Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

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 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 variables that are involved in facilitating 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 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.

[1]  UnCheol Lee,et al.  Dissociable Network Properties of Anesthetic State Transitions , 2011, Anesthesiology.

[2]  Zonghua Liu,et al.  Explosive synchronization in a general complex network. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  UnCheol Lee,et al.  Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness , 2013, Anesthesiology.

[4]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[5]  Sergio Gómez,et al.  Explosive synchronization transitions in scale-free networks. , 2011, Physical review letters.

[6]  J. Sleigh,et al.  Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. II. Numerical simulations, spectral entropy, and correlation times. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Steven Laureys,et al.  Dynamic Change of Global and Local Information Processing in Propofol-Induced Loss and Recovery of Consciousness , 2013, PLoS Comput. Biol..

[8]  Larissa Albantakis,et al.  From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0 , 2014, PLoS Comput. Biol..

[9]  J. Sleigh,et al.  Modelling general anaesthesia as a first-order phase transition in the cortex. , 2004, Progress in biophysics and molecular biology.

[10]  Emery N. Brown,et al.  Electroencephalogram signatures of loss and recovery of consciousness from propofol , 2013, Proceedings of the National Academy of Sciences.

[11]  Q. C. Meng,et al.  A Conserved Behavioral State Barrier Impedes Transitions between Anesthetic-Induced Unconsciousness and Wakefulness: Evidence for Neural Inertia , 2010, PloS one.

[12]  Zonghua Liu,et al.  Explosive synchronization in adaptive and multilayer networks. , 2014, Physical review letters.

[13]  Michael Small,et al.  Reexamination of explosive synchronization in scale-free networks: the effect of disassortativity. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Manuel S. Schröter,et al.  Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness , 2012, The Journal of Neuroscience.

[15]  Ye Wu,et al.  Effects of frequency-degree correlation on synchronization transition in scale-free networks , 2013 .

[16]  G. Tononi,et al.  A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior , 2013, Science Translational Medicine.

[17]  Liuhua Zhu,et al.  Criterion for the emergence of explosive synchronization transitions in networks of phase oscillators. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  UnCheol Lee,et al.  Propofol induction reduces the capacity for neural information integration: Implications for the mechanism of consciousness and general anesthesia , 2008, Consciousness and Cognition.

[19]  UnCheol Lee,et al.  Preferential Inhibition of Frontal-to-Parietal Feedback Connectivity Is a Neurophysiologic Correlate of General Anesthesia in Surgical Patients , 2011, PloS one.

[20]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[21]  Juan G. Restrepo,et al.  Effects of degree-frequency correlations on network synchronization: Universality and full phase-locking , 2012, 1208.4540.

[22]  J. A. Almendral,et al.  Effects of degree correlations on the explosive synchronization of scale-free networks. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Emery N. Brown,et al.  Tracking brain states under general anesthesia by using global coherence analysis , 2011, Proceedings of the National Academy of Sciences.

[24]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[25]  Kyunghee Koh,et al.  Genetic and Anatomical Basis of the Barrier Separating Wakefulness and Anesthetic-Induced Unresponsiveness , 2013, PLoS genetics.

[26]  Jamie W. Sleigh,et al.  Electroencephalographic Variation during End Maintenance and Emergence from Surgical Anesthesia , 2014, PloS one.

[27]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[28]  George A. Mashour,et al.  Assessing levels of consciousness with symbolic analysis , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[29]  UnCheol Lee,et al.  Disruption of Frontal–Parietal Communication by Ketamine, Propofol, and Sevoflurane , 2013, Anesthesiology.

[30]  Masashi Yanagisawa,et al.  An essential role for orexins in emergence from general anesthesia , 2008, Proceedings of the National Academy of Sciences.

[31]  Alex Arenas,et al.  Disorder induces explosive synchronization. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  J. Sleigh,et al.  Theoretical predictions for spatial covariance of the electroencephalographic signal during the anesthetic-induced phase transition: Increased correlation length and emergence of spatial self-organization. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  G. Mashour Cognitive unbinding: A neuroscientific paradigm of general anesthesia and related states of unconsciousness , 2013, Neuroscience & Biobehavioral Reviews.

[34]  George A. Mashour,et al.  General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks , 2015, PLoS Comput. Biol..

[35]  M. Boly,et al.  Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness , 2010, Anesthesiology.

[36]  S. Boccaletti,et al.  Synchronization centrality and explosive synchronization in complex networks , 2018 .

[37]  G. Mashour,et al.  Neurophysiological Correlates of Sevoflurane-induced Unconsciousness , 2015, Anesthesiology.

[38]  D A Steyn-Ross,et al.  Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. I. A thermodynamics analogy. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  Bernhard Hemmer,et al.  Simultaneous Electroencephalographic and Functional Magnetic Resonance Imaging Indicate Impaired Cortical Top–Down Processing in Association with Anesthetic-induced Unconsciousness , 2013, Anesthesiology.

[40]  Andrew J. Szeri,et al.  Emergence from general anesthesia and the sleep-manifold , 2014, Front. Syst. Neurosci..

[41]  D. Pfaff,et al.  Recovery of consciousness is mediated by a network of discrete metastable activity states , 2014, Proceedings of the National Academy of Sciences.

[42]  G. Tononi,et al.  Consciousness and Anesthesia , 2008, Science.

[43]  G. Tononi,et al.  The neurology of consciousness : cognitive neuroscience and neuropathology , 2009 .

[44]  Raissa M. D'Souza,et al.  Anomalous critical and supercritical phenomena in explosive percolation , 2015, Nature Physics.

[45]  D. Liley,et al.  Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: evidence for a general anesthetic-induced phase transition. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[46]  George A. Mashour,et al.  Electroencephalographic effects of ketamine on power, cross-frequency coupling, and connectivity in the alpha bandwidth , 2014, Front. Syst. Neurosci..

[47]  I Leyva,et al.  Explosive synchronization in weighted complex networks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Jared S. Moore Is This Phenomenology , 1942 .

[49]  G. Tononi,et al.  Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness , 2010, Proceedings of the National Academy of Sciences.

[50]  Yong Zou,et al.  Explosive synchronization as a process of explosive percolation in dynamical phase space , 2014, Scientific Reports.