Computational modelling and electrical stimulation for epilepsy surgery

Abstract

Epilepsy surgery may provide a cure for patients with focal epilepsy. Where epilepsy surgery was traditionally focused on removing pathological cortex in the anatomic sense, recent developments suggests that epilepsy is also a brain network disease. Computational models for epilepsy offer a framework to study the joint effect of local, intrinsic epileptogenicity and network interaction and allow to incorporate patient-specific information. Single pulse electrical stimulation (SPES) might be an interesting technique to obtain this patient-specific information. SPES evokes early responses, representing connectivity, and delayed responses which are a biomarker for epileptogenicity. In this thesis the added value of combining computational network models and SPES for epilepsy surgery is investigated. First, we study the effects of surgery on the seizure rate in simple, small, computational network models. Removal of normal populations located at a crucial spot in the network, is typically more effective in reducing seizure rate than removing a hyperexcitable population. Second, we compare connectivity probed using SPES with two traditional methods for connectivity. Strong connections in the cross-correlation network form more or less a subset of the SPES network, while Granger causality and SPES networks are related more weakly. Connectivity known to exist between two major hubs in the language circuit, is only found in SPES networks. Next, we study how SPES may trigger delayed responses (DRs) using a neural mass model. This model suddenly generates large, non-linear responses, mimicking DRs, when input to a neural mass falls below a threshold. In combination with noisy background input this threshold explains the typical stochastic appearance of DRs. Using slow-fast analysis we reveal the origin of this sudden transition. Computational network models offer an interesting framework to explore effects of epilepsy surgery. SPES networks are of interest to use in these models as they incorporate more physiological long-range connections compared to two traditional methods. Delayed responses to SPES are a candidate marker to tune local excitability in these models, with the advantage that they can be probed actively. In conclusion, SPES and patient-specific computational network models form a promising combination that have great potential to improve epilepsy surgery.