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Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

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  • Gorka Zamora-López, Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain. Electronic address: tristan.nakagawa@upf.edu.
  • ,
  • Yuhan Chen, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, P.R. China.
  • ,
  • Gustavo Deco, Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.
  • ,
  • Morten L Kringelbach
  • Changsong Zhou, The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems, Hong Kong China.

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.

Original languageEnglish
Article number38424
JournalScientific Reports
Volume6
ISSN2045-2322
DOIs
Publication statusPublished - 5 Dec 2016

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  • Journal Article

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