Classifying absence seizure generation mechanisms: A critical transitions framework
Understanding how the brain switches from normal activity to an epileptic seizure is essential for improving seizure therapy, yet the underlying seizure generation mechanisms remain largely unknown. In particular, while seizure onset has been described as a critical transition (CT), 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 which consist of thousands of real absence seizures. We find that the largest proportion of analysed seizures are classified as noise-induced CTs. In other words, our results on absence seizures in rodents are in contrast to the conventional view that seizures are predominantly bifurcation-induced, and indicate that different CT mechanisms may dominate different seizure types.