Communication in brain networks is the foundation of cognitive function and behavior. A multitude of evolutionary pressures, including the minimization of metabolic costs while maximizing communication efficiency, contribute to shaping the structure and dynamics of these networks. However, how communication efficiency is characterized depends on the assumed model of communication dynamics. Traditional models include shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Yet, a general and model-agnostic framework for characterizing optimal neural communication remains to be established.

In this non-technical/math-free presentation, I model communication efficiency via game theory, based on a combination of structural data from human cortical networks with computational models of brain dynamics. I show how communication patterns unfold in the given brain network if regions maximize their influence over one another. I then compare this communication landscape with a spectrum of brain communication models, showing that optimal communication most closely resembles a broadcasting model in which regions leverage multiple parallel channels for information dissemination. I discuss the implications of this regime, where hub regions exploit their topological vantage point by broadcasting across numerous pathways, thereby significantly enhancing their effective reach even when the anatomical connections are weak.

I then show how this specific solution gives rise to a disproportionate computational capacity, with which the brain is capable of handling complex tasks.