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A Topological Criterion for Filtering Information in Complex Brain Networks.

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We developed a new method, Efficiency Cost Optimization (ECO), to objectively threshold inferred biological networks. This approach balances network efficiency and wiring cost, revealing intrinsic properties while maintaining sparsity.

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Biological networks are often inferred from experimental data.
  • Thresholding is used to create sparse networks from dense inferred data, but objective criteria are lacking.
  • Network properties depend heavily on the chosen threshold.

Purpose of the Study:

  • To introduce a principled criterion, Efficiency Cost Optimization (ECO), for selecting thresholds in inferred biological networks.
  • To optimize the trade-off between network efficiency and wiring cost.
  • To provide a method for objective network analysis and comparison.

Main Methods:

  • Developed the Efficiency Cost Optimization (ECO) criterion.
  • Analytical proof and numerical simulations to validate the method.
  • Applied ECO to various brain networks across different scales and imaging modalities.
  • Compared ECO with alternative filtering methods for brain state discrimination.

Main Results:

  • ECO objectively selects thresholds that balance network efficiency and wiring cost.
  • The optimal connection density follows a power-law dependent on network size, allowing a-priori threshold determination.
  • ECO effectively highlights intrinsic network properties and preserves sparsity.
  • ECO demonstrates potential in discriminating brain states.

Conclusions:

  • ECO offers a fast, principled, and objective method for analyzing and comparing inferred biological networks.
  • This approach advances the understanding of network structure and function across biological scales.
  • ECO provides a robust alternative to arbitrary thresholding methods.