Percolation Model of Sensory Transmission and Loss of Consciousness Under General Anesthesia.

Abstract

Neurons communicate with each other dynamically; how such communications lead to consciousness remains unclear. Here, we present a theoretical model to understand the dynamic nature of sensory activity and information integration in a hierarchical network, in which edges are stochastically defined by a single parameter p representing the percolation probability of information transmission. We validate the model by comparing the transmitted and original signal distributions, and we show that a basic version of this model can reproduce key spectral features clinically observed in electroencephalographic recordings of transitions from conscious to unconscious brain activities during general anesthesia. As p decreases, a steep divergence of the transmitted signal from the original was observed, along with a loss of signal synchrony and a sharp increase in information entropy in a critical manner; this resembles the precipitous loss of consciousness during anesthesia. The model offers mechanistic insights into the emergence of information integration from a stochastic process, laying the foundation for understanding the origin of cognition.

• Authors: David W Zhou, David D Mowrey, Pei Tang, Yan Xu
• Journal: Physical review letters (Phys Rev Lett) Vol. 115 Issue 10 Pg. 108103 (Sep 04 2015) ISSN: 1079-7114 [Electronic] United States
• PMID: 26382705 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Afferent Pathways (physiology)
• Anesthesia, General
• Cerebral Cortex (cytology, physiology)
• Consciousness (physiology)
• Humans
• Models, Neurological
• Nerve Net (physiology)
• Neurons (physiology)
• Synaptic Transmission (physiology)
• Thalamus (cytology, physiology)