Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

Monitoring the hypnotic component of anesthesia during surgeries is critical to prevent intraoperative awareness and reduce adverse side effects. For this purpose, electroencephalographic methods complementing measures of autonomic functions and behavioral responses are in use in clinical practice. However, in human neonates and infants existing methods may be unreliable and the correlation between brain activity and anesthetic depth is still poorly understood. Here, we characterize the effects of different anesthetics on activity of several brain areas in neonatal mice and develop machine learning approaches to identify electrophysiological features predicting inspired or end-tidal anesthetic concentration as a proxy for anesthetic depth. We show that similar features from electroencephalographic recordings can be applied to predict anesthetic concentration in neonatal mice, and human neonates and infants. These results might support a novel strategy to monitor anesthetic depth in human newborns.