Dynamical Modeling Of Epileptogenic Zones In Focal Epilepsy And Virtual Resection Intervention Study

Epilepsy is a chronic neurological disorder affecting over 70 million people worldwide.Among them,patients with focal origins make up around 60%,making it the primary focus of epilepsy research.Despite drug therapy,about one-third of patients struggle to control their seizures effectively,leading to refractory epilepsy.Epilepsy surgery serves as the primary treatment for drug-resistant epilepsy.However,due to the unclear nature of epileptogenic mechanisms and the complexity of locating epileptogenic foci,many patients continue to suffer from uncontrolled seizures post-surgery.In this thesis,we present a network model of the epileptogenic zone of focal epilepsy and a spatial-temporal analysis method based on optical flow estimation to better understand the epileptogenic mechanisms and identify potential localization markers for epileptogenic foci.Our findings include:Firstly,we constructed a network model of the focal epileptogenic region with micro column-column-cortex structure,divided into background,bistable,and oscillatory states through parameter spatial exploration.Adjusting parameters in the background and bistable states can simulate two seizure origination modes: Low Amplitude Fast Oscillations(LAF)and High Amplitude Spikes(HAS).Secondly,we apply an analytical framework based on optical flow estimation to investigate the complex spatial-temporal dynamics of epilepsy propagation,focusing on the source-sink mode.We discovered a dominant pattern during epilepsy propagation that varies significantly depending on epileptogenic foci and connectome.This confirms the synchronous nature of seizures and the highly individualized characteristics of epileptic patients.By analyzing the source-sink pattern in the dominant pattern,we found a high correlation between sources dominated by unstable critical points and epileptogenic foci,which can be used as a marker to localize epileptogenic foci.Thirdly,we performed virtual resection intervention for three seizure types: HAS with a single epileptogenic focus,LAF with multiple epileptogenic foci,and HAS with multiple epileptogenic foci.The resection area was a circular area generated using the unstable critical points as the seed points.We found significantly better intervention effect in the LAF than in the HAS,and in the HAS,intervention effect in single foci than in multiple foci.Additionally,we discovered that detecting unstable critical points with reduced excitability of surrounding tissues improved intervention effect in the HASmultiple type.Our study suggests that there is a clear source-sink mode of epileptic propagation in the epileptogenic zone,and the unstable critical points of the dominant pattern may serve as potential markers for localizing epileptogenic foci.Using these unstable critical points as seed points to generate resection areas for virtual resection intervention can help alleviate or eliminate seizures.This research integrates computational neural modeling techniques and applies an analytical framework of optical flow estimation to analyze the spatial-temporal patterns of epileptogenic propagation in the epileptogenic region thoroughly.We focus on the effects of factors like seizure onset patterns,the number and distribution patterns of epileptogenic foci on virtual resection intervention,providing model support and reference for localizing epileptogenic foci and assessing epilepsy surgery results.