Maxime O Baud1,2, Timothée Proix3, Vikram R Rao4, Kaspar Schindler1. 1. Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern. 2. Wyss Center for Bio- and Neuro-engineering. 3. Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland. 4. Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, California, USA.
Abstract
PURPOSE OF REVIEW: Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS: Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY: The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.
PURPOSE OF REVIEW: Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS: Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY: The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.
Authors: Timothée Proix; Wilson Truccolo; Marc G Leguia; Thomas K Tcheng; David King-Stephens; Vikram R Rao; Maxime O Baud Journal: Lancet Neurol Date: 2020-12-17 Impact factor: 44.182
Authors: Daniel E Payne; Katrina L Dell; Phillipa J Karoly; Vaclav Kremen; Vaclav Gerla; Levin Kuhlmann; Gregory A Worrell; Mark J Cook; David B Grayden; Dean R Freestone Journal: Epilepsia Date: 2020-12-30 Impact factor: 6.740
Authors: Sharon Chiang; Ankit N Khambhati; Emily T Wang; Marina Vannucci; Edward F Chang; Vikram R Rao Journal: Brain Stimul Date: 2021-02-06 Impact factor: 8.955
Authors: Philippa J Karoly; Vikram R Rao; Maxime O Baud; Nicholas M Gregg; Gregory A Worrell; Christophe Bernard; Mark J Cook Journal: Nat Rev Neurol Date: 2021-03-15 Impact factor: 42.937