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Whole-brain computational modeling based on structural connectivity has showngreat promise in successfully simulating fMRI BOLD signals with temporalco-activation patterns that are highly similar to empirical functionalconnectivity patterns during resting state. Importantly, previous studies haveshown that spontaneous fluctuations in co-activation patterns of distributedbrain regions have an inherent dynamic nature with regard to the frequencyspectrum of intrinsic brain oscillations. In this modeling study, we introducedfrequency dynamics into a system of coupled oscillators, where each oscillatorrepresents the local mean-field model of a brain region. We first showed thatthe collective behavior of interacting oscillators reproduces previously shownfeatures of brain dynamics. Second, we examined the effect of simulated lesionsin gray matter by applying an in silico perturbation protocol to the brainmodel. We present a new approach to map the effects of vulnerability in brainnetworks and introduce a measure of regional hazardousness based on mapping ofthe degree of divergence in a feature space

Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates