Dominant Control Set Selection in Clustered Complex Brain Network

The human brain is a complex network divided into distinct cognitive networks. Although much research has been done in the area of brain controllability, a comprehensive method to select a dominant control set while guaranteeing the distribution of control inputs in local areas is lacking. This study evaluates the relative effect of clustering on structural brain network controllability by considering inter-cluster and intra- cluster connections. Our framework employs average controllability criteria to assess the ability of brain regions to spread control inputs. Our experiment demonstrates efficient dominant control set selection by choosing some hub regions that are within hub clusters. We conclude that clustering the brain complex network is a useful method for identifying potential dominant control regions that are overlooked in whole-brain controllability measurements.