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Functional brain networks formed using cross-sample entropy are scale free.

Walter S Pritchard1, Paul J Laurienti, Jonathan H Burdette

  • 11 Department of Social Sciences, Surry Community College , Dobson, North Carolina.

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An entropy-based method reveals nonlinear brain network connections, showing scale-free properties unlike linear methods. This suggests human brain networks exhibit scale-free characteristics, challenging previous assumptions.

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Network science is increasingly applied to understand the human brain.
  • Traditional methods use linear correlations (LinCorr) to map brain networks.
  • Nonlinear associations in brain connectivity remain underexplored.

Purpose of the Study:

  • To compare functional brain networks derived from linear correlation and entropy-based methods.
  • To investigate whether nonlinear techniques reveal scale-free properties in brain networks.
  • To assess differences in network topology, clustering, and assortativity.

Main Methods:

  • Applied an entropy-based method to resting-state fMRI data from 10 subjects.
  • Constructed functional brain networks using both entropy-based and LinCorr techniques.
  • Compared network properties including degree distribution, clustering coefficient, and path length.

Main Results:

  • Entropy-based networks exhibited power-law degree distributions, indicating scale-free properties.
  • These networks showed higher clustering coefficients and shorter path lengths than LinCorr networks.
  • Entropy-based networks were disassortative, featuring 'mega-hubs' connecting diverse nodes.

Conclusions:

  • Nonlinear analysis reveals distinct functional brain network structures compared to linear methods.
  • The findings support the hypothesis that human brain networks possess scale-free characteristics.
  • This study highlights the importance of nonlinear methods for a comprehensive understanding of brain connectivity.