Assessing levels of consciousness with symbolic analysis

‘Covert consciousness’ is a state in which consciousness is present without the capacity for behavioural response, and it can occur in patients with intraoperative awareness or unresponsive wakefulness syndrome. To detect and prevent this undesirable state, it is critical to develop a reliable neurobiological assessment of an individual's level of consciousness that is independent of behaviour. One such approach that shows potential is measuring surrogates of cortical communication in the brain using electroencephalography (EEG). EEG is practicable in clinical application, but involves many fundamental signal processing problems, including signal-to-noise ratio and high dimensional complexity. Symbolic analysis of EEG can mitigate these problems, improving the measurement of brain connectivity and the ability to successfully assess levels of consciousness. In this article, we review the problem of covert consciousness, basic neurobiological principles of consciousness, current methods of measuring brain connectivity and the advantages of symbolic processing, with a focus on symbolic transfer entropy (STE). Finally, we discuss recent advances and clinical applications of STE and other symbolic analyses to assess levels of consciousness.

[1]  Daniel Polani,et al.  Information Flows in Causal Networks , 2008, Adv. Complex Syst..

[2]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[3]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[4]  J. Victor Binless strategies for estimation of information from neural data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Mikhail Prokopenko,et al.  Differentiating information transfer and causal effect , 2008, 0812.4373.

[6]  J. Changeux,et al.  Experimental and Theoretical Approaches to Conscious Processing , 2011, Neuron.

[7]  P. Holmes Poincaré, celestial mechanics, dynamical-systems theory and “chaos” , 1990 .

[8]  George A. Mashour,et al.  Prevention of Intraoperative Awareness with Explicit Recall in an Unselected Surgical Population: A Randomized Comparative Effectiveness Trial , 2012, Anesthesiology.

[9]  Matthew H. Davis,et al.  Detecting Awareness in the Vegetative State , 2006, Science.

[10]  George A Mashour,et al.  Capturing covert consciousness , 2013, The Lancet.

[11]  B. Kolk,et al.  Awareness under anesthesia and the development of posttraumatic stress disorder. , 2001, General hospital psychiatry.

[12]  G. Tononi,et al.  Unresponsiveness ≠ Unconsciousness , 2012, Anesthesiology.

[13]  Viola Priesemann,et al.  Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans , 2013, PLoS Comput. Biol..

[14]  H. Lau,et al.  Empirical support for higher-order theories of conscious awareness , 2011, Trends in Cognitive Sciences.

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

[16]  A. Revonsuo Binding and the Phenomenal Unity of Consciousness , 1999, Consciousness and Cognition.

[17]  Dimitris Kugiumtzis,et al.  Transfer Entropy on Rank Vectors , 2010, ArXiv.

[18]  R. Zatorre,et al.  Cortical Processing of Complex Auditory Stimuli during Alterations of Consciousness with the General Anesthetic Propofol , 2006, Anesthesiology.

[19]  Top-Flight Safety Model for Nuclear Industry—Response , 2011 .

[20]  Luca Faes,et al.  Effect of Age on Complexity and Causality of the Cardiovascular Control: Comparison between Model-Based and Model-Free Approaches , 2014, PloS one.

[21]  Paul S. Myles,et al.  Posttraumatic Stress Disorder in Aware Patients from the B-Aware Trial , 2010, Anesthesia and analgesia.

[22]  Karl J. Friston Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[23]  Boris Gourévitch,et al.  Evaluating information transfer between auditory cortical neurons. , 2007, Journal of neurophysiology.

[24]  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.

[25]  Fang Sun,et al.  Willful modulation of brain activity in disorders of consciousness. , 2010, The New England journal of medicine.

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

[27]  G. Mashour,et al.  Intraoperative awareness: from neurobiology to clinical practice. , 2011, Anesthesiology.

[28]  Luca Faes,et al.  Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series , 2013, Entropy.

[29]  A. Porta,et al.  Quantifying heart rate dynamics using different approaches of symbolic dynamics , 2013 .

[30]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  J. Sleigh The study of consciousness comes of age. , 2013, Anesthesiology.

[32]  G. Mashour,et al.  Prevention of Intraoperative Awareness with Explicit Recall: Making Sense of the Evidence , 2013, Anesthesiology.

[33]  Karl J. Friston,et al.  Dynamic causal modeling for EEG and MEG , 2009, Human brain mapping.

[34]  Viola Priesemann,et al.  TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy , 2011, BMC Neuroscience.

[35]  C. Finney,et al.  A review of symbolic analysis of experimental data , 2003 .

[36]  Srivas Chennu,et al.  Bedside detection of awareness in the vegetative state: a cohort study , 2011, The Lancet.

[37]  A. Seth,et al.  Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.

[38]  Gordon Pipa,et al.  Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.

[39]  Dimitris Kugiumtzis,et al.  Partial transfer entropy on rank vectors , 2013, ArXiv.

[40]  Anil K. Seth,et al.  Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling , 2013, NeuroImage.

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

[42]  Gerhard Schneider,et al.  Fronto-Parietal Connectivity Is a Non-Static Phenomenon with Characteristic Changes during Unconsciousness , 2014, PloS one.

[43]  UnCheol Lee,et al.  Surge of neurophysiological coherence and connectivity in the dying brain , 2013, Proceedings of the National Academy of Sciences.

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

[45]  S. Dehaene,et al.  Information Sharing in the Brain Indexes Consciousness in Noncommunicative Patients , 2013, Current Biology.

[46]  L. Faes,et al.  Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Viola Priesemann,et al.  Measuring Information-Transfer Delays , 2013, PloS one.

[48]  G. Rangarajan,et al.  Mitigating the effects of measurement noise on Granger causality. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[50]  J. Eckmann,et al.  Iterated maps on the interval as dynamical systems , 1980 .

[51]  G. Tononi An information integration theory of consciousness , 2004, BMC Neuroscience.

[52]  Dimitris Kugiumtzis,et al.  Evaluation of Mutual Information estimators for Time Series , 2009, Int. J. Bifurc. Chaos.

[53]  Mary M. Conte,et al.  Bedside detection of awareness in the vegetative state , 2012, The Lancet.

[54]  G Tononi,et al.  Integrated information theory of consciousness: an updated account. , 2012, Archives italiennes de biologie.

[55]  Laurent Vercueil,et al.  Persistence of Cortical Sensory Processing during Absence Seizures in Human and an Animal Model: Evidence from EEG and Intracellular Recordings , 2013, PloS one.

[56]  Stanislas Dehaene,et al.  Comment on “Preserved Feedforward But Impaired Top-Down Processes in the Vegetative State” , 2011, Science.

[57]  A. Ledberg,et al.  When two become one: the limits of causality analysis of brain dynamics. , 2012, PloS one.

[58]  W. Singer Consciousness and the Binding Problem , 2001, Annals of the New York Academy of Sciences.

[59]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[60]  G. Tononi,et al.  Breakdown of Cortical Effective Connectivity During Sleep , 2005, Science.

[61]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[62]  Matthäus Staniek,et al.  Symbolic transfer entropy. , 2008, Physical review letters.

[63]  M. Boly,et al.  Consciousness and responsiveness: lessons from anaesthesia and the vegetative state , 2013, Current opinion in anaesthesiology.

[64]  A. Clark Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[65]  Karl J. Friston,et al.  Preserved Feedforward But Impaired Top-Down Processes in the Vegetative State , 2011, Science.

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

[67]  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.