Critical slowing down as a biomarker for seizure susceptibility

被引:0
作者
Matias I. Maturana
Christian Meisel
Katrina Dell
Philippa J. Karoly
Wendyl D’Souza
David B. Grayden
Anthony N. Burkitt
Premysl Jiruska
Jan Kudlacek
Jaroslav Hlinka
Mark J. Cook
Levin Kuhlmann
Dean R. Freestone
机构
[1] The University of Melbourne,Department of Medicine, St Vincent’s Hospital
[2] Seer Medical,Department of Neurology
[3] University Clinic Carl Gustav Carus,Graeme Clark Institute
[4] Boston Children’s Hospital,Department of Biomedical Engineering
[5] The University of Melbourne,Department of Physiology, Second Faculty of Medicine
[6] The University of Melbourne,Department of Developmental Epileptology, Institute of Physiology
[7] Charles University,Department of Circuit Theory, Faculty of Electrical Engineering
[8] Czech Academy of Sciences,Faculty of Information Technology
[9] Czech Technical University in Prague,Centre for Human Psychopharmacology
[10] Institute of Computer Science of the Czech Academy of Sciences,undefined
[11] National Institute of Mental Health,undefined
[12] Monash University,undefined
[13] Swinburne University of Technology,undefined
来源
Nature Communications | / 11卷
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摘要
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.
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