Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

External brain stimulation is a promising tool for investigating and altering cognitive processes, with potential clinical applications to the restoration of dysfunctional neural dynamics. In line with experimental observations, we study how the effects of stimulation crucially depend on the ongoing dynamics of the brain, at the local level of the stimulated region but also of global coordinated brain activity. Specifically, we use connectome-based whole-brain computational modeling to explore how the effects of single-pulse stimulation to different regions strongly depend on both the phase of regional oscillatory activity and on the transiently occurring network of functional connectivity at the time of the applied stimulation. Importantly, we show that stimulation has not only state-dependent effects but can also induce global state switching. Lastly, predicting the effect of stimulation by using machine learning shows that functional network-aware measures (i.e., knowledge of either a discrete state of functional connectivity or of a detailed functional connectivity matrix) can increase the performance by up to 40%. Our results suggest that a fine characterization of intrinsic functional connectivity dynamics is essential for improving the reliability of exogenous stimulation.