A Mechanistic Neural Field Theory for Loss of Consciousness During General Anesthesia

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The goal of this research is to merge dynamical systems theory and dynamic network notions to develop a framework that predicts the qualitative salient characteristics of the anesthetic transition. Specifically, we propose to develop and analyze mean field models to model the electrocortical activity of the central nervous system as a dynamical system and show that the transition to the anesthetic state exhibits multiple bifurcations (i.e., transitions) with the awake state transitioning to a stable limit cycle and subsequently to a stable unconscious equilibrium state as the concentration of the anesthetic agent increases. The proposed framework will allow for the development of models that go beyond words to dynamic equations, leading to mathematical models with greater precision and self-consistency. This research can foster the development of new frameworks that can allow us to interpret experimental and clinical results, connect biophysical findings to psychophysical phenomena, explore new hypothesis based on the cognitive neuroscience of consciousness and develop new assertions, and ultimately improve the reliability of general anesthesia. Intellectual Merit: The results will be applied to excitatory and inhibitory biological neuronal networks to explain the underlying neuronal mechanisms of action for anesthesia and unconsciousness from a macroscopic neural field electrocortical perspective, thereby providing a theoretical foundation for general anesthesia using the dynamic network properties of the brain. Broader Impacts: While the primary focus is general anesthesia, the proposed dynamical systems framework has potential applicability to understand seizure activity and schizophrenia. This also has applicability to the phenomena of multistability seen in biochemical systems, ecosystems, gene regulation and cell replications as well as numerous conditions in medical science including epilepsy, multiple sclerosis, and Alzheimer's disease. The primary impact of this research will be to allow for the development of mathematical models that advance our understanding of the wide effects of pharmacologic agents and anesthetics leading to improvements in medical care, health care, and reliability of drug development and dosing.