Speaker
Description
Computational modeling and dynamical systems theory enhance our understanding of epileptic seizure dynamics and potentially provide new intervention strategies. Models like the Virtual Epileptic Patient (VEP) aim to make patient-specific predictions about the epileptogenic zone and subsequently identify targets for epilepsy surgery. The VEP is based on individual neuroimaging data, electrophysiological measurements of seizures, and a dynamical model of neural activity to construct a full brain network model of the patient. Currently, the VEP uses low-resolution neural mass models (NMM) to represent individual regions of the brain network. NMMs approximate neural activity within a single point, thus ignoring the spatial extent of the neural tissue and local propagation phenomena like the traveling waves observed in epileptic seizures. Therefore, we extended the model to high-resolution neural fields (NF) that represent the cortical sheet in its spatial extent.
We developed a high-resolution, personalized computational model for a patient with drug-resistant focal epilepsy in the left temporal lobe. Using T1-weighted and diffusion MRI combined with tractography, we reconstructed the cortical surface and estimated connections between surface points at a 1mm³ scale. Seizure dynamics were simulated using the two-dimensional Epileptor model in an excitable regime to test reentry effects, as suggested by empirical in-vivo and in-vitro studies. We applied the dynamical model to the cortical surface and explored the parameter space across coupling strengths and reentry frequencies. This approach revealed self-limiting excitations, spiral waves, and sustained reentry excitation. To terminate reentry, we tested two intervention strategies used in epilepsy treatment in our model: virtual thermocoagulation, which involved lesioning fiber tracts in the white matter to modify cortical connectivity, and virtual phase-dependent stimulation via virtually implanted electrodes.
Future research should focus on fine-tuning model parameters to match individual empirical data and optimize interventions.
