Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome

BackgroundThe topological structure of the wiring of the mammalian brain cortex plays an important role in shaping the functional dynamics of large-scale neural activity. Due to their central embedding in the network, high degree hub regions and their connections (often referred to as the ‘rich club’) have been hypothesized to facilitate intermodular neural communication and global integration of information by means of synchronization. Here, we examined the theoretical role of anatomical hubs and their wiring in brain dynamics. The Kuramoto model was used to simulate interaction of cortical brain areas by means of coupled phase oscillators—with anatomical connections between regions derived from diffusion weighted imaging and module assignment of brain regions based on empirically determined resting-state data.ResultsOur findings show that synchrony among hub nodes was higher than any module’s intramodular synchrony (p < 10−4, for cortical coupling strengths, λ, in the range 0.02 < λ < 0.05), suggesting that hub nodes lead the functional modules in the process of synchronization. Furthermore, suppressing structural connectivity among hub nodes resulted in an elevated modular state (p < 4.1 × l0−3, 0.015 < λ < 0.04), indicating that hub-to-hub connections are critical in intermodular synchronization. Finally, perturbing the oscillatory behavior of hub nodes prevented functional modules from synchronizing, implying that synchronization of functional modules is dependent on the hub nodes’ behavior.ConclusionOur results converge on anatomical hubs having a leading role in intermodular synchronization and integration in the human brain.

[1]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[2]  O. Sporns,et al.  High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.

[3]  M. Shanahan.  The brain's connective core and its role in animal cognition , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[4]  Vito Latora,et al.  Remote synchronization reveals network symmetries and functional modules. , 2012, Physical review letters.

[5]  M. A. Muñoz,et al.  Frustrated hierarchical synchronization and emergent complexity in the human connectome network , 2014, Scientific Reports.

[6]  Olaf Sporns,et al.  Can structure predict function in the human brain? , 2010, NeuroImage.

[7]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[8]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[9]  Daniel A. Braun,et al.  Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences , 2014, Front. Hum. Neurosci..

[10]  M. V. D. Heuvel,et al.  Simulated rich club lesioning in brain networks: a scaffold for communication and integration? , 2014, Front. Hum. Neurosci..

[11]  Gustavo Deco,et al.  Rich club organization supports a diverse set of functional network configurations , 2014, NeuroImage.

[12]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[13]  Brian D. Mills,et al.  Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology , 2014, The Journal of Neuroscience.

[14]  Joaquín Goñi,et al.  Abnormal rich club organization and functional brain dynamics in schizophrenia. , 2013, JAMA psychiatry.

[15]  Simon Farmer,et al.  Comment on “Broadband Criticality of Human Brain Network Synchronization” by Kitzbichler MG, Smith ML, Christensen SR, Bullmore E (2009) PLoS Comput Biol 5: e1000314 , 2015, PLoS Comput. Biol..

[16]  Emma K. Towlson,et al.  The Rich Club of the C. elegans Neuronal Connectome , 2013, The Journal of Neuroscience.

[17]  Alex Arenas,et al.  Synchronization reveals topological scales in complex networks. , 2006, Physical review letters.

[18]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[19]  M. Young,et al.  Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[20]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[21]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[22]  Gustavo Deco,et al.  How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model , 2012, Front. Comput. Neurosci..

[23]  W. Singer,et al.  Dynamic predictions: Oscillations and synchrony in top–down processing , 2001, Nature Reviews Neuroscience.

[24]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Philipp Hövel,et al.  Functional connectivity of distant cortical regions: Role of remote synchronization and symmetry in interactions , 2014, NeuroImage.

[26]  W. Singer,et al.  Neuronal assemblies: necessity, signature and detectability , 1997, Trends in Cognitive Sciences.

[27]  Maurizio Corbetta,et al.  Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity , 2015, PLoS Comput. Biol..

[28]  G. Edelman,et al.  Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Gustavo Deco,et al.  Role of local network oscillations in resting-state functional connectivity , 2011, NeuroImage.

[30]  Marcus Kaiser,et al.  Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems , 2006, PLoS Comput. Biol..

[31]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[32]  Changsong Zhou,et al.  Graph analysis of cortical networks reveals complex anatomical communication substrate. , 2009, Chaos.

[33]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

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

[35]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[36]  Changsong Zhou,et al.  Hierarchical organization unveiled by functional connectivity in complex brain networks. , 2006, Physical review letters.

[37]  O. Sporns,et al.  Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication , 2012, PloS one.

[38]  M. P. van den Heuvel,et al.  Rich Club Organization and Intermodule Communication in the Cat Connectome , 2013, The Journal of Neuroscience.

[39]  Morten L. Kringelbach,et al.  Modeling the outcome of structural disconnection on resting-state functional connectivity , 2012, NeuroImage.

[40]  Markus Brede,et al.  Locals vs. global synchronization in networks of non-identical Kuramoto oscillators , 2008 .

[41]  Ruben Schmidt,et al.  Linking Macroscale Graph Analytical Organization to Microscale Neuroarchitectonics in the Macaque Connectome , 2014, The Journal of Neuroscience.

[42]  Martijn P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks , 2013, NeuroImage.

[43]  W. Singer,et al.  Abnormal neural oscillations and synchrony in schizophrenia , 2010, Nature Reviews Neuroscience.

[44]  Andreas Daffertshofer,et al.  Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model , 2010, Front. Hum. Neurosci..

[45]  Marcus Kaiser,et al.  Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens , 2011, Network.

[46]  Morten L. Kringelbach,et al.  Exploring the network dynamics underlying brain activity during rest , 2014, Progress in Neurobiology.

[47]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[48]  O. Sporns,et al.  Dynamical consequences of lesions in cortical networks , 2008, Human brain mapping.

[49]  Patric Hagmann,et al.  Mapping the human connectome at multiple scales with diffusion spectrum MRI , 2012, Journal of Neuroscience Methods.

[50]  W. Singer,et al.  Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[51]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[52]  M. P. van den Heuvel,et al.  Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.

[53]  R. Kahn,et al.  Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain , 2009, Human brain mapping.

[54]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[55]  Alex Arenas,et al.  From Modular to Centralized Organization of Synchronization in Functional Areas of the Cat Cerebral Cortex , 2010, PloS one.

[56]  O. Sporns,et al.  An Anatomical Substrate for Integration among Functional Networks in Human Cortex , 2013, The Journal of Neuroscience.

[57]  S Dehaene,et al.  A neuronal model of a global workspace in effortful cognitive tasks. , 1998, Proceedings of the National Academy of Sciences of the United States of America.