Critical slowing down as a biomarker for seizure susceptibility

0524739 - FGÚ 2021 RIV GB eng J - Journal Article
Maturana, M. I. - Meisel, Ch. - Dell, K. - Karoly, P. J. - D´Souza, W. - Grayden, D. B. - Burkitt, A. N. - Jiruška, Přemysl - Kudláček, Jan - Hlinka, Jaroslav - Cook, M. J. - Kuhlmann, L. - Freestone, D. R.
Critical slowing down as a biomarker for seizure susceptibility.
Nature Communications. Roč. 11, č. 1 (2020), č. článku 2172. E-ISSN 2041-1723
R&D Projects: GA MZd(CZ) NV17-28427A
Institutional support: RVO:67985823 ; RVO:67985807
Keywords : epilepsy * seizure forecasting * critical slowing * early warning signal * critical transition * epileptic seizures * neuronal avalanches * long-term * discharges * prediction * resilience * melatonin * systems * onset * risk
OECD category: Neurosciences (including psychophysiology; Neurosciences (including psychophysiology (UIVT-O)
Impact factor: 14.919, year: 2020
Method of publishing: Open access
https://www.nature.com/articles/s41467-020-15908-3

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.
Permanent Link: http://hdl.handle.net/11104/0309042