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Functional and topological conditions for explosive synchronization develop in human brain networks with the onset of anesthetic-induced unconsciousness

Authors :
MinKyung eKim
George A. Mashour
Stefanie eBlain-Moraes
Giancarlo eVanini
Vijay eTarnal
Ellen eJanke
Anthony G. Hudetz
Uncheol eLee
Source :
Frontiers in Computational Neuroscience, Vol 10 (2016)
Publication Year :
2016
Publisher :
Frontiers Media S.A., 2016.

Abstract

Sleep, anesthesia and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt state transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four conditions that were previously reported as conditions for ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship for ES between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.

Details

Language :
English
ISSN :
16625188
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Computational Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.8fc9754c6cac420ba332579c93f996e4
Document Type :
article
Full Text :
https://doi.org/10.3389/fncom.2016.00001