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Burst suppression is an electroencephalography (EEG) pattern associated withprofoundly inactivated brain states characterized by cerebral metabolicdepression. Its distinctive feature is alternation between short temporalsegments of near-isoelectric inactivity (suppressions) and relativelyhigh-voltage activity (bursts). Prior modeling studies suggest thatburst-suppression EEG is a manifestation of two alternating brain statesassociated with consumption (during a burst) and production (during asuppression) of adenosine triphosphate (ATP). This finding motivates us toinfer latent states characterizing alternating brain states and underlying ATPkinetics from instantaneous power of multichannel EEG using a switchingstate-space model. Our model assumes Gaussian distributed data as a broadcastnetwork manifestation of one of two global brain states. The two brain statesare allowed to stochastically alternate with transition probabilities thatdepend on the instantaneous ATP level, which evolves according to first-orderkinetics. The rate constants governing the ATP kinetics are allowed to vary asfirst-order autoregressive processes. Our latent state estimates are determinedfrom data using a sequential Monte Carlo algorithm. Ourneurophysiology-informed model not only provides unsupervised segmentation ofmulti-channel burst-suppression EEG but can also generate additional insightson the level of brain inactivation during anesthesia.

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