Rethinking General Anesthesia

DeWitt Stetten Jr. Lecture | Wednesday, November 18, 2020 | 3:00 to 4:00 p.m. ET

Emery N. Brown, M.D., Ph.D.

Warren M. Zapol Professor of Anaesthesia at Harvard Medical School/ Massachusetts General Anesthesia

Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience

Massachusetts Institute of Technology

Website

Dr. Brown is the Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience at the Massachusetts Institute of Technology. He is an anesthesiologist and statistician whose research has helped explain the way anesthetic drugs act in the brain. His discoveries have led to new ways of monitoring patients’ brain states during general anesthesia, as well as strategies for drug dosing and precisely controlling the anesthetic state. Dr. Brown also has developed signal–processing algorithms and statistical methods that characterize the dynamic properties of neuroscience data. A highly lauded lecturer, he is a member of the National Academy of Sciences, the National Academy of Medicine, and the National Academy of Engineering.

Summary

We have established a systems neuroscience (conceptual, experimental, data analysis and modeling) paradigm for studying the mechanisms of general anesthesia-induced loss of consciousness. Through this work my research group has established specific neurophysiological mechanisms for general-anesthesia-induced unconsciousness in humans, non-human primates and rodents, and approaches to precisely control the anesthetic state. We are studying arousal states in general anesthesia and sedation which involves developing a detailed understanding of the neural circuitry of the brainstem, thalamus, and cortex. We are now adapting this neurophysiological understanding to improve brain state monitoring of patients receiving general anesthesia and to develop closed-loop anesthesia delivery for maintenance of unconsciousness. In my signal processing and statistical methods research, we develop algorithms using likelihood, Bayesian, state-space, time-series and point process approaches to study how neural systems represent and transmit information. Our paradigm has been used to: provide dynamic characterizations of how neurons represent information and conduct dynamic assessments of learning.