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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Navigation of brain networks.

Caio Seguin1, Martijn P van den Heuvel2,3, Andrew Zalesky4,5

  • 1Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC 3010, Australia; caioseguin@gmail.com.

Proceedings of the National Academy of Sciences of the United States of America
|June 1, 2018
PubMed
Summary
This summary is machine-generated.

Brain network navigation offers an efficient, decentralized communication strategy. This routing model, observed across mammals, optimizes information flow without needing global network knowledge.

Keywords:
complex networksconnectomenetwork navigationneural communication

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Understanding neural communication in large-scale brain networks is crucial.
  • Current models often assume global knowledge of network topology.

Purpose of the Study:

  • To investigate navigation as a parsimonious routing model for connectomics.
  • To quantify navigation efficiency in mammalian brain networks.

Main Methods:

  • Developed a measure for navigation efficiency.
  • Analyzed connectomes from human, mouse, and macaque species.
  • Simulated rewiring and node repositioning to assess topological impact.

Main Results:

  • Mammalian connectomes exhibit near-optimal navigation efficiency (>80%).
  • Network alterations significantly reduced navigation performance (45-60%).
  • Human connectome topology balances regularity and randomness for efficient navigation.

Conclusions:

  • Brain network topology and geometry support efficient decentralized communication via navigation.
  • Navigation promotes resource-efficient information traffic distribution.
  • This model explains functional connectivity variations without requiring global network awareness.