Computational modeling allows unsupervised classification of epileptic brain states across species

0574286 - ÚI 2024 RIV US eng J - Journal Article
Dallmer-Zerbe, Isa - Jajcay, Nikola - Chvojka, J. - Janča, R. - Ježdík, P. - Kršek, P. - Marusič, P. - Jiruška, P. - Hlinka, Jaroslav
Computational modeling allows unsupervised classification of epileptic brain states across species.
Scientific Reports. Roč. 13, č. 1 (2023), č. článku 13436. ISSN 2045-2322. E-ISSN 2045-2322
R&D Projects: GA ČR(CZ) GA21-32608S; GA ČR(CZ) GA21-17564S; GA MZd(CZ) NU21-08-00533
Institutional support: RVO:67985807
Keywords : Brain * Computer Simulation * Electrocorticography * Epilepsy * Humans * Rats * Classification * Computational neuroscience
OECD category: Neurosciences (including psychophysiology
Impact factor: 4.6, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.1038/s41598-023-39867-z

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.
Permanent Link: https://hdl.handle.net/11104/0344628