Dynamical criticality during induction of anesthesia in human ECoG recordings

In this work we analyze electro-corticography (ECoG) recordings in human subjects during induction of anesthesia with propofol. We hypothesize that the decrease in responsiveness that defines the anesthetized state is concomitant with the stabilization of neuronal dynamics. To test this hypothesis, we performed a moving vector autoregressive analysis and quantified stability of neuronal dynamics using eigenmode decomposition of the autoregressive matrices, independently fitted to short sliding temporal windows. Consistent with the hypothesis we show that while the subject is awake, many modes of neuronal activity oscillations are found at the edge of instability. As the subject becomes anesthetized, we observe statistically significant increase in the stability of neuronal dynamics, most prominently observed for high frequency oscillations. Stabilization was not observed in phase randomized surrogates constructed to preserve the spectral signatures of each channel of neuronal activity. Thus, stability analysis offers a novel way of quantifying changes in neuronal activity that characterize loss of consciousness induced by general anesthetics.

[1]  Thomas Nowotny,et al.  Criteria for robustness of heteroclinic cycles in neural microcircuits , 2011, Journal of mathematical neuroscience.

[2]  Arnold Neumaier,et al.  Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[3]  Nelson A. M. Lemos,et al.  Novel Experience Induces Persistent Sleep-Dependent Plasticity in the Cortex but not in the Hippocampus , 2007, Front. Neurosci..

[4]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[5]  Woodrow L. Shew,et al.  The Functional Benefits of Criticality in the Cortex , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[6]  George A. Mashour,et al.  Genuine and Spurious Phase Synchronization Strengths during Consciousness and General Anesthesia , 2012, PloS one.

[7]  O. Shriki,et al.  Fading Signatures of Critical Brain Dynamics during Sustained Wakefulness in Humans , 2013, The Journal of Neuroscience.

[8]  G. Tononi.  Consciousness as Integrated Information: a Provisional Manifesto , 2008, The Biological Bulletin.

[9]  E. Brown,et al.  General anesthesia, sleep, and coma. , 2010, The New England journal of medicine.

[10]  M. Nicolelis,et al.  Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle , 2010, PloS one.

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

[12]  Anthony G. Hudetz,et al.  Volatile anesthetics disrupt frontal-posterior recurrent information transfer at gamma frequencies in rat , 2005, Neuroscience Letters.

[13]  D. Chialvo.  Emergent complex neural dynamics , 2010, 1010.2530.

[14]  M. Magnasco,et al.  Self-Regulated Dynamical Criticality in Human ECoG , 2012, Front. Integr. Neurosci..

[15]  G. Mashour,et al.  Prevention of intraoperative awareness in a high-risk surgical population. , 2011, The New England journal of medicine.

[16]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[17]  Peter Ashwin,et al.  Dynamics of Coupled Cell Networks: Synchrony, Heteroclinic Cycles and Inflation , 2011, J. Nonlinear Sci..

[18]  W. Bialek,et al.  Are Biological Systems Poised at Criticality? , 2010, 1012.2242.

[19]  Gil,et al.  Landau-Ginzburg theory of self-organized criticality. , 1996, Physical review letters.

[20]  John M. Beggs,et al.  Being Critical of Criticality in the Brain , 2012, Front. Physio..

[21]  A. Yli-Hankala,et al.  Increase in high frequency EEG activity explains the poor performance of EEG spectral entropy monitor during S-ketamine anesthesia , 2006, Clinical Neurophysiology.

[22]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[23]  M. Magnasco,et al.  Self-tuned critical anti-Hebbian networks. , 2009, Physical review letters.

[24]  H. Laufs,et al.  Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep , 2013, Proceedings of the National Academy of Sciences.

[25]  S. Strogatz.  Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering , 1995 .

[26]  D. Plenz,et al.  Neuronal Avalanches in the Resting MEG of the Human Brain , 2012, The Journal of Neuroscience.

[27]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[28]  S. Emid,et al.  On the relativistic dynamics of polarized systems. III: The case of atoms and molecules with electric and magnetic dipole moments and electric quadrupole moments , 1969 .

[29]  S L Shafer,et al.  The influence of age on propofol pharmacodynamics. , 1999, Anesthesiology.

[30]  D. Plenz,et al.  Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3 , 2008, Proceedings of the National Academy of Sciences.

[31]  R Huerta,et al.  Dynamical encoding by networks of competing neuron groups: winnerless competition. , 2001, Physical review letters.