State transitions in physiologic systems: a complexity model for loss of consciousness

Complex physiologic systems in which the emergent global (observable) behavior results from the interplay among local processes cannot be studied effectively by conventional mathematical models. In contrast to traditional computational methods which provide linear or nonlinear input-output data mapping without regard to the internal workings of the system, complexity theory offers scientifically and computationally tractable models which take into account microscopic mechanisms and interactions responsible for the overall input-output behavior. This article offers a brief introduction to some of the tenets of complexity theory and outlines the process involved in the development and testing of a model that duplicates the global dynamics of the induction of loss of consciousness (LOG) in humans due to cerebral ischemia. Under the broad definition of complexity, we view the brain of humans as a complex system. Successful development of a model for this complex system requires careful combination of basic knowledge of the physiological system both at the local (microscopic) and global (macroscopic) levels with experimental data and the appropriate mathematical tools. It represents an attempt to develop a model that can both replicate human data and provide insights about possible underlying mechanisms.