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Computational modeling allows unsupervised classification of epileptic brain states across species
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SYSNO ASEP 0574286 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Computational modeling allows unsupervised classification of epileptic brain states across species Author(s) Dallmer-Zerbe, Isa (UIVT-O) RID, SAI
Jajcay, Nikola (UIVT-O) RID, ORCID, SAI
Chvojka, J. (CZ)
Janča, R. (CZ)
Ježdík, P. (CZ)
Kršek, P. (CZ)
Marusič, P. (CZ)
Jiruška, P. (CZ)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCIDNumber of authors 9 Article number 13436 Source Title Scientific Reports. - : Nature Publishing Group - ISSN 2045-2322
Roč. 13, č. 1 (2023)Number of pages 13 s. Language eng - English Country US - United States Keywords Brain ; Computer Simulation ; Electrocorticography ; Epilepsy ; Humans ; Rats ; Classification ; Computational neuroscience OECD category Neurosciences (including psychophysiology R&D Projects GA21-32608S GA ČR - Czech Science Foundation (CSF) GA21-17564S GA ČR - Czech Science Foundation (CSF) NU21-08-00533 GA MZd - Ministry of Health (MZ) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 001051244000025 EID SCOPUS 85168354473 DOI 10.1038/s41598-023-39867-z Annotation Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2024 Electronic address https://dx.doi.org/10.1038/s41598-023-39867-z
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