Epilepsy surgery is the most efficient therapy for patients with pharmaco-resistant epilepsy. Surgical treatment of pharmaco-resistant MR-negative/MR multi-lesion epilepsy is a major problem. The new AZV grant will be focused on predicting surgical outcome of patients with epilepsy, using advanced diagnostic methods. For prediction will be used: PET, SISCOM (ictal and interictal SPECT), EEG source imaging and power spectral entropy, sample entropy, permutation entropy and robust empirical permutation entropy. In MRI Gray Matter Volume, ASL (arterial spine labelling), ReHo (regional homogeneity), DWI (diffusion tensor imaging), DKI (diffusion kurtosis imaging). Machine learning (ML) methods will be used to build robust classifier capable to predict surgical outcome based on multimodal data. The multimodal fusion is vital for evaluating the extent and precision of the surgical intervention in targeting epileptogenic zones. Post-surgery structural MRIs will be tested to delineate the resected cavity, determining the areas excised during the operation and considering the surgical impact on epilepsy.
Other methods may also increase probability of epileptic zone detection and precision of the prediction. Intracranial video-EEG (SEEG) is necessary in epilepsy surgery candidates if scalp and semi-invasive recordings do not yield enough information about the location and extent of the epileptogenic zone. Psychology evaluation will assess memory, attention, executive functions, language, visuospatial skills, intelligence, depression, anxiety, and quality of life.
Project Aims: Optimal combination of techniques for prediction of surgical outcome in MR negative/MR multi-lesion epilepsies will be tested. Patients with previously unremarkable MRI scans should be rescanned using novel advanced methods and indicated to epilepsy surgery. Ultimately, this study aims to work with an optimal combination of diagnostic techniques for identifying and treating the seizure onset zone in complex epilepsy cases.

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