| Autor: |
Zhou DW; Department of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA., Mowrey DD; Department of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA., Tang P; Department of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA., Xu Y; Department of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA.; Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA. |
| Abstrakt: |
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. |
