Critical dynamics, anesthesia and information integration: Lessons from multi-scale criticality analysis of voltage imaging data

&NA; Critical dynamics are thought to play an important role in neuronal information‐processing: near critical networks exhibit neuronal avalanches, cascades of spatiotemporal activity that are scale‐free, and are considered to enhance information capacity and transfer. However, the exact relationship between criticality, awareness, and information integration remains unclear. To characterize this relationship, we applied multi‐scale avalanche analysis to voltage‐sensitive dye imaging data collected from animals of various species under different anesthetics. We found that anesthesia systematically varied the scaling behavior of neural dynamics, a change that was mirrored in reduced neural complexity. These findings were corroborated by applying the same analyses to a biophysically realistic cortical network model, in which multi‐scale criticality measures were associated with network properties and the capacity for information integration. Our results imply that multi‐scale criticality measures are potential biomarkers for assessing the level of consciousness.

[1]  Amiram Grinvald,et al.  Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different , 2019, Cerebral cortex.

[2]  Woodrow L. Shew,et al.  Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality , 2009, The Journal of Neuroscience.

[3]  Amiram Grinvald,et al.  Arousal increases the representational capacity of cortical tissue , 2009, Journal of Computational Neuroscience.

[4]  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.

[5]  K. Linkenkaer-Hansen,et al.  Critical-State Dynamics of Avalanches and Oscillations Jointly Emerge from Balanced Excitation/Inhibition in Neuronal Networks , 2012, The Journal of Neuroscience.

[6]  Gustavo Deco,et al.  Spontaneous cortical activity is transiently poised close to criticality , 2017, PLoS Comput. Biol..

[7]  Woodrow L. Shew Neuronal Avalanches , 2014, Encyclopedia of Computational Neuroscience.

[8]  Narayan Srinivasa,et al.  Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks , 2015, PLoS Comput. Biol..

[9]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[10]  G. Tononi,et al.  Human cortical excitability increases with time awake. , 2013, Cerebral cortex.

[11]  Oren Shriki,et al.  Deviations from Critical Dynamics in Interictal Epileptiform Activity , 2016, The Journal of Neuroscience.

[12]  Q. Vuong Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[13]  Frédéric Chavane,et al.  Effects of GABAA kinetics on cortical population activity: computational studies and physiological confirmations. , 2016, Journal of neurophysiology.

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

[15]  T. E. Harris,et al.  The Theory of Branching Processes. , 1963 .

[16]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[17]  O. Kinouchi,et al.  Optimal dynamical range of excitable networks at criticality , 2006, q-bio/0601037.

[18]  G. Tononi,et al.  Sleep function and synaptic homeostasis. , 2006, Sleep medicine reviews.

[19]  Woodrow L. Shew,et al.  Voltage Imaging of Waking Mouse Cortex Reveals Emergence of Critical Neuronal Dynamics , 2014, The Journal of Neuroscience.

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

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

[22]  A. Grinvald,et al.  A tandem-lens epifluorescence macroscope: Hundred-fold brightness advantage for wide-field imaging , 1991, Journal of Neuroscience Methods.

[23]  Enzo Tagliazucchi,et al.  Brain complexity born out of criticality , 2012, 1211.0309.

[24]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[25]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[26]  A. Grinvald,et al.  Imaging Cortical Dynamics at High Spatial and Temporal Resolution with Novel Blue Voltage-Sensitive Dyes , 1999, Neuron.

[27]  K. Linkenkaer-Hansen,et al.  Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws , 2013, Proceedings of the National Academy of Sciences.

[28]  Oren Shriki,et al.  Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network , 2016, PLoS Comput. Biol..

[29]  John M. Beggs,et al.  Universal critical dynamics in high resolution neuronal avalanche data. , 2012, Physical review letters.

[30]  G. Edelman,et al.  Consciousness and Complexity , 1998 .

[31]  Anil K. Seth,et al.  Consciousness and Complexity , 2022 .

[32]  M. Boly,et al.  Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia , 2015, PloS one.

[33]  Oren Shriki,et al.  Near-Critical Dynamics in Stimulus-Evoked Activity of the Human Brain and Its Relation to Spontaneous Resting-State Activity , 2015, The Journal of Neuroscience.

[34]  George A. Mashour,et al.  Electroencephalographic coherence and cortical acetylcholine during ketamine-induced unconsciousness. , 2015, British journal of anaesthesia.

[35]  C. Honey,et al.  A place for time: the spatiotemporal structure of neural dynamics during natural audition. , 2013, Journal of neurophysiology.

[36]  J. M. Herrmann,et al.  Dynamical synapses causing self-organized criticality in neural networks , 2007, 0712.1003.

[37]  A. Grinvald,et al.  Long-term voltage-sensitive dye imaging reveals cortical dynamics in behaving monkeys. , 2002, Journal of neurophysiology.

[38]  Nikos K Logothetis,et al.  A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings , 2009, BMC Neuroscience.

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

[40]  Laura D. Lewis,et al.  Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness , 2012, Proceedings of the National Academy of Sciences.

[41]  D. Plenz,et al.  Spontaneous cortical activity in awake monkeys composed of neuronal avalanches , 2009, Proceedings of the National Academy of Sciences.

[42]  G. Tononi,et al.  Propofol anesthesia reduces Lempel-Ziv complexity of spontaneous brain activity in rats , 2016, Neuroscience Letters.

[43]  D. Plenz Neuronal avalanches and coherence potentials , 2012 .

[44]  W. Singer,et al.  Neuronal avalanches in spontaneous activity in vivo. , 2010, Journal of neurophysiology.

[45]  Stefan Mihalas,et al.  Self-organized criticality occurs in non-conservative neuronal networks during Up states , 2010, Nature physics.

[46]  Woodrow L. Shew,et al.  Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection , 2017, PLoS Comput. Biol..

[47]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

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

[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]  M. A. Muñoz,et al.  Griffiths phases and the stretching of criticality in brain networks , 2013, Nature Communications.

[51]  Amiram Grinvald,et al.  Interhemispheric Synchrony of Spontaneous Cortical States at the Cortical Column Level , 2018, Cerebral cortex.

[52]  Amiram Grinvald,et al.  Milliseconds of Sensory Input Abruptly Modulate the Dynamics of Cortical States for Seconds , 2016, Cerebral cortex.

[53]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[54]  Woodrow L. Shew,et al.  Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches , 2010, The Journal of Neuroscience.

[55]  Fabrice Wendling,et al.  Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals , 2000, Biological Cybernetics.

[56]  Amiram Grinvald,et al.  Dural substitute for long-term imaging of cortical activity in behaving monkeys and its clinical implications , 2002, Journal of Neuroscience Methods.

[57]  Steven Laureys,et al.  Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics , 2015, Journal of The Royal Society Interface.

[58]  Toru Yanagawa,et al.  Loss of Consciousness Is Associated with Stabilization of Cortical Activity , 2015, The Journal of Neuroscience.

[59]  A Grinvald,et al.  Long-Term Optical Imaging and Spectroscopy Reveal Mechanisms Underlying the Intrinsic Signal and Stability of Cortical Maps in V1 of Behaving Monkeys , 2000, The Journal of Neuroscience.

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