  Recent developments in network neuroscience have highlighted the importance
of developing techniques for analyzing and modeling brain networks. A
particularly powerful approach for studying complex neural systems is to
formulate generative models that use wiring rules to synthesize networks
closely resembling the topology of a given connectome. Successful models can
highlight the principles by which a network is organized (identify structural
features that arise from wiring rules versus those that emerge) and potentially
uncover the mechanisms by which it grows and develops. Previous research has
shown that such models can validate the effectiveness of spatial embedding and
other (non-spatial) wiring rules in shaping the network topology of the human
connectome. In this research, we propose variants of the action-based model
that combine a variety of generative factors capable of explaining the topology
of the human connectome. We test the descriptive validity of our models by
evaluating their ability to explain between-subject variability. Our analysis
provides evidence that geometric constraints are vital for connectivity between
brain regions, and an action-based model relying on both topological and
geometric properties can account for between-subject variability in structural
network properties. Further, we test correlations between parameters of
subject-optimized models and various measures of cognitive ability and find
that higher cognitive ability is associated with an individual's tendency to
form long-range or non-local connections.
