Viktor Ší́p

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

This study links seizure spread on a patient-specific cortical surface to theta-alpha activity patterns using computational modeling in The Virtual Brain.

This paper introduces a data-driven method to infer neural mass models and parameters from brain data, showing it can recover original parameters and produce realistic dynamics in synthetic and resting-state fMRI data.

This study investigates whether theta-alpha activity patterns in seizure recordings are caused by spreading seizures, finding support for this hypothesis through simulations and comparisons with real patient data.