Parsing spatiotemporal dynamical stability in ECoG during seizure onset, propagation, and termination

TL;DR

This study applies dynamical systems analysis to ECoG data from epilepsy patients, revealing patterns in seizure dynamics and regional contributions across timescales, which could inform new control strategies.

Contribution

The paper introduces a novel dynamical systems approach to characterize spatiotemporal seizure behavior in ECoG data, highlighting eigenvalue and eigenvector patterns during seizures.

Findings

Seizure onset involves increased high-frequency spatial information from few regions.

Seizure termination shows rapid dynamics involving many regions.

Post-ictal regime characterized by fast-damping oscillations.

Abstract

Understanding brain dynamics in epilepsy is critical for establishing rigorous control objectives that enable new therapeutic methods to mitigate seizure occurrence. In multichannel electrocorticography (ECoG) recordings acquired in 21 subjects during a total of 94 seizures, we apply dynamical systems stability analysis to assess the balance versus imbalance of seizure dynamics across different timescales and brain regions. Specifically, we consider a sliding time window multivariate autoregressive linear approximation of the data captured by the ECoG channels, where eigendecomposition of the estimated matrix of coefficients describes the contribution of different regions to the spatiotemporal process (eigenvectors) associated with a particular timescale (eigenvalues). Interestingly, we observe a pattern of eigenvalue evolution and slowly changing (or approximately time-invariant) eigenvectors across both seizures and subjects. The seizure-onset is marked by an increase in high frequency spatial information to which a few regions contribute for a long period. By contrast, the seizure termination is characterized by a sudden, small time period change in dynamics to which many regions contribute. As the seizure terminates, the relatively stable ictal dynamics rapidly transition into the post-ictal regime, marked by relatively fast-damping oscillations. Our methodology offers a subject-specific characterization of the spatiotemporal behavior of the seizure, providing new insights into the dynamic patterns and functional interactions between brain regions that occur over different timescales. More generally, our approach informs the development of engineering objectives that can be used to deploy new control strategies to prevent seizure evolution or to hasten seizure termination.