Dynamical phase transitions in postictal generalized EEG suppression

TL;DR

This paper models the brain's transition during postictal generalized EEG suppression (PGES) using a Hopf model, revealing universal dynamical signatures and phase transitions that explain recovery mechanisms after seizures.

Contribution

It introduces a dynamical systems model to explain PGES recovery, highlighting a phase transition from fixed point to bistable states as a universal feature.

Findings

Recovery involves a transition from fixed point to bistable states.

Patterns are consistent across patients, indicating a universal signature.

The model captures the bimodal distribution of EEG power during recovery.

Abstract

Postictal generalized EEG suppression (PGES) is a neurological condition that occurs in patients with generalized tonic-clonic seizures. It is marked by suppressed signals just after the seizure before the brain gradually recovers. Recovery from PGES involves a mixed state of amplitude suppression and high-amplitude oscillations, exhibiting a bimodal exponential distribution in power, unlike the unimodal exponential distribution of PGES. In this study, using the subcritical Hopf model, we explain the nature of phase transitions that underlie PGES. Our results reveal that recovery from PGES involves a change from a fixed point state to a bistable state (mixed phase), effectively captured by the noisy fixed-point and bistable regimes of the model. Consistent patterns across patients suggest a universal dynamical signature in PGES recovery. Our findings offer a mechanistic understanding of seizure termination and postictal brain state transitions.