Inferring neural dynamics during burst suppression using a neurophysiology-inspired switching state-space model

TL;DR

This paper introduces a neurophysiology-inspired switching state-space model to infer latent brain states and ATP kinetics from EEG data during burst suppression, providing insights into brain inactivation during anesthesia.

Contribution

The study develops a novel stochastic model that captures the dynamics of burst suppression EEG and infers underlying ATP-related brain states using a sequential Monte Carlo algorithm.

Findings

Unsupervised segmentation of burst suppression EEG into brain states.

Inference of ATP kinetics from EEG data.

Insights into brain inactivation levels during anesthesia.

Abstract

Burst suppression is an electroencephalography (EEG) pattern associated with profoundly inactivated brain states characterized by cerebral metabolic depression. Its distinctive feature is alternation between short temporal segments of near-isoelectric inactivity (suppressions) and relatively high-voltage activity (bursts). Prior modeling studies suggest that burst-suppression EEG is a manifestation of two alternating brain states associated with consumption (during a burst) and production (during a suppression) of adenosine triphosphate (ATP). This finding motivates us to infer latent states characterizing alternating brain states and underlying ATP kinetics from instantaneous power of multichannel EEG using a switching state-space model. Our model assumes Gaussian distributed data as a broadcast network manifestation of one of two global brain states. The two brain states are allowed to stochastically alternate with transition probabilities that depend on the instantaneous ATP level, which evolves according to first-order kinetics. The rate constants governing the ATP kinetics are allowed to vary as first-order autoregressive processes. Our latent state estimates are determined from data using a sequential Monte Carlo algorithm. Our neurophysiology-informed model not only provides unsupervised segmentation of multi-channel burst-suppression EEG but can also generate additional insights on the level of brain inactivation during anesthesia.