A Bayesian switching linear dynamical system for estimating seizure chronotypes

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dc.citation.articleNumbere2200822119en_US
dc.citation.issueNumber46en_US
dc.citation.journalTitleProceedings of the National Academy of Sciencesen_US
dc.citation.volumeNumber119en_US
dc.contributor.authorWang, Emily T.en_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorHaneef, Zulfien_US
dc.contributor.authorMoss, Roberten_US
dc.contributor.authorRao, Vikram R.en_US
dc.contributor.authorChiang, Sharonen_US
dc.date.accessioned2022-12-13T19:11:29Zen_US
dc.date.available2022-12-13T19:11:29Zen_US
dc.date.issued2022en_US
dc.description.abstractEpilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.en_US
dc.identifier.citationWang, Emily T., Vannucci, Marina, Haneef, Zulfi, et al.. "A Bayesian switching linear dynamical system for estimating seizure chronotypes." <i>Proceedings of the National Academy of Sciences,</i> 119, no. 46 (2022) National Academy of Sciences: https://doi.org/10.1073/pnas.2200822119.en_US
dc.identifier.digitalpnas-2200822119en_US
dc.identifier.doihttps://doi.org/10.1073/pnas.2200822119en_US
dc.identifier.urihttps://hdl.handle.net/1911/114111en_US
dc.language.isoengen_US
dc.publisherNational Academy of Sciencesen_US
dc.rightsThis article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleA Bayesian switching linear dynamical system for estimating seizure chronotypesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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