On-Off Intermittency in Time Series of Spontaneous Paroxysmal Activity in Rats with Genetic Absence Epilepsy

Dynamic behavior of complex neuronal ensembles is a topic comprising a streamline of current researches worldwide. In this article we study the behavior manifested by epileptic brain, in the case of spontaneous non-convulsive paroxysmal activity. For this purpose we analyzed archived long-term recording of paroxysmal activity in animals genetically susceptible to absence epilepsy, namely WAG/Rij rats. We first report that the brain activity alternated between normal states and epilepsy paroxysms is the on-off intermittency phenomenon which has been observed and studied earlier in the different nonlinear systems.Comment: 11 pages, 6 figure

Regularities of Alternate Behavior in Spontaneous Nonconvulsive Seizure Activity in Rats

The study of the complicated behavior of ensembles of neuronal elements is of great interest. In this connection, the brain, which is a very complicated neuronal ensemble, is an interesting object for investigation. The brain research is of theoretical interest and has a great importance because the study of fundamental laws of brain function helps to find the reasons of origin and development of pathologies in the central nervous system Processes in the brain are often studied using approaches based on the construction of relatively simple models of single neurons Another approach is the study of experimental time series and their analysis by methods of nonlinear dynamics The purpose of this work was the diagnosis of the dynamic behavior in the case of spontaneous seizure activity of the nonconvulsive type. In our experiments, we used long-term recording of paroxysmal activity of rats genetically predisposed to absentia epilepsy (WAG/Rij rats). Seizure activity was expert assessed using the electrocorticogram (ECoG) recorded from intact freely moving animals with chronically implanted electrodes, as described in We found that the interlace of seizure activity and normal functioning of the brain could be adequately described in terms of on-off alternatio