Abstract
Epilepsy is a neurological disorder characterized by the recurrence
of seizures. It affects 50 million people worldwide. Although a considerable
number of new antiepileptic drugs with reduced side effects and toxicity
have been introduced since the 1950s, 30% of patients remain pharmacoresistant.
Although epilepsy research is making progress, advances in understanding
drug resistance have been hampered by the complexity of the underlying
neuronal systems responsible for epileptic activity. In such systems
where short- or long-term plasticity plays a role, pathophysiological
alterations may take place at subcellular (i.e., membrane ion channels
and neurotransmitter receptors), cellular (neurons), tissular (networks
of neurons) and regional (networks of networks of neurons) scales.
In such a context, the demand for integrative approaches is high
and neurocomputational models become recognized tools for tackling
the complexity of epileptic phenomena. The purpose of this report
is to provide an overview on computational modeling as a way of structuring
and interpreting multimodal data recorded from the epileptic brain.
Some examples are briefly described, which illustrate how computational
models closely related with either experimental or clinical data
can markedly advance our understanding of essential issues in epilepsy
such as the transition from background to seizure activity. A commentary
is also made on the potential use of such models in the study of
therapeutic strategies such as rational drug design or electrical
stimulations.
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