Computational modeling allows unsupervised classification of epileptic brain states across species
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39867-z |
| _version_ | 1827634764810551296 |
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| author | Isa Dallmer-Zerbe Nikola Jajcay Jan Chvojka Radek Janca Petr Jezdik Pavel Krsek Petr Marusic Premysl Jiruska Jaroslav Hlinka |
| author_facet | Isa Dallmer-Zerbe Nikola Jajcay Jan Chvojka Radek Janca Petr Jezdik Pavel Krsek Petr Marusic Premysl Jiruska Jaroslav Hlinka |
| author_sort | Isa Dallmer-Zerbe |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T15:17:50Z |
| format | Article |
| id | doaj.art-bd1c23abf47d4c978d093500fc58ce04 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:17:50Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bd1c23abf47d4c978d093500fc58ce042023-11-26T12:58:41ZengNature PortfolioScientific Reports2045-23222023-08-0113111310.1038/s41598-023-39867-zComputational modeling allows unsupervised classification of epileptic brain states across speciesIsa Dallmer-Zerbe0Nikola Jajcay1Jan Chvojka2Radek Janca3Petr Jezdik4Pavel Krsek5Petr Marusic6Premysl Jiruska7Jaroslav Hlinka8Department of Complex Systems, Institute of Computer Science, Czech Academy of SciencesDepartment of Complex Systems, Institute of Computer Science, Czech Academy of SciencesDepartment of Physiology, Second Faculty of Medicine, Charles UniversityDepartment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in PragueDepartment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in PragueDepartment of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles UniversityDepartment of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles UniversityDepartment of Physiology, Second Faculty of Medicine, Charles UniversityDepartment of Complex Systems, Institute of Computer Science, Czech Academy of SciencesAbstract 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.https://doi.org/10.1038/s41598-023-39867-z |
| spellingShingle | Isa Dallmer-Zerbe Nikola Jajcay Jan Chvojka Radek Janca Petr Jezdik Pavel Krsek Petr Marusic Premysl Jiruska Jaroslav Hlinka Computational modeling allows unsupervised classification of epileptic brain states across species Scientific Reports |
| title | Computational modeling allows unsupervised classification of epileptic brain states across species |
| title_full | Computational modeling allows unsupervised classification of epileptic brain states across species |
| title_fullStr | Computational modeling allows unsupervised classification of epileptic brain states across species |
| title_full_unstemmed | Computational modeling allows unsupervised classification of epileptic brain states across species |
| title_short | Computational modeling allows unsupervised classification of epileptic brain states across species |
| title_sort | computational modeling allows unsupervised classification of epileptic brain states across species |
| url | https://doi.org/10.1038/s41598-023-39867-z |
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