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TOWARDS SIMULATION-ASSISTED SEIZURE ONSET ZONE DETECTION
Daniel Müller-Komorowskaand 1 co-author
Okinawa Institute of Science and Technology
FENS Forum 2026 (2026)
Barcelona, Spain
Board PS05-09AM-393
Presentation
Date TBA
Board: PS05-09AM-393
Event Information
Poster Board
PS05-09AM-393
Poster
View posterAbstract
The brain areas that generate seizures in patients with epilepsy are called the seizure onset zone (SOZ). For many patients, surgical resection of the SOZ is the only reliable way to prevent seizures. Clinicians identify the SOZ using structural and functional data sources recorded between and during seizures. High-frequency oscillations (HFOs) between seizures are currently the most accurate SOZ marker. But many patients still experience seizures after these brain areas are removed.
We build spiking microcircuit simulators and use simulation-based inference to estimate parameters that reproduce the dynamics of interictal electrocorticogram recordings. These parameter estimates could then be used directly for SOZ detection or to generate synthetic training data for machine learning algorithms. We hope this approach can identify SOZ areas that HFOs do not.
We build spiking microcircuit simulators and use simulation-based inference to estimate parameters that reproduce the dynamics of interictal electrocorticogram recordings. These parameter estimates could then be used directly for SOZ detection or to generate synthetic training data for machine learning algorithms. We hope this approach can identify SOZ areas that HFOs do not.
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