bioRxiv2025
BackgroundBrain stimulation at specific frequencies has been shown to have therapeutic effects for certain neurological disorders. However, it remains unclear how these oscillatory inputs interact with neuronal dynamics and what drives this frequency selectivity. Here, we propose that this is achieved by targeting specific brain circuits through frequency-selective network entrainment. MethodsTo demonstrate that this type of entrainment can occur in the brain connectome structure, we use a minimal physics-based model of coupled oscillators that preserves only the essential structural connectivity of real brain networks. Using this model, we test how periodic stimulation influences network oscillatory dynamics, identifying sub-networks that synchronize with specific stimulation frequencies. Furthermore, we validate the suitability of the model to reproduce well-known frequency-selective entrainment using visual and auditory periodic stimulation. ResultsOur model reproduces selective network entrainment, whereby distinct stimulation frequencies favor the synchrony of different sub-networks. Crucially, entrainment exhibits a physics-predicted inverse relationship with frequency: lower-frequency stimulation favors broader synchrony across the network, while higher-frequency inputs produce spatially confined effects. These patterns emerge purely from network structure and oscillatory physics, independent of biological details. ConclusionThis physics-based approach elucidates the fundamental mechanistic principles governing frequency-selective brain network stimulation. Our minimal model demonstrates that selective entrainment of specific subnetworks can be achieved through frequency selection alone, largely determined by network structure and oscillatory dynamics. These findings provide a theoretical foundation for understanding how network architecture determines stimulation selectivity, supporting the development of principled approaches to targeted neuromodulation. Author summaryIn this work, we explore how external rhythmic stimulation interacts with the brains own rhythms. We use a physics-based model that represents the brain as a network of interconnected oscillators, linked according to the human connectome. Through this model, we show that stimulation effects are not limited to the targeted region but spread across the network, depending on the stimulation frequency, its spatial location and the underlying network dynamics. Our results reveal that low-frequency rhythms promote large-scale synchrony, while high-frequency rhythms engage more localized areas, all emerging from the brains structural connections and communication delays. The simulated cortical activation exhibits a frequency-selective profile that aligns strongly with that estimated from sensory-evoked electroencephalographic activity. This agreement suggests that differences between individuals in how their brain networks operate may explain why stimulation affects each person differently. Our findings offer a mechanistic framework for designing personalized stimulation protocols, illustrating how computational modeling can guide the optimization of noninvasive brain stimulation in both research and clinical contexts.