  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.
