Maintained avalanche dynamics during task-induced changes of neuronal activity in nonhuman primates

Sensory events, cognitive processing and motor actions correlate with transient changes in neuronal activity. In cortex, these transients form widespread spatiotemporal patterns with largely unknown statistical regularities. Here, we show that activity associated with behavioral events carry the signature of scale-invariant spatiotemporal clusters, neuronal avalanches. Using high-density microelectrode arrays in nonhuman primates, we recorded extracellular unit activity and the local field potential (LFP) in premotor and prefrontal cortex during motor and cognitive tasks. Unit activity and negative LFP deflections (nLFP) consistently changed in rate at single electrodes during tasks. Accordingly, nLFP clusters on the array deviated from scale-invariance compared to ongoing activity. Scale-invariance was recovered using ‘adaptive binning’, that is identifying clusters at temporal resolution given by task-induced changes in nLFP rate. Measures of LFP synchronization confirmed and computer simulations detailed our findings. We suggest optimization principles identified for avalanches during ongoing activity to apply to cortical information processing during behavior.

[1]  Peter E. Latham,et al.  Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables , 2016, PLoS Comput. Biol..

[2]  Andreas Schulze-Bonhage,et al.  Quantifying antiepileptic drug effects using intrinsic excitability measures , 2016, Epilepsia.

[3]  David J. Sharp,et al.  Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice , 2016, Cerebral cortex.

[4]  Woodrow L. Shew,et al.  State-dependent intrinsic predictability of cortical network dynamics , 2015, PLoS Comput. Biol..

[5]  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.

[6]  Woodrow L. Shew,et al.  Adaptation to sensory input tunes visual cortex to criticality , 2015, Nature Physics.

[7]  Andreas Klaus,et al.  Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state , 2015, eLife.

[8]  Woodrow L. Shew,et al.  Cascades and Cognitive State: Focused Attention Incurs Subcritical Dynamics , 2015, The Journal of Neuroscience.

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

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

[11]  Nergis Tomen,et al.  Marginally subcritical dynamics explain enhanced stimulus discriminability under attention , 2014, Front. Syst. Neurosci..

[12]  Jochen Triesch,et al.  Spike avalanches in vivo suggest a driven, slightly subcritical brain state , 2014, Front. Syst. Neurosci..

[13]  Andreas Klaus,et al.  Scale-Invariant Neuronal Avalanche Dynamics and the Cut-Off in Size Distributions , 2014, PloS one.

[14]  John M. Beggs,et al.  Quasicritical brain dynamics on a nonequilibrium Widom line. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  M. Copelli,et al.  Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches , 2014, PloS one.

[16]  David J Schwab,et al.  Zipf's law and criticality in multivariate data without fine-tuning. , 2013, Physical review letters.

[17]  Luc Berthouze,et al.  Identification of Criticality in Neuronal Avalanches: II. A Theoretical and Empirical Investigation of the Driven Case , 2013, Journal of mathematical neuroscience.

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

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

[20]  K. Zilles,et al.  The "what" and "when" of self-initiated movements. , 2013, Cerebral cortex.

[21]  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.

[22]  Luc Berthouze,et al.  Identification of Criticality in Neuronal Avalanches: I. A Theoretical Investigation of the Non-driven Case , 2012, Journal of mathematical neuroscience.

[23]  Dante R Chialvo,et al.  Brain organization into resting state networks emerges at criticality on a model of the human connectome. , 2012, Physical review letters.

[24]  Konrad P. Körding,et al.  Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons , 2012, PLoS Comput. Biol..

[25]  Chun-I Yeh,et al.  Laminar analysis of visually evoked activity in the primary visual cortex , 2012, Proceedings of the National Academy of Sciences.

[26]  E. Miller,et al.  The Role of Prefrontal Dopamine D1 Receptors in the Neural Mechanisms of Associative Learning , 2012, Neuron.

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

[28]  Woodrow L. Shew,et al.  Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches , 2012, The Journal of Neuroscience.

[29]  Pablo Balenzuela,et al.  Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis , 2012, Front. Physio..

[30]  Dante R. Chialvo,et al.  What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations , 2010, Front. Physio..

[31]  Shan Yu,et al.  Higher-Order Interactions Characterized in Cortical Activity , 2011, The Journal of Neuroscience.

[32]  K. Harris,et al.  Cortical state and attention , 2011, Nature Reviews Neuroscience.

[33]  Andreas Klaus,et al.  Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches , 2011, PloS one.

[34]  E. Ott,et al.  Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus. , 2011, Chaos.

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

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

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

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

[39]  L. de Arcangelis,et al.  Learning as a phenomenon occurring in a critical state , 2010, Proceedings of the National Academy of Sciences.

[40]  Andrew M. Clark,et al.  Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.

[41]  D. Plenz,et al.  Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex , 2010, PLoS biology.

[42]  J. Maunsell,et al.  Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.

[43]  Jude F. Mitchell,et al.  Spatial Attention Decorrelates Intrinsic Activity Fluctuations in Macaque Area V4 , 2009, Neuron.

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

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

[46]  K. Harris,et al.  Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations , 2009, Neuron.

[47]  Edward T. Bullmore,et al.  Broadband Criticality of Human Brain Network Synchronization , 2009, PLoS Comput. Biol..

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

[49]  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.

[50]  Ilya Shmulevich,et al.  Critical networks exhibit maximal information diversity in structure-dynamics relationships. , 2008, Physical review letters.

[51]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[52]  R. Shapley,et al.  A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons , 2007, The Journal of Neuroscience.

[53]  S. Kauffman,et al.  Measures for information propagation in Boolean networks , 2007 .

[54]  N. Hatsopoulos,et al.  Propagating waves mediate information transfer in the motor cortex , 2006, Nature Neuroscience.

[55]  D. Plenz,et al.  Inverted-U Profile of Dopamine–NMDA-Mediated Spontaneous Avalanche Recurrence in Superficial Layers of Rat Prefrontal Cortex , 2006, The Journal of Neuroscience.

[56]  R. Desimone,et al.  Gamma-band synchronization in visual cortex predicts speed of change detection , 2006, Nature.

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

[58]  John M Beggs,et al.  Critical branching captures activity in living neural networks and maximizes the number of metastable States. , 2005, Physical review letters.

[59]  Nils Bertschinger,et al.  Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.

[60]  S. Wise,et al.  Comparison of learning‐related neuronal activity in the dorsal premotor cortex and striatum , 2004, The European journal of neuroscience.

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

[62]  Victor A. F. Lamme,et al.  Source (or Part of the following Source): Type Article Title Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure Ground Perception Author(s) Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure–ground Perception Materials and Methods , 2022 .

[63]  Steven A Boring,et al.  A novel food-delivery device for neurophysiological and neuropsychological studies in monkeys , 2001, Journal of Neuroscience Methods.

[64]  S. Wise,et al.  Arbitrary associations between antecedents and actions , 2000, Trends in Neurosciences.

[65]  A. Grinvald,et al.  Linking spontaneous activity of single cortical neurons and the underlying functional architecture. , 1999, Science.

[66]  D. Turcotte,et al.  Self-organized criticality , 1999 .

[67]  L. Paninski,et al.  Information about movement direction obtained from synchronous activity of motor cortical neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[68]  E. Miller,et al.  Neural Activity in the Primate Prefrontal Cortex during Associative Learning , 1998, Neuron.

[69]  Alessandro Vespignani,et al.  How self-organized criticality works: A unified mean-field picture , 1997, cond-mat/9709192.

[70]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[71]  Stanley,et al.  Self-organized branching processes: Mean-field theory for avalanches. , 1995, Physical review letters.

[72]  Idan Segev,et al.  The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells , 1992, Neural Computation.

[73]  C. Koch,et al.  Synaptic Background Activity Influences Spatiotemporal Integration in Single Pyramidal Cells. , 1991, The Biological bulletin.

[74]  S. Wise,et al.  A neurophysiological study of the premotor cortex in the rhesus monkey. , 1984, Brain : a journal of neurology.

[75]  G L Gerstein,et al.  Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. , 1972, Biophysical journal.

[76]  D. Perkel,et al.  Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation , 1969, Science.