Cortical Hubs Form a Module for Multisensory Integration on Top of the Hierarchy of Cortical Networks

Sensory stimuli entering the nervous system follow particular paths of processing, typically separated (segregated) from the paths of other modal information. However, sensory perception, awareness and cognition emerge from the combination of information (integration). The corticocortical networks of cats and macaque monkeys display three prominent characteristics: (i) modular organisation (facilitating the segregation), (ii) abundant alternative processing paths and (iii) the presence of highly connected hubs. Here, we study in detail the organisation and potential function of the cortical hubs by graph analysis and information theoretical methods. We find that the cortical hubs form a spatially delocalised, but topologically central module with the capacity to integrate multisensory information in a collaborative manner. With this, we resolve the underlying anatomical substrate that supports the simultaneous capacity of the cortex to segregate and to integrate multisensory information.

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

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

[3]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

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

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

[6]  Prasad Tetali,et al.  Simple Markov-Chain Algorithms for Generating Bipartite Graphs and Tournaments (Extended Abstract) , 1999, SODA.

[7]  Jack W Scannell,et al.  The connectional organization of neural systems in the cat cerebral cortex , 1993, Current Biology.

[8]  J. Anthonisse The rush in a directed graph , 1971 .

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

[10]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[11]  J. Kelso,et al.  Cortical coordination dynamics and cognition , 2001, Trends in Cognitive Sciences.

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  Marc-Thorsten Hütt,et al.  Organization of Excitable Dynamics in Hierarchical Biological Networks , 2008, PLoS Comput. Biol..

[14]  D. P. Russell,et al.  Functional Clustering: Identifying Strongly Interactive Brain Regions in Neuroimaging Data , 1998, NeuroImage.

[15]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[16]  R. Knight Neural Networks Debunk Phrenology , 2007, Science.

[17]  J. Kurths,et al.  Structure–function relationship in complex brain networks expressed by hierarchical synchronization , 2007 .

[18]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[19]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[20]  P. Holland,et al.  The Statistical Analysis of Local Structure in Social Networks , 1974 .

[21]  G Tononi,et al.  A complexity measure for selective matching of signals by the brain. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[22]  M P Young,et al.  Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[23]  Peter Andras,et al.  Simulation of robustness against lesions of cortical networks , 2007, The European journal of neuroscience.

[24]  M P Young,et al.  On imputing function to structure from the behavioural effects of brain lesions. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[25]  M Fahle,et al.  Figure–ground discrimination from temporal information , 1993, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[27]  C. Blakemore,et al.  Analysis of connectivity in the cat cerebral cortex , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[28]  J. Fuster The cognit: a network model of cortical representation. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[29]  L. Katz,et al.  PROBABILITY DISTRIBUTIONS OF RANDOM VARIABLES ASSOCIATED WITH A STRUCTURE OF THE SAMPLE SPACE OF SOCIOMETRIC INVESTIGATIONS , 1957 .

[30]  John M. Roberts Simple methods for simulating sociomatrices with given marginal totals , 2000, Soc. Networks.

[31]  A. Rao,et al.  A Markov chain Monte carol method for generating random (0, 1)-matrices with given marginals , 1996 .

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

[33]  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.

[34]  Prasad Tetali,et al.  Simple Markov-chain algorithms for generating bipartite graphs and tournaments , 1997, SODA '97.

[35]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[36]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[37]  K. Jellinger Cortex and Mind. Unifying Cognition , 2003 .

[38]  Cornelis J Stam,et al.  Graph theoretical analysis of complex networks in the brain , 2007, Nonlinear biomedical physics.

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

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

[41]  I. K. Wood,et al.  Neuroscience: Exploring the brain , 1996 .

[42]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[43]  Olaf Sporns,et al.  Classes of network connectivity and dynamics , 2001, Complex..

[44]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  L. Robertson Binding, spatial attention and perceptual awareness , 2003, Nature Reviews Neuroscience.

[46]  F. Sommer,et al.  Global Relationship between Anatomical Connectivity and Activity Propagation in the Cerebral Cortex , 2022 .

[47]  A. Damasio Time-locked multiregional retroactivation: A systems-level proposal for the neural substrates of recall and recognition , 1989, Cognition.

[48]  G. Tononi An information integration theory of consciousness , 2004, BMC Neuroscience.

[49]  Jürgen Kurths,et al.  Structural and functional clusters of complex brain networks , 2006 .