Hierarchical clustering identifies hub nodes in a model of resting-state brain activity

A novel clustering algorithm is presented for analyzing the temporal dynamics of synchronization in networks of coupled oscillators and applied to a model of resting-state brain activity. Connectivity in the model is based on a human-brain structural connectivity matrix derived from diffusion tensor imaging tractography. We find a strong correspondence between areas of high synchronization and highly connected "hub" nodes, anatomical regions forming the structural core of the network linking all areas of the brain. Such models have the potential to increase our understanding of the constraints placed on brain function by underlying anatomical structure.