across lifespan differentiates the precuneus from the default network. Neu- roimage 89, 45–56. Yeh, F.-C., Wedeen, V. J., Tseng, W.-Y. I., 2010. Generalized-sampling imaging. Medical Imaging, IEEE Transactions on 29 (9), 1626–1635. Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Y¨ ucel, M., Pantelis, C., Bullmore, E. T., 2010. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50 (3), 970–983. Figure 1: Summary of the geometric model: (A) observed (black) and synthetic networks generated at different points in parameter space. (B) Energy landscape showing the behavior of KS$_{e}$ , KS$_{c}$ , and energy as a function of η . The dashed vertical lines indicate the param- eter values at which the example synthetic networks were generated. (C) Distribution of η parameter of top 1% lowest-energy synthetic networks aggregated across all participants. (D) Cumulative distributions of degree (orange), clustering coefficient (green), betweenness centrality (yellow), and edge length (purple) for observed connectome (darker line) and best- fitting synthetic networks (lighter lines) for a representative participant. 39