The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

Abstract In order to promote survival through flexible cognition and goal‐directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so‐called rich club which show dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole‐brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time (“dynamical workspace of binding nodes”). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone. HighlightsWe propose a novel method for finding the most functionally relevant brain regions for binding information over time.This “dynamical workspace of binding nodes” is determined using causal whole‐brain computational modelling.We compare the functional dynamical significance of binding nodes to the rich club members.Removal of binding nodes compared to rich club nodes significantly decreases integration.

[1]  Abraham Z. Snyder,et al.  A brief history of the resting state: The Washington University perspective , 2012, NeuroImage.

[2]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[3]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[4]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[5]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[6]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

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

[8]  G. Tononi,et al.  Rethinking segregation and integration: contributions of whole-brain modelling , 2015, Nature Reviews Neuroscience.

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  M. Kringelbach,et al.  Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders , 2014, Neuron.

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

[12]  Heidi Johansen-Berg,et al.  Using diffusion imaging to study human connectional anatomy. , 2009, Annual review of neuroscience.

[13]  Christof Koch,et al.  Quantifying synergistic mutual information , 2012, ArXiv.

[14]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[15]  G. Goranović,et al.  Theory and simulation. , 1996, Current opinion in structural biology.

[16]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[17]  Giulio Tononi,et al.  Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework , 2008, PLoS Comput. Biol..

[18]  K. Amunts,et al.  Centenary of Brodmann's Map — Conception and Fate , 2022 .

[19]  C. Koch,et al.  Towards a neurobiological theory of consciousness , 1990 .

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

[21]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[22]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Olaf Sporns,et al.  Network attributes for segregation and integration in the human brain , 2013, Current Opinion in Neurobiology.

[24]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

[25]  Wulfram Gerstner,et al.  Theory and Simulation in Neuroscience , 2012, Science.

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

[27]  M. Corbetta,et al.  How Local Excitation–Inhibition Ratio Impacts the Whole Brain Dynamics , 2014, The Journal of Neuroscience.

[28]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[29]  Maurizio Corbetta,et al.  Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations , 2013, The Journal of Neuroscience.

[30]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

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

[32]  George Loewenstein,et al.  Social Projection of Transient Drive States , 2003, Personality & social psychology bulletin.

[33]  Morten L. Kringelbach,et al.  Balancing the Brain: Resting State Networks and Deep Brain Stimulation , 2011, Front. Integr. Neurosci..

[34]  Joaquín Goñi,et al.  A Network Convergence Zone in the Hippocampus , 2014, PLoS Comput. Biol..

[35]  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.

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

[37]  M. Corbetta,et al.  The Dynamical Balance of the Brain at Rest , 2011, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[38]  Olaf Sporns,et al.  MR connectomics: Principles and challenges , 2010, Journal of Neuroscience Methods.

[39]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[40]  Erick Jorge Canales-Rodríguez,et al.  Brain hemispheric structural efficiency and interconnectivity rightward asymmetry in human and nonhuman primates. , 2011, Cerebral cortex.

[41]  W. Singer,et al.  Temporal binding and the neural correlates of sensory awareness , 2001, Trends in Cognitive Sciences.

[42]  Ravi S. Menon,et al.  Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling , 2014, The Journal of Neuroscience.

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

[44]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[45]  Bing Wang,et al.  Diffusion-weighted MR imaging , 1999 .

[46]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

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

[48]  M. Kringelbach,et al.  Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using A Whole-Brain Computational Perspective , 2016, Trends in Neurosciences.

[49]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

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

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

[52]  B. Baars A cognitive theory of consciousness , 1988 .

[53]  Henry Kennedy,et al.  Cortical High-Density Counterstream Architectures , 2013, Science.

[54]  Hamid Reza Mohseni,et al.  Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations , 2014, NeuroImage.

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

[56]  M. Kringelbach,et al.  The Rediscovery of Slowness: Exploring the Timing of Cognition , 2015, Trends in Cognitive Sciences.

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

[58]  C. Koch,et al.  Information integration without awareness , 2014, Trends in Cognitive Sciences.

[59]  Habib Benali,et al.  Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities , 2014, PLoS Comput. Biol..

[60]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[61]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[62]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[63]  Kenneth H. Norwich,et al.  Information, sensation, and perception , 1993 .

[64]  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.

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