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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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The relation between structural and functional connectivity patterns in complex brain networks.

C J Stam1, E C W van Straaten1, E Van Dellen2

  • 1Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|February 14, 2015
PubMed
Summary
This summary is machine-generated.

Simulations reveal that functional brain network interactions depend on direct connections and node degree product. Network properties, especially near critical transitions, reveal patterns in functional and effective connectivity.

Keywords:
Brain dynamicsEffective connectivityFunctional connectivityGraph theoryPhase transitionSIS model

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Area of Science:

  • Systems Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Understanding the relationship between brain structure and function is crucial in systems neuroscience.
  • Brain networks exhibit complex structural and functional interactions that are not fully understood.

Purpose of the Study:

  • To investigate how functional interactions between brain areas depend on direct structural connections and the degree product of nodes.
  • To explore the influence of network topology on emergent brain dynamics using a simulation model.

Main Methods:

  • Simulated brain dynamics on an empirical structural brain network using a Susceptible-Infected-Susceptible (SIS) model.
  • Analyzed phase transitions and statistical interdependencies between node states.
  • Quantified functional connectivity (correlation) and effective connectivity (conditional co-activation).

Main Results:

  • A phase transition was observed between inactive and active states at a critical transition rate ratio.
  • Functional connectivity matrices mirrored structural connectivity, while effective connectivity showed less resemblance.
  • Both connectivity measures were higher for directly connected nodes; effective connectivity scaled with degree product, especially in random networks.

Conclusions:

  • Simple dynamic models near critical transitions reveal clear patterns in functional brain network interactions.
  • The degree product significantly influences interaction strength, particularly for disconnected nodes and effective connectivity, likely due to indirect connections.
  • Functional and effective connectivity patterns are sensitive to the chosen measure and network properties.