  Information transmission in the human brain is a fundamentally dynamic
network process. In partial epilepsy, this process is perturbed and highly
synchronous seizures originate in a local network, the so-called epileptogenic
zone (EZ), before recruiting other close or distant brain regions. We studied
patient-specific brain network models of 15 drug-resistant epilepsy patients
with implanted stereotactic electroencephalography (SEEG) electrodes. Each
personalized brain model was derived from structural data of magnetic resonance
imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes
equipped with region specific neural mass models capable of demonstrating a
range of epileptiform discharges. Each patients virtual brain was further
personalized through the integration of the clinically hypothesized EZ.
Subsequent simulations and connectivity modulations were performed and
uncovered a finite repertoire of seizure propagation patterns. Across patients,
we found that (i) patient-specific network connectivity is predictive for the
subsequent seizure propagation pattern; (ii)seizure propagation is
characterized by a systematic sequence of brain states; (iii) propagation can
be controlled by an optimal intervention on the connectivity matrix; (iv) the
degree of invasiveness can be significantly reduced via the here proposed
seizure control as compared to traditional resective surgery. To stop seizures,
neurosurgeons typically resect the EZ completely. We showed that stability
analysis of the network dynamics using graph theoretical metrics estimates
reliably the spatiotemporal properties of seizure propagation. This suggests
novel less invasive paradigms of surgical interventions to treat and manage
partial epilepsy.
