Understanding how structural brain organization shapes the capacity for dynamic transitions in neural activity is a key challenge in network neuroscience. Here, we constructed individualized structural brain networks from diffusion tensor imaging and T1-weighted MRI in 30 healthy adults (with mean age 34 ± 5; 3 females, 27 males; all participants were right-handed). Using network control theory, we quantified two complementary control metrics–average controllability, reflecting the ability of a region to support energetically easy transitions, and modal controllability, reflecting the ability to drive the system toward difficult-to-reach states. We compared these dynamical measures with a comprehensive set of network centrality metrics, including degree, betweenness, closeness, eigenvector, harmonic, Katz, and pagerank centrality. Across subjects, average controllability was strongly and positively correlated with nodal connectivity, whereas modal controllability showed a robust negative relationship with all major centrality measures except harmonic centrality, which exhibited weak and inconsistent associations. These findings reveal a clear trade-off between energetic efficiency and dynamical flexibility across the connectome. Importantly, controllability metrics do not simply replicate topological descriptors but capture distinct dynamical properties not inferable from network structure alone. This systematic multi-metric comparison establishes a baseline for interpreting controllability in structural connectomics and clarifies how topology constrains the brain’s dynamic control capacities.