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Coming up short: Generative network models fail to accurately capture long-range connectivity.

Stuart Oldham1,2, Alex Fornito2, Gareth Ball1,3

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Summary

Generative network models (GNMs) fail to capture brain network topography, particularly long-range connections crucial for hub locations. Careful evaluation is needed to understand their limitations in modeling brain organization.

Keywords:
Brain networkGenerative network modelHubsLong-range connectionsTopographyTopology

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Generative network models (GNMs) aim to explain brain connectome organization.
  • Current GNMs use simplified trade-offs but often miss spatial embedding of network properties.

Purpose of the Study:

  • Investigate diverse GNM formulations for connectome organization.
  • Identify limitations of current GNMs in capturing empirical brain network topography.

Main Methods:

  • Evaluated various generative network model formulations.
  • Assessed model performance in capturing long-range connectivity and hub topography.
  • Analyzed standard optimization and evaluation metrics for GNMs.

Main Results:

  • No tested GNM accurately captured empirical long-range connectivity patterns.
  • Spatial embedding of long-range connections is critical for hub location, a key GNM failure.
  • Standard evaluation metrics can obscure critical differences between models and empirical data.

Conclusions:

  • Existing GNMs have common failure modes in modeling brain network organization.
  • Failure to capture spatial embedding of long-range connections limits GNM accuracy.
  • Revising GNM formulations and evaluation metrics is essential for advancing connectome modeling.