Whole-brain computational modeling based on structural connectivity measures has shown great promise in successfully simulating fMRI BOLD signals that includes temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in co-activation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduce frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first show that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Our analysis of static and dynamic measures of simulated BOLD fMRI signals support the notion that the frequency of each oscillator is modulated by flow of activity in its neighborhood. Second, we examine the effects of simulated lesions in gray brain matter by applying a new perturbation protocol to our model. We present a new approach to map the effects of vulnerability in brain networks and introducing a measure of regional hazardousness based on mapping of the degree of divergence in a feature space consisting of graph theoretical measures.
Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in co-activation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space 
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Our analysis of static and dynamic measures of simulated BOLD fMRI signals support the notion that the frequency of each oscillator is modulated by flow of activity in its neighborhood.
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consisting of graph theoretical measures.