Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia

Jeongkyu Shin,Seunghwan Kim,Seungwoo Ku,George A Mashour,Uncheol Lee,Jerzy Bodurka

doi:10.1371/journal.pone.0070899

Abstract

General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network “backbones.” We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.

Highlights

General anesthesia rapidly modulates levels of consciousness and has been suggested to be a useful tool for the study of consciousness [1,2]
Recent studies of single unit recordings and local field potentials in humans have demonstrated the importance of dynamic temporal changes of neural networks during general anesthesia [9]
To examine the spatial and temporal properties of functional brain connectivity simultaneously, we introduce a novel concept, termed ‘‘dynamic network backbones.’’ The dynamic network backbone allows us to measure the statistical relevance of subgraphs in a network and to trace the dynamics of subgraphs by extending the concept of a network motif [10] to the temporal domain

Summary

Introduction

General anesthesia rapidly modulates levels of consciousness and has been suggested to be a useful tool for the study of consciousness [1,2]. The precipitous state transitions across loss and recovery of consciousness provide a unique opportunity to study dynamic brain network behavior. The reduced level of consciousness during anesthesia is associated with topological changes of functional connectivity in the brain [3,4,5,6,7,8]. The disruption of functional connectivity and suppression of metabolic activity are common features demonstrated across numerous neuroimaging studies. Recent studies of single unit recordings and local field potentials in humans have demonstrated the importance of dynamic temporal changes of neural networks during general anesthesia [9]

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