Probing Neural Networks for Dynamic Switches of Communication Pathways

Abstract

Dynamic communication and routing play important roles in the human brain to facilitate 2exibility in task solving and thought processes. Here, we present a network perturbation methodology that allows to investigate dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. Specifically, we found that 60.1% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This indicates that phase offsets and coupling delays play an important computational role for the dynamic switching between communication pathways in the brain.