title: Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs.
abstract: The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.
authors.affiliation: Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.|Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Gorka
authors.initials: G
authors.lastname: Zamora-López
authors.affiliation: Department of Physics, Hong Kong Baptist University, Hong Kong, China.|Centre for Nonlinear Studies, Hong Kong Baptist University, Hong Kong, China.|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, P.R. China.
authors.forename: Yuhan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.|Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.|Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Deco
authors.affiliation: Department of Psychiatry, University of Oxford, Oxford, UK.|Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark.|Oxford Functional Neurosurgery and Experimental Neurology Group, Nuffield Departments of Clinical Neuroscience and Surgical Sciences, University of Oxford, UK.
authors.forename: Morten L
authors.initials: ML
authors.lastname: Kringelbach
authors.affiliation: Department of Physics, Hong Kong Baptist University, Hong Kong, China.|Centre for Nonlinear Studies, Hong Kong Baptist University, Hong Kong, China.|Beijing Computational Science Research Center, Beijing, China.|Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen, China.|The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems, Hong Kong China.
authors.forename: Changsong
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology*; D017173:Caenorhabditis elegans / Q000502:physiology; D002415:Cats; D063132:Connectome; D006801:Humans; D008251:Macaca / Q000502:physiology; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology*; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology*