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The organization in brain networks shows highly modular features with weakinter-modular interaction. The topology of the networks involves emergence ofmodules and sub-modules at different levels of constitution governed by fractallaws. The modular organization, in terms of modular mass, inter-modular, andintra-modular interaction, also obeys fractal nature. The parameters whichcharacterize topological properties of brain networks follow one parameterscaling theory in all levels of network structure which reveals theself-similar rules governing the network structure. The calculated fractaldimensions of brain networks of different species are found to decrease whenone goes from lower to higher level species which implicates the more orderedand self-organized topography at higher level species. The sparsely distributedhubs in brain networks may be most influencing nodes but their absence may notcause network breakdown, and centrality parameters characterizing them alsofollow one parameter scaling law indicating self-similar roles of these hubs atdifferent levels of organization in brain networks.

Scaling in topological properties of brain networks