Classifying seizure generation mechanisms: A critical transitions framework

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dc.contributor.authorFlynn, Andrew
dc.contributor.authorMcCafferty, Cian
dc.contributor.authorLehnertz, Klaus
dc.contributor.authorDavid, François
dc.contributor.authorCrunelli, Vincenzo
dc.contributor.authorMarnane, William P.
dc.contributor.authorWieczorek, Sebastian
dc.date.accessioned2026-01-23T16:11:23Z
dc.date.available2026-01-23T16:11:23Z
dc.date.issued2025-11-25
dc.description.abstractUnderstanding how the brain switches from normal activity to an epileptic seizure is essential for improving seizure therapy, yet the underlying mechanisms remain largely unknown. In particular, seizure onset can be described as a critical transition (CT), but there is no consensus on whether (i) bifurcation-induced, (ii) noise-induced, or (iii) bifurcation/noise-induced CTs are responsible. To clarify this, we develop a versatile CT-classification framework that can be applied to seizures in both animals and humans. First, we identify a canonical mathematical model which displays CTs that closely resemble voltage recordings of real seizures and can be of the three types mentioned above. We then identify distinctive properties of each CT-type in the model's output and use them to train a machine learning CT-type classifier. Finally, we apply the model-trained classifier to voltage recordings from epileptic rodents. We find that the largest proportion of analysed seizures are classified as noise-induced CTs. This challenges the conventional view that seizures are predominantly bifurcation-induced and could inform new therapeutic strategies for seizures.en
dc.format.extent42
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidFlynn, Andrew§0000-0002-4968-8972
dc.identifier.authororcidMcCafferty, Cian§0000-0001-5206-4450
dc.identifier.authororcidLehnertz, Klaus
dc.identifier.authororcidDavid, François
dc.identifier.authororcidCrunelli, Vincenzo
dc.identifier.authororcidMarnane, William P.§0000-0002-5039-1498
dc.identifier.authororcidWieczorek, Sebastian§0000-0003-0090-7836
dc.identifier.citationFlynn, A, McCafferty, C, Lehnertz, K, David, F, Crunelli, V, Marnane, W P & Wieczorek, S 2025 'Classifying seizure generation mechanisms : A critical transitions framework' arXiv, pp. 1-42. https://doi.org/10.48550/arXiv.2511.20522
dc.identifier.doi10.48550/arXiv.2511.20522
dc.identifier.endpage42
dc.identifier.otherArXiv: http://arxiv.org/abs/2511.20522v1
dc.identifier.otherORCID: /0000-0002-4968-8972/work/203086396
dc.identifier.otherORCID: /0000-0001-5206-4450/work/209189134
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18454
dc.language.isound
dc.publisherarXiv
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectmath.DS
dc.subject[Maths]
dc.titleClassifying seizure generation mechanisms: A critical transitions framework
dc.typeArticle (preprint)
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