Trade-offs between Complexity and Hub Presence in Network-Based Brain Models

Abstract

In the emerging field of computational neuroscience, the architecture of brain networks is a subject of intense study and debate. While models that only consider complex systems provide significant insights into neuronal interconnections, they often overlook the pivotal role of brain hubs—central nodes that manage a large number of connections. On the other hand, giving too much importance to brain hubs can lead to an oversimplification of the true complexity found in neuronal networks. This paper explores the challenges and trade-offs of incorporating both complexity and hubs in brain models. Through a custom-built model featuring five hubs with varying weights and distances, we investigate how these elements interact and influence the emergent network properties such as alpha brain wave patterns. Our findings suggest that a balanced approach that considers both complexity and the presence of hubs yields a more accurate and nuanced understanding of brain network architecture.