Critical slowing down as a biomarker for seizure susceptibility
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu kazuistiky, časopisecké články, práce podpořená grantem
PubMed
32358560
PubMed Central
PMC7195436
DOI
10.1038/s41467-020-15908-3
PII: 10.1038/s41467-020-15908-3
Knihovny.cz E-zdroje
- MeSH
- algoritmy MeSH
- biologické markery MeSH
- elektrokortikografie MeSH
- epilepsie parciální diagnóza MeSH
- lidé MeSH
- modely neurologické MeSH
- mozek patofyziologie MeSH
- rizikové faktory MeSH
- záchvaty diagnóza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- kazuistiky MeSH
- práce podpořená grantem MeSH
- Názvy látek
- biologické markery MeSH
The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.
Boston Children's Hospital Boston MA USA
Centre for Human Psychopharmacology Swinburne University of Technology Hawthorn Victoria Australia
Department of Biomedical Engineering The University of Melbourne Melbourne Australia
Department of Medicine St Vincent's Hospital The University of Melbourne Melbourne Australia
Department of Neurology University Clinic Carl Gustav Carus Dresden Germany
Department of Physiology 2nd Faculty of Medicine Charles University Prague Czech Republic
Faculty of Information Technology Monash University Clayton Victoria Australia
Graeme Clark Institute The University of Melbourne Melbourne Australia
Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic
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