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An Evolutionary Game Theory Model of Spontaneous Brain Functioning.

Dario Madeo1,2, Agostino Talarico3, Alvaro Pascual-Leone4

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This study introduces an evolutionary game theory model to understand brain network dynamics. The model explains resting-state functional connectivity and simulates network lesions, offering a new framework for neuroscience.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • The brain comprises interconnected regions forming spontaneous networks.
  • Information integration within and between brain networks occurs continuously, even at rest.
  • Resting-state brain dynamics predict cognitive profiles and may indicate neurological/psychiatric conditions.

Purpose of the Study:

  • To develop a theoretical and mathematical model for brain interregional dynamics.
  • To capture spontaneous activity in resting-state networks using functional magnetic resonance imaging (fMRI).
  • To explore the potential of evolutionary game theory on networks (EGN) in understanding brain function.

Main Methods:

  • An extension of evolutionary game theory on networks (EGN) was developed.
  • The model balances emulative and non-emulative attitudes among brain regions.
  • The model's output was related to blood-oxygen-level-dependent (BOLD) fMRI signals.

Main Results:

  • The EGN model generates low-frequency oscillatory behaviors mimicking functional connectivity dynamics.
  • The model can approximate fMRI time series and simulate the impact of network lesions.
  • Evidence for compensatory mechanisms across brain networks was observed.

Conclusions:

  • Evolutionary game theory on networks provides a novel framework for understanding human brain network dynamics.
  • The model successfully captures key aspects of resting-state brain activity.
  • This approach offers insights into brain function and potential markers for neurological conditions.