Scaling in topological properties of brain networks

TL;DR

This paper investigates the fractal and scaling properties of brain network topology, revealing self-similar hierarchical organization and differences across species, with implications for understanding brain complexity.

Contribution

It introduces a one-parameter scaling theory for topological features of brain networks, highlighting fractal laws and self-similarity across different species and organizational levels.

Findings

Brain networks exhibit fractal modular organization.

Fractal dimensions decrease from lower to higher species.

Hubs follow scaling laws and influence network robustness.

Abstract

The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure which reveals the self-similar rules governing the network structure. The calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks.