Coming up short: Generative network models fail to accurately capture long-range connectivity.

ABSTRACT: Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organization of the connectome. These models parameterize the formation of interregional connections using a trade-off between connection cost and topological complexity or biophysical similarity. Despite their simplicity, GNMs can generate synthetic networks that capture many topological properties of empirical brain networks. However, current models often fail to capture the topography (i.e., spatial embedding) of many such properties, such as the anatomical location of network hubs. In this study, we investigate a diverse array of GNM formulations and find that none can accurately capture empirical patterns of long-range connectivity. We demonstrate that the spatial embedding of lon

SUBMITTER: Oldham S 

PROVIDER: S-EPMC12635836 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature