LAUSR

INTRODUCTION As a dynamical system, the brain constantly modulates its state and epileptic seizures have been hypothesized to be low dimensional periodic states of the brain. With this assumption, seizures have previously been investigated to identify patterns of these recurrent states; however, these attempts have generated conflicting results. These discrepant observations led us to reconsider the dynamic of state transitions during seizures. METHODS Using intracerebral recordings of 17 refractory epilepsy patients assessed prior to surgery, we studied ictal states with several state-of-the-art methods in order to investigate their dynamics. Global states were identified based on distinct functional connectivity measures in the time domain, frequency domain, and phase-space. We further investigated the state transitions in different brain regions locally using a univariate measure based on dynamical system analysis named the Recurrence Plot (RP). RESULTS For the ictal period, we detected lower global state transition rates compared to pre- and post-ictal periods (p < 0.05 for seizure-free (SF) and p > 0.05 for non-seizure-free (NSF) groups post-surgery); however, the structure of RPs pointed towards higher state transition rates in some regions like the seizure-onset-zone (p < 0.001 for SF and p > 0.05 for NSF group). Moreover, a direct comparison of state transition dynamics between SF and NSF patients revealed different patterns for local state transitions between SF and NSF patients (p < 0.05 for seizure-onset-zone while p > 0.05 for other regions) and no significant difference in global state transition rates (p > 0.05). CONCLUSION Our findings pointed to distinct dynamics for state transitions at different spatial scales. While the pattern of global state transitions led to the conclusion that the brain changes state less frequently during ictal activity, locally, it experienced a higher rate of state transition. Furthermore, our results for different patterns of state transitions in the seizure-onset-zone between SF and NSF patients could have a practical application in predicting surgical outcome.