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In a collaboration with Viktor Jirsa and Christophe Bernard (Marseille, France), we developed a dynamical model of seizure activity, based upon the bifurcations and state variables necessary to produce focal seizures. We found that the most common bifurcations were a saddle node at seizure onset, and a homoclinic bifurcation at offset. These bifurcations led to several predictions about canonical seizure dynamics, several of which we were able to validate in multiple species such as rats, mice, zebrafish, and humans. Using this framework, we are now categorizing human seizure dynamics on a larger scale, with the hopes to improve our understanding of the time course of seizures. This work led to a novel method to characterize the basic dynamical features of seizure onset and offset, which we named the "dynamotype" of a seizure.
Project 1: Developing an atlas and toolbox for mathmatical modeling of seizures
Epileptic seizures involve the brain transitioning from a resting state to an abnormal state of synchronized bursting, akin to a bifurcation in dynamical systems where a parameter shift triggers a sudden change in behavior. Recently, a comprehensive model was proposed that used dynamical equations capable of simulating 16 “dynamotypes” of seizures that span the full range of theoretical dynamics. In the current work, we developed a dynamical atlas of all 16 possible onset-offset bifurcation combinations, each characterized by distinct features in the simulated EEG-like recordings. We developed a primer and GUI allowing a user-friendly guide for generating diverse datasets of simulated seizure recordings and enhancing their resemblance to human EEG data through the addition of pink noise in post-processing and an electrode drift correction filter. This toolbox can thus produce large numbers of diverse seizure patterns that have similar noise and filtering characteristics as human EEG, which can aid in training seizure detection algorithms, understanding brain dynamical behavior for clinicians, and exploring the impact of noise on EEG recordings.
Project 2: Optimization of ictal abprting stimulation using the dynamotype taxonomy
Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster’s duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.
Project 3: A taxonomy of seizure dynamotypes
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties.
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