EÖTVÖS LORÁND UNIVERSITY FACULTY OF SCIENCE SIGNAL FLOW HIERARCHY IN A MULTI-SCALE DYNAMIC NETWORK MODEL OF THE CEREBRAL CORTEX

The cerebral cortex is a network with a nested hierarchy. Neural communication gives rise to complex dynamics characterizing the signal flow in the cortical network. However, the relationship between structure and function is highly non-trivial. While physical connections do impose constraints on signal flow, the observable statistical interdependencies between the elements of the network show time-varying patterns. The understanding of this dynamics is of special interest since it would grant us the ability to diagnose the workings of the brain in health and disease. The goal of my studies was to implement and analyze an anatomically and physiologically realistic dynamic model of a large-scale network of cortical areas. To this end the functioning of the cortical areas were modelled by a multi-compartment neural mass model in a directed and weighted hierarchical network. Structure-function relationship was studied by correlating three measures: 1) convergence degree (CD), a topological measure of signal flow, 2) an anatomical index of cortical hierarchy and 3) dynamical dependence by computing spectral Granger causalities between areas. To obtain a biologically relevant CD I introduced a modified version of the shortest path structure of the weighted graph, which favours robustness in the expense of the winner-take-all approach. CD exposed a densely connected component of higher-order areas resembling the rich club of the network that was not seen in the anatomical hierarchy. Due to computational limitations structure-function relationship was studied in a subnetwork of eight areas. Remarkably, CD significantly correlated with the empirical index of anatomical hierarchy as well as with the causal relationship of areal activities. This is the first study showing the close correspondence of network topology and dynamics in a weighted and hierarchical model of the large-scale cortical network.

[1]  J. E. Boodin.  Organization of cognition. , 1939 .

[2]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[3]  L. Ectors.  [The organization of the central nervous system]. , 1967, Annales de la Societe royale des sciences medicales et naturelles de Bruxelles.

[4]  H. Barlow.  Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .

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

[6]  A. Crofts,et al.  Structure and function of the -complex of , 1992 .

[7]  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.

[8]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  J. Fuster.  Upper processing stages of the perception–action cycle , 2004, Trends in Cognitive Sciences.

[10]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[11]  P. Érdi,et al.  Computational neuropharmacology: dynamical approaches in drug discovery. , 2006, Trends in pharmacological sciences.

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

[13]  T. Nepusz,et al.  Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas , 2008, Proceedings of the Royal Society B: Biological Sciences.

[14]  L. Abbott,et al.  Theoretical Neuroscience Rising , 2008, Neuron.

[15]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

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

[17]  Karl J. Friston.  The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[18]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[19]  M. B'anyai,et al.  Organization of signal flow in directed networks , 2010, 1007.0566.

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

[21]  Karl J. Friston,et al.  Canonical Microcircuits for Predictive Coding , 2012, Neuron.

[22]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[23]  G. Rangarajan,et al.  Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Henry Kennedy,et al.  A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule , 2013, Neuron.

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

[26]  P. Roelfsema,et al.  Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex , 2014, Proceedings of the National Academy of Sciences.

[27]  Nikola T. Markov,et al.  Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex , 2013, The Journal of comparative neurology.

[28]  2 Nerve Cells , Neural Circuitry , and Behavior , 2015 .

[29]  H. Kennedy,et al.  Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels , 2014, Neuron.

[30]  J. Cowan,et al.  Wilson–Cowan Equations for Neocortical Dynamics , 2016, Journal of mathematical neuroscience.

[31]  Xiao-Jing Wang,et al.  Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex , 2016, Science Advances.

[32]  Michael J. Prerau,et al.  Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis. , 2017, Physiology.

[33]  M. Breakspear.  Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[34]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[35]  Olaf Sporns,et al.  Communication dynamics in complex brain networks , 2017, Nature Reviews Neuroscience.

[36]  A. Griffa,et al.  The road ahead in clinical network neuroscience , 2019, Network Neuroscience.

[37]  Sacha Jennifer van Albada,et al.  An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain , 2019, Network Neuroscience.

[38]  Claus C. Hilgetag,et al.  ‘Hierarchy’ in the organization of brain networks , 2020, Philosophical Transactions of the Royal Society B.