  Neural oscillations are considered to be brain-specific signatures of
information processing and communication in the brain. They also reflect
pathological brain activity in neurological disorders, thus offering a basis
for diagnoses and forecasting. Epilepsy is one of the most common neurological
disorders, characterized by abnormal synchronization and desynchronization of
the oscillations in the brain. About one third of epilepsy cases are
pharmacoresistant, and as such emphasize the need for novel therapy approaches,
where brain stimulation appears to be a promising therapeutic option. The
development of brain stimulation paradigms, however, is often based on
generalized assumptions about brain dynamics, although it is known that
significant differences occur between patients and brain states. We developed a
framework to extract individualized predictive models of epileptic network
dynamics directly from EEG data. The models are based on the dominant coherent
oscillations and their dynamical coupling, thus combining an established
interpretation of dynamics through neural oscillations, with accurate
patient-specific features. We show that it is possible to build a direct
correspondence between the models of brain-network dynamics under periodic
driving, and the mechanism of neural entrainment via periodic stimulation. When
our framework is applied to EEG recordings of patients in status epilepticus (a
brain state of perpetual seizure activity), it yields a model-driven predictive
analysis of the therapeutic performance of periodic brain stimulation. This
suggests that periodic brain stimulation can drive pathological states of
epileptic network dynamics towards a healthy functional brain state.
