This dataset describes the ESSES framework, a personalized computational model to aid the planning of epilepsy surgery. ESSES combines epidemic spreading models to depict seizure propagation with a virtual resection method to simulate the effect of a given surgery. An individualized model is built for each patient integrating patient-specific data such as MEG brain connectivity and multimodal presurgical data into seed-probability maps. The individualized models can be used to test different resection strategies and predict outcomes. The project demonstrates the potential of individualized computational models to improve epilepsy surgery outcomes and has been successfully validated in a pseudo-prospective study.

Date Submitted: 2023-04-17