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EEG-based functional brain networks: does the network size matter?

Amir Joudaki1, Niloufar Salehi, Mahdi Jalili

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Plos One
|May 5, 2012
PubMed
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Network size significantly impacts electroencephalography (EEG) brain network metrics. Larger networks show higher efficiency and assortativity, but lower modularity, necessitating size consideration in comparative studies.

Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Functional brain connectivity can be modeled as networks using electroencephalography (EEG) signals.
  • Graph theory metrics are used to characterize these complex brain networks.

Purpose of the Study:

  • To investigate the influence of network size on various graph theory metrics derived from EEG functional connectivity.
  • To determine how network size affects measures like clustering coefficient, modularity, efficiency, and assortativity.

Main Methods:

  • EEG data were recorded from 32 healthy subjects.
  • Functional networks of three different sizes were constructed using a state-space based method to calculate cross-correlation matrices.
  • Binary adjacency connectomes were analyzed using graph metrics including clustering coefficient, modularity, efficiency, and assortativity.

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Last Updated: May 22, 2026

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Main Results:

  • Graph metric estimates were found to significantly differ based on network size.
  • Larger networks exhibited higher efficiency and assortativity compared to smaller networks of the same density.
  • Conversely, larger networks demonstrated lower modularity than smaller networks.

Conclusions:

  • Network size is a critical factor that must be accounted for when comparing functional brain networks across different studies.
  • The observed variations in graph metrics highlight the importance of standardized network sizes or size-aware analysis in EEG research.