From the Epileptor to the Virtual Epileptic Patient

Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. We analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the predominant seizure class we developed a generic model called Epileptor. We show that only five state variables linked by integral-differential equations are sufficient to describe the onset, time course and offset of ictal-like discharges as well as their recurrence. We propose that normal and ictal activities coexist: a separatrix acts as a barrier (or seizure threshold) between these states. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Finally, we identified several possible biophysical parameters contributing to the five state variables in our model system.

Epileptor and the seizure taxonomy are precious to guide modeling and translational research by identifying universal rules. Nevertheless individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. Building on the epileptor theoretical developments, we have recently proposed a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. We will illustrate here the example of a patient with bitemporal epilepsy, showing step by step how to build a Virtual Epileptic Patient (VEP) brain model of seizure propagation, validating it against the patient’s empirical stereotactic EEG (SEEG) data and demonstrating how to develop novel personalized strategies towards therapy and intervention.

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