Wessel Woldman

The paper examines if baseline EEG network metrics correlate with resilience to sleep deprivation, finding strong associations between certain graph features and resilience, but also time-dependent changes in network stability.

This paper develops a dynamic network model to study how treatment affects seizure propensity over time in epilepsy, identifying potential prognostic markers for personalized treatment.

The paper uses a mathematical model to analyze EEG data from people with epilepsy and identifies cortisol levels and sleep stages as factors explaining daily variability in epileptiform discharges.

The paper classifies human chronotypes using fMRI network-based statistics, achieving 97.3% accuracy in distinguishing early from late chronotypes.

The paper introduces three measures to predict seizure onset patterns using interictal brain network properties from scalp EEG data.