2008 Special IssueNeuronal population oscillations of rat hippocampus during epileptic seizures
Section snippets
Hippocampal neuronal populations
In this study, the rat tetanus toxin model of focal epilepsy is applied to study the neuronal oscillations in CA1/CA3 of rat hippocampus. Recordings were made during previous studies of this model (Finnerty and Jefferys, 2000, Finnerty and Jefferys, 2002). Methods were described in the previous reports, but briefly, male Sprague-Dawley rats (280–400 g) were anaesthetised with halothane. Bipolar recording electrodes (twisted Teflon-coated stainless steel wire with the tips separated 250–350 μm
EMD of neuronal populations
In this study, we examine the neuronal populations of rat hippocampal areas CA1 and CA3. The EEG recording (Left CA3) in Fig. 1 describes a typical trace from pre-ictal towards ictal state. To identify the dynamical change of neuronal oscillations at the pre-ictal, seizure onset and ictal state, I-II-III segments are extracted for further analysis. The EMD of three segments are plotted in Fig. 1B. It can be seen that a neuronal population oscillation in the hippocampus is composed of IMFs that
Discussion
In this study, the tetanus toxin model of epilepsy is applied on the behaving rats. EMD is used to decompose the neuronal population oscillations from right and left CA1 and CA3. By the observations of 9 seizures from 6 rats with this method, some findings are below: (i) A neuronal population oscillation is composed of several relaxation oscillations. The frequency distribution of neuronal oscillations shows that the difference of dynamic activity between the pre-ictal and ictal stage is
Acknowledgements
This work was partially supported by National Natural Science Foundation of China (60575012), Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP, 20060216003) and Cercia, The University of Birmingham, UK.
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2019, Biocybernetics and Biomedical EngineeringCitation Excerpt :The results obtained in this study provide considerable information in terms of the studies mentioned, especially regarding the discriminability of stimulus types. Filtering studies have been carried out using various signal types [25]; however, cognitive-based EEG signals have their own unique characteristics. Classical methods were selected as stimulation system because TMS and tDCS like trend systems' standards are ambiguous and effects over physiological signals couldn’t be discovered clearly [8–10].