{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:02:14Z","timestamp":1780081334638,"version":"3.54.0"},"reference-count":42,"publisher":"Society for Neuroscience","issue":"5","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"vor","delay-in-days":188,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"name":"DOD | USA | AMS | U.S. Army Veterinary Corps","award":["PR100040"],"award-info":[{"award-number":["PR100040"]}]}],"content-domain":{"domain":["eneuro.org"],"crossmark-restriction":true},"short-container-title":["eNeuro"],"accepted":{"date-parts":[[2026,4,11]]},"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid percussion injury in male rats. We applied transfer learning to a neural-network trained and tested on three potentially distinct electroencephalographic phenotypes: (1) late-onset convulsive seizures associated with rare post-traumatic epilepsy, (2) early-onset convulsive seizures that often occurred after sham or injury treatment (independent of post-traumatic epilepsy), and (3) spike-wave discharges (SWDs), which occurred in both injured and sham-control rats. The neural network was able to detect seizure events within individual animals and across different cohorts and showed that early and late seizures have similar electroencephalographic phenotypes. Additionally, cross-over training and testing on SWDs from injured and sham-control rats distinguished a convulsive seizure phenotype from normal SWDs. Convolutional neural network modeling of the electroencephalogram can identify spectro-temporal phenotypes that reliably distinguish SWDs from convulsive seizures, indicating that (1) SWDs are normal, not to be falsely classified as nonconvulsive epileptic seizures; (2) the automated detection of convulsive seizures over months revealed rare post-traumatic epilepsy with low seizure frequency; (3) early and late (epileptic) seizures were indistinguishable within and across rats, thus suggesting similar underlying neuronal circuits and ictogenic pathways. This commonality, however, may also obscure important differences between seizure types; (4) convolutional neural network modeling may facilitate objective comparison of seizures within and between laboratories, supplementing subjective expert visual classification, and (5) the rarity of injury-induced epilepsy argues fluid percussion injury is poorly suited for effectively testing anti-epileptogenesis therapies.<\/jats:p>","DOI":"10.1523\/eneuro.0032-26.2026","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T17:50:20Z","timestamp":1777312220000},"page":"ENEURO.0032-26.2026","update-policy":"https:\/\/doi.org\/10.1523\/eneuro.crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Discriminates Seizures from Normal Brain Oscillations in the Electroencephalogram of a Rat Model of Post-traumatic Epilepsy"],"prefix":"10.1523","volume":"13","author":[{"given":"Sean","family":"Tatum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-4350","authenticated-orcid":false,"given":"Jeremy A.","family":"Taylor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katie","family":"Waldon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony J.","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"Jr","given":"Aaron","family":"Witt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachariah Z.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Slavka","family":"Ryger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Zayachkivsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5964-3375","authenticated-orcid":false,"given":"F. Edward","family":"Dudek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5474-1320","authenticated-orcid":false,"given":"Daniel S.","family":"Barth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199801013380104"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.2","unstructured":"Bangar S (2024) GoogLeNet: a deep dive into Google\u2019s neural network technology. Medium. Available at: https:\/\/medium.com\/@siddheshb008\/googlenet-a-deep-dive-into-googles-neural-network-technology-f588d1b49e55 [Accessed July 24, 2025]."},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.3","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.2803-15.2015"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.4","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2011.1954"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.5","doi-asserted-by":"publisher","DOI":"10.1016\/0920-1211(92)90029-S"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.6","doi-asserted-by":"publisher","DOI":"10.1111\/j.1535-7511.2010.01358.x"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.7","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awh038"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.8","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awh337"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.9","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awp217"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2015.05.022"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1535-7511.2010.01368.x"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.12","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4093-09.2010"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.13","doi-asserted-by":"publisher","DOI":"10.1111\/j.1535-7597.2004.04503.x"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.14","doi-asserted-by":"publisher","DOI":"10.1111\/epi.12854"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.15","first-page":"1208","article-title":"Lateral fluid percussion injury in rat - a clinically relevant model of post-traumatic epilepsy","volume":"22","author":"Kharatishvili","year":"2005","journal-title":"J Neurotrauma"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2006.03.012"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.17","doi-asserted-by":"crossref","first-page":"15807","DOI":"10.1038\/s41598-023-40628-1","article-title":"A machine learning approach to seizure detection in a rat model of post-traumatic epilepsy","volume":"13","author":"Kotloski","year":"2023","journal-title":"Sci Rep"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.18","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1111\/epi.16958","article-title":"Spike and wave discharges and fast ripples during posttraumatic epileptogenesis","volume":"62","author":"Kumar","year":"2021","journal-title":"Epilepsia"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.19","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/epi.17838","article-title":"Epilepsy phenotype and its reproducibility after lateral fluid percussion-induced traumatic brain injury in rats: multicenter EpiBioS4Rx study project 1","volume":"65","author":"Ndode-Ekane","year":"2024","journal-title":"Epilepsia"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.20","doi-asserted-by":"crossref","unstructured":"Nguyen A , Yosinski J , Clune J (2019) Understanding neural networks via feature visualization: a survey. Available at: http:\/\/arxiv.org\/abs\/1904.08939 [Accessed December 12, 2025].","DOI":"10.1007\/978-3-030-28954-6_4"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.21","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2014.01.004"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.22","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-3270(03)00075-X"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.23","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2006.23.241"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.24","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528-1167.2007.01393.x"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1111\/j.1528-1167.2008.02007.x","article-title":"From traumatic brain injury to posttraumatic epilepsy: what animal models tell us about the process and treatment options","volume":"50","author":"Pitk\u00e4nen","year":"2009","journal-title":"Epilepsia"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.26","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0919-15.2015"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.27","doi-asserted-by":"crossref","unstructured":"Roy Y , Banville H , Albuquerque I , Gramfort A , Falk TH , Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. Available at: http:\/\/arxiv.org\/abs\/1901.05498 [Accessed July 22, 2025].","DOI":"10.1088\/1741-2552\/ab260c"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.28","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.35.10.1406"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2009.04.006"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.30","first-page":"1","article-title":"Sleep spindles in rats with absence epilepsy","volume":"1","author":"Sitnikova","year":"2017","journal-title":"Sleep Spindles Cortical Up States"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.31","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00721.2017"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.32","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.bbi.2020.05.034","article-title":"Effects of immunization with heat-killed Mycobacterium vaccae on autism spectrum disorder-like behavior and epileptogenesis in a rat model of comorbid autism and epilepsy","volume":"88","author":"Smith","year":"2020","journal-title":"Brain Behav Immun"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eplepsyres.2009.05.006"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.34","doi-asserted-by":"crossref","unstructured":"Szegedy C , Liu W , Jia Y , Sermanet P , Reed S , Anguelov D , Erhan D , Vanhoucke V , Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139. Boston, MA, USA: IEEE ; Available at: http:\/\/ieeexplore.ieee.org\/document\/7298594\/ [Accessed March 8, 2022].","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.35","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.3235-16.2017"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.36","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1167-18.2019"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.37","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1113\/JP279778","article-title":"On the Yin and Yang of spike and waves","volume":"598","author":"Van Luijtelaar","year":"2020","journal-title":"J Physiol"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2006.03.002"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.39","doi-asserted-by":"publisher","DOI":"10.1038\/nn1107"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.40","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0980-08.2009"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.41","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s10462-025-11441-1","article-title":"Neural networks for epilepsy detection and prediction with EEG signals: a systematic review","volume":"59","author":"Wu","year":"2025","journal-title":"Artif Intell Rev"},{"key":"2026052911101652000_13.5.ENEURO.0032-26.2026.42","doi-asserted-by":"crossref","first-page":"1468967","DOI":"10.3389\/fnins.2024.1468967","article-title":"A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning","volume":"18","author":"Zhang","year":"2024","journal-title":"Front Neurosci"}],"container-title":["eneuro"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1523\/ENEURO.0032-26.2026","content-type":"unspecified","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1523\/ENEURO.0032-26.2026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:10:24Z","timestamp":1780078224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.eneuro.org\/lookup\/doi\/10.1523\/ENEURO.0032-26.2026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,27]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5,11]]},"published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1523\/ENEURO.0032-26.2026"],"URL":"https:\/\/doi.org\/10.1523\/eneuro.0032-26.2026","relation":{},"ISSN":["2373-2822"],"issn-type":[{"value":"2373-2822","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,27]]}}}