Reduction of intracellular calcium removal rate can explain changes in seizure dynamics: studies in neuronal network models

• Published in: Epilepsy research 2003-12, Vol.57 (2), p.95-109
• Main Authors: Kudela, Pawel, Franaszczuk, Piotr J, Bergey, Gregory K
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Calcium - metabolism; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Intracellular calcium removal; Intracellular Fluid - metabolism; Medical sciences; Nervous system (semeiology, syndromes); Neural Networks (Computer); Neurology; Neuronal network models; Seizure dynamics; Seizures - metabolism
• ISSN: 0920-1211; 1872-6844
• DOI: 10.1016/j.eplepsyres.2003.10.009

Complex partial seizures originating from mesial temporal structures are characterized by relatively short durations of organized rhythmic activity (ORA) of 5–8 Hz, typically lasting less than 60 s. Previous investigations into seizure dynamics have revealed that this ORA undergoes a monotonic decline prior to seizure evolution into intermittent bursting and subsequent seizure termination. Large neural network models of simplified single-compartment neurons were employed to address the hypothesis that changes in the free intracellular calcium ([Ca 2+] i) removal rate during network bursting can result in the alterations of rhythmic seizure activity similar to that observed in recordings from humans. Both exponential and linear models of decreasing calcium removal rates resulted in changes in the predominant frequency of network bursting very similar in frequency and time course to those seen in human intracranial recordings. This supports the concept that changes in [Ca 2+] i removal can explain this important network behavior, while not excluding alternative hypotheses. Identifying potential mechanisms underlying the dynamic changes seen in epileptogenic activity in large neural networks can provide important insights into seizure evolution and termination. Model neural network ensembles are attractive systems to address these questions that are difficult to investigate in biological preparations.