Gilles van Luijtelaar1, Annika Lüttjohann2, Vladimir V Makarov3, Vladimir A Maksimenko3, Alexei A Koronovskii3, Alexander E Hramov3. 1. Donders Centre for Cognition, Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands. Electronic address: g.vanluijtelaar@donders.ru.nl. 2. Donders Centre for Cognition, Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Institute of Physiology I, Westfälische Wilhelms University Münster, Münster, Germany. 3. REC "Nonlinear Dynamics of Complex Systems", Saratov State Technical University, Politechnicheskaja 77, Saratov, 410028, Russia; Faculty of Nonlinear Processes, Saratov State University, Astrakhanskaya 83, Saratov, 410012, Russia.
Abstract
BACKGROUND: Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. METHOD: Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. RESULTS: Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. CONCLUSIONS: The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control.
BACKGROUND: Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. METHOD: Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. RESULTS: Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. CONCLUSIONS: The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control.
Authors: Vladimir A Maksimenko; Sabrina van Heukelum; Vladimir V Makarov; Janita Kelderhuis; Annika Lüttjohann; Alexey A Koronovskii; Alexander E Hramov; Gilles van Luijtelaar Journal: Sci Rep Date: 2017-05-29 Impact factor: 4.379
Authors: Björn Budde; Vladimir Maksimenko; Kelvin Sarink; Thomas Seidenbecher; Gilles van Luijtelaar; Tim Hahn; Hans-Christian Pape; Annika Lüttjohann Journal: eNeuro Date: 2022-02-09