Binary mesoscale architecture does not explain controllability of structural brain networks

The human brain displays rich communication dynamics that are thought to be particularly well- reflected in its marked community structure. Yet, the precise relationship between community struc- ture in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to perform targeted manipula- tion of the brain’s large-scale dynamical activity. In this paper, we investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends heavily on edge weight distribution. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we put forth a node-level metric rooted in the spectrum of the network adjacency matrix that is statistically correlated with controllability regardless of the distribution of edge weights. Our study contributes to an understanding of how the brain’s diverse structural architecture supports transient communication dynamics.