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Graphlet characteristics in directed networks.

Igor Trpevski1, Tamara Dimitrova1, Tommy Boshkovski1

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This study introduces a new graphlet analysis for directed brain networks, revealing distinct patterns in excitatory versus inhibitory connections. Excitatory networks show stronger causal influences, offering new insights into brain connectivity.

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Area of Science:

  • Network theory
  • Computational neuroscience
  • Brain connectivity analysis

Background:

  • Graphlet analysis provides a null-model-independent method for characterizing local network structure.
  • Understanding directed brain effective connectivity, including the sign of connections (excitatory/inhibitory), is crucial for neuroscience.

Purpose of the Study:

  • To develop and apply a novel graphlet-based method for analyzing directed brain effective connectivity networks.
  • To investigate the differences in network structure and causal patterns between excitatory and inhibitory brain networks.

Main Methods:

  • Computed signature vectors for each vertex and graphlet correlation matrices for directed brain networks.
  • Applied the method to effective connectivity data from 40 healthy subjects, considering connection direction and sign.
  • Analyzed significant correlations in the graphlet correlation matrix and Granger causality (G-causes and G-effects).

Main Results:

  • Signature vectors (node, wedge, triangle degrees) were found to be dominant in excitatory effective brain networks.
  • Significant correlations (>0.7 or <-0.7) present in over 60% of subjects revealed stronger causal patterns in excitatory networks compared to inhibitory networks.

Conclusions:

  • The proposed graphlet analysis method effectively characterizes directed brain networks.
  • Excitatory effective brain networks exhibit more pronounced causal relationships than inhibitory networks, highlighting the importance of connection sign in brain function.