Evolutionary games on networks for modeling spontaneous brain functioning

Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is in a quiescent state at rest. In fact, the same brain regions contributing to our ability to think, speak and remember, show coordinated activity also while we are not engaged in specific tasks, suggesting that it is a preparatory state for any subsequent activity. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitutes a potential marker in pathological condition, thus making its understanding a crucial quest for modern neuroscience. However, how and why such complex, spontaneous activity emerges is still unknown. In this thesis work, it is presented a theoretical and mathematical model where the interaction between brain regions can be modeled as an evolutionary game on network (EGN); here, each region behaves as a player which maximizes its fitness by monitoring other players behaviour. The proposed model, labeled as EGN-B, is based on nonlinear emulative and non-emulative behaviors, where the balancing between these two attitudes is responsible for the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by spontaneous positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, EGN-B model is able to mimic network dynamics, approximate fMRI time series on the basis of an initial subset of available input, as well as simulate the impact of network lesions, providing initial evidence of compensation mechanisms across networks. Results suggest EGN as a new potential framework for the understanding of human brain network dynamics.