title":"Variable Projection Methods for an Optimized Dynamic Mode Decomposition","volume":"17","author":"Askham","year":"2018","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"jnead705fbib43","first-page":"pp 2814","article-title":"Bayesian dynamic mode decomposition","author":"Takeishi","year":"2017"},{"key":"jnead705fbib44","first-page":"pp 8083","article-title":"Bayesian dynamic mode decomposition with variational matrix factorization","volume":"vol 35","author":"Kawashima","year":"2021"},{"key":"jnead705fbib45","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2021.0199","article-title":"Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification","volume":"380","author":"Sashidhar","year":"2022","journal-title":"Phil. Trans. R. Soc. A"},{"key":"jnead705fbib46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-024-06294-3","article-title":"Fast, accurate and interpretable decoding of electrocorticographic signals using dynamic mode decomposition","volume":"7","author":"Fukuma","year":"2024","journal-title":"Commun. Biol."},{"key":"jnead705fbib47","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac64c4","article-title":"Abnormal phase\u2013amplitude coupling characterizes the interictal state in epilepsy","volume":"19","author":"Fujita","year":"2022","journal-title":"J. Neural Eng."},{"key":"jnead705fbib48","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac520f","article-title":"Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone","volume":"19","author":"Nunez","year":"2022","journal-title":"J. Neural Eng."},{"key":"jnead705fbib49","doi-asserted-by":"publisher","first-page":"3932","DOI":"10.1073\/pnas.1517384113","article-title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","volume":"113","author":"Brunton","year":"2016","journal-title":"Proc. Natl Acad. Sci."},{"key":"jnead705fbib50","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.032916","article-title":"Nonlinear mode decomposition: A noise-robust, adaptive decomposition method","volume":"92","author":"Iatsenko","year":"2015","journal-title":"Phys. Rev. E"},{"key":"jnead705fbib51","volume":"vol 104","author":"Basseville","year":"1993"},{"key":"jnead705fbib52","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1016\/j.sigpro.2005.01.012","article-title":"Using penalized contrasts for the change-point problem","volume":"85","author":"Lavielle","year":"2005","journal-title":"Signal Process."},{"key":"jnead705fbib53","volume":"vol 243","author":"Brodsky","year":"2013"}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:17:37Z","timestamp":1724930257000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ad705f"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,8,29]]},"published-print":{"date-parts":[[2024,8,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/ad705f","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"Epileptic network identification: insights from dynamic mode decomposition of sEEG data","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-03-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-08-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-08-29","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}