Edge-centric network control on the human brain structural network.

Network control theory models how gray matter regions transition between cognitive statesthrough associated white matter connections, where controllability quantifies the contributionof each region to driving these state transitions. Current applications predominantly adoptnode-centric views and overlook the potential contribution of brain network connections. Tobridge this gap, we use edge-centric network control theory (E-NCT) to assess the role of brainconnectivity (i.e., edges) in governing brain dynamic processes. We applied this framework todiffusion MRI data from individuals in the Human Connectome Project. We first validate edgecontrollability through comparisons against null models, node controllability, and structuraland functional connectomes. Notably, edge controllability predicted individual differences inphenotypic information. Using E-NCT, we estimate the brain's energy consumption foractivating specific networks. Our results reveal that the activation of a complex, whole-brainnetwork predicting executive function (EF) is more energy efficient than the correspondingcanonical network pairs. Overall, E-NCT provides an edge-centric perspective on thebrain's network control mechanism. It captures control energy patterns andbrain-behavior phenotypes with a more comprehensive understanding of brain dynamics.