Classifying seizure generation mechanisms: A critical transitions framework

TL;DR

This study introduces a framework to classify seizure onset mechanisms, revealing most seizures are noise-induced, which challenges traditional bifurcation-based views and could influence future therapies.

Contribution

The paper develops a versatile classification framework using a mathematical model and machine learning to identify seizure transition types in both animals and humans.

Findings

Most analyzed seizures are noise-induced CTs

Challenges the view that seizures are mainly bifurcation-induced

Provides a new approach for seizure mechanism classification

Abstract

Understanding how the brain switches from normal activity to an epileptic seizure is essential for improving seizure therapy, yet the underlying mechanisms remain largely unknown. In particular, seizure onset can be described as a critical transition (CT), but there is no consensus on whether (i) bifurcation-induced, (ii) noise-induced, or (iii) bifurcation/noise-induced CTs are responsible. To clarify this, we develop a versatile CT-classification framework that can be applied to seizures in both animals and humans. First, we identify a canonical mathematical model which displays CTs that closely resemble voltage recordings of real seizures and can be of the three types mentioned above. We then identify distinctive properties of each CT-type in the model's output and use them to train a machine learning CT-type classifier. Finally, we apply the model-trained classifier to voltage recordings from epileptic rodents. We find that the largest proportion of analysed seizures are classified as noise-induced CTs. This challenges the conventional view that seizures are predominantly bifurcation-induced and could inform new therapeutic strategies for seizures.