The study was posted as a preprint on bioRxiv on July 13, 2026, by researchers at Nova Southeastern University, who reported that in patients with epilepsy, seizures are associated with pathological neural synchronization, while the preictal period preceding a seizure often exhibits reduced spatial synchronization compared to normal cognition, consistent with the view of the brain as a complex dynamical system in which reduced dimensionality and resilience can precede a phase transition; the Critical Brain Hypothesis, the researchers noted, links the loss of healthy scale-free behavior to epilepsy and other disorders.
According to the researchers, the team used network features such as mean node degree and mean clustering coefficient, derived from thresholded correlation matrices of patients' intracranial electrocorticographic electrode data, to investigate preictal changes. The researchers observed a suppression of intermittent high-synchronization periods within the feature space in the minutes leading up to seizure onset, indicating a breakdown in the brain's ability to maintain normal coherence. These preictal changes, they said, were used to predict seizure probability with a Support Vector Machine algorithm, with discrete predictions then combined into real-time continuous seizure risk forecasts via Bayesian updating. The researchers noted the approach may offer more adaptable, quantitative, and interpretable tools for managing seizures.