Modular co-organization of functional connectivity and scale-free dynamics in the human brain

Scale-free neuronal dynamics and interareal correlations are emergent characteristics of spontaneous brain activity. How such dynamics and the anatomical patterns of neuronal connectivity are mutually related in brain networks has, however, remained unclear. We addressed this relationship by quantifying the network colocalization of scale-free neuronal activity—both neuronal avalanches and long-range temporal correlations (LRTCs)—and functional connectivity (FC) by means of intracranial and noninvasive human resting-state electrophysiological recordings. We found frequency-specific colocalization of scale-free dynamics and FC so that the interareal couplings of LRTCs and the propagation of neuronal avalanches were most pronounced in the predominant pathways of FC. Several control analyses and the frequency specificity of network colocalization showed that the results were not trivial by-products of either brain dynamics or our analysis approach. Crucially, scale-free neuronal dynamics and connectivity also had colocalized modular structures at multiple levels of network organization, suggesting that modules of FC would be endowed with partially independent dynamic states. These findings thus suggest that FC and scale-free dynamics—hence, putatively, neuronal criticality as well—coemerge in a hierarchically modular structure in which the modules are characterized by dense connectivity, avalanche propagation, and shared dynamic states. Author Summary The framework of criticality has been suggested to explain the scale-free dynamics of neuronal activity in complex interaction networks. However, the in vivo relationship between scale-free dynamics and functional connectivity (FC) has remained unclear. We used human intracranial and noninvasive electrophysiological measurements to map scale-free dynamics and connectivity. We found that the propagation of fast activity avalanches and the interareal coupling of slow, long-range temporal correlations—two key forms of scale-free neuronal dynamics—were nontrivially colocalized with the strongest functional connections. Most importantly, scale-free dynamics and FC exhibited similar modular network structures. FC and scale-free dynamics, and possibly also neuronal criticality, appear to co-emerge in a modular architecture in which the modules are characterized internally by shared dynamic states, avalanche propagation, and dense functional connectivity.

[1]  M. Corbetta,et al.  A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain , 2012, Neuron.

[2]  Edward T. Bullmore,et al.  Modular and Hierarchically Modular Organization of Brain Networks , 2010, Front. Neurosci..

[3]  Mark W. Woolrich,et al.  Adding dynamics to the Human Connectome Project with MEG , 2013, NeuroImage.

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

[5]  C. Hilgetag,et al.  Hierarchical modular brain connectivity is a stretch for criticality , 2014, Trends in Cognitive Sciences.

[6]  Andreas Ziehe,et al.  Constructing Surrogate Data to Control for Artifacts of Volume Conduction for Functional Connectivity Measures , 2010 .

[7]  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.

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

[9]  J. Palva,et al.  Relationship of Fast- and Slow-Timescale Neuronal Dynamics in Human MEG and SEEG , 2015, The Journal of Neuroscience.

[10]  L. Cammoun,et al.  The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI , 2012, PloS one.

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

[12]  Dietmar Plenz,et al.  Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches , 2009, PLoS Comput. Biol..

[13]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[14]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[15]  M. Raichle,et al.  Lag threads organize the brain’s intrinsic activity , 2015, Proceedings of the National Academy of Sciences.

[16]  Onerva Korhonen,et al.  Sparse weightings for collapsing inverse solutions to cortical parcellations optimize M/EEG source reconstruction accuracy , 2014, Journal of Neuroscience Methods.

[17]  Gustavo Deco,et al.  Resting brains never rest: computational insights into potential cognitive architectures , 2013, Trends in Neurosciences.

[18]  Leonardo L. Gollo,et al.  Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.

[19]  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.

[20]  Gabriele Arnulfo,et al.  Phase and amplitude correlations in resting-state activity in human stereotactical EEG recordings , 2015, NeuroImage.

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

[22]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[23]  Vadim V. Nikulin,et al.  Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia , 2012, NeuroImage.

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

[25]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[26]  Arjen van Ooyen,et al.  Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease , 2009, Proceedings of the National Academy of Sciences.

[27]  Theiler,et al.  Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.

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

[29]  M. Kramer,et al.  Beyond the Connectome: The Dynome , 2014, Neuron.

[30]  Klaus Linkenkaer-Hansen,et al.  Breakdown of Long-Range Temporal Correlations in Theta Oscillations in Patients with Major Depressive Disorder , 2005, The Journal of Neuroscience.

[31]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[32]  Luc Berthouze,et al.  Markers of criticality in phase synchronization , 2014, Front. Syst. Neurosci..

[33]  J. Matias Palva,et al.  Modulation of critical brain dynamics using closed-loop neurofeedback stimulation , 2016, Clinical Neurophysiology.

[34]  Eric J Friedman,et al.  Hierarchical networks, power laws, and neuronal avalanches. , 2013, Chaos.

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

[36]  Alan C. Evans,et al.  Multi-level bootstrap analysis of stable clusters in resting-state fMRI , 2009, NeuroImage.

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

[38]  Mariano Sigman,et al.  A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks , 2011, Proceedings of the National Academy of Sciences.

[39]  Stefan Haufe,et al.  The effect of linear mixing in the EEG on Hurst exponent estimation , 2014, NeuroImage.

[40]  M. Raichle A brief history of human brain mapping , 2009, Trends in Neurosciences.

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

[42]  Olaf Sporns,et al.  Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons , 2011, PLoS Comput. Biol..

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

[44]  Gabriele Arnulfo,et al.  Stereoelectroencephalography: surgical methodology, safety, and stereotactic application accuracy in 500 procedures. , 2013, Neurosurgery.

[45]  T. Ros,et al.  Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity , 2015, Cerebral cortex.

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

[47]  Kazuyuki Aihara,et al.  Unambiguous reconstruction of network structure using avalanche dynamics. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Gabriele Arnulfo,et al.  Automatic segmentation of deep intracerebral electrodes in computed tomography scans , 2015, BMC Bioinformatics.

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

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

[51]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[52]  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.

[53]  H. J. Herrmann,et al.  Brain modularity controls the critical behavior of spontaneous activity , 2014, Scientific Reports.

[54]  Patrice Abry,et al.  Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks , 2014, NeuroImage.

[55]  S. Taulu,et al.  Applications of the signal space separation method , 2005, IEEE Transactions on Signal Processing.

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

[57]  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.

[58]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[59]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[60]  Mark W. Woolrich,et al.  Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage , 2012, NeuroImage.

[61]  J. Palva,et al.  Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs , 2012, Trends in Cognitive Sciences.

[62]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[63]  J. Palva,et al.  Epileptogenic neocortical networks are revealed by abnormal temporal dynamics in seizure-free subdural EEG. , 2007, Cerebral cortex.

[64]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[65]  J. Touboul,et al.  Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic Dynamics? , 2009, PloS one.

[66]  D. Plenz,et al.  The organizing principles of neuronal avalanches: cell assemblies in the cortex? , 2007, Trends in Neurosciences.

[67]  Susan A. Sadek,et al.  A Shift to Randomness of Brain Oscillations in People with Autism , 2010, Biological Psychiatry.