Identification of global and local states during seizures using quantitative functional connectivity and recurrence...

Area of Science:

• Neuroscience
• Dynamical Systems Theory
• Epilepsy Research

Background:

• The brain functions as a dynamical system, constantly modulating its state.
• Epileptic seizures are hypothesized to be low-dimensional periodic states of the brain.
• Previous studies on seizure state transitions yielded conflicting results, necessitating a re-evaluation of seizure dynamics.

Purpose of the Study:

• To investigate the dynamics of state transitions during epileptic seizures.
• To differentiate between global and local state transition patterns during ictal activity.
• To explore the potential of state transition dynamics in predicting surgical outcomes for epilepsy patients.

Main Methods:

• Utilized intracerebral recordings from 17 refractory epilepsy patients.
• Identified global brain states using functional connectivity in time, frequency, and phase-space domains.
• Analyzed local state transitions within brain regions using Recurrence Plots (RPs).

Main Results:

• Global state transition rates were lower during seizures compared to pre- and post-ictal periods.
• Local analysis revealed higher state transition rates in specific regions, notably the seizure-onset-zone (SOZ).
• Distinct local state transition patterns were observed between seizure-free (SF) and non-seizure-free (NSF) patients, particularly in the SOZ.

Conclusions:

• Epileptic seizures exhibit distinct dynamics at different spatial scales: reduced global transitions but increased local transitions.
• The differing local state transition patterns in the SOZ between SF and NSF patients offer potential for predicting surgical success.
• This study advances the understanding of seizure dynamics and their clinical implications.