Topological Reinforcement as a Principle of Modularity Emergence in Brain Networks

Abstract

1Modularity is a ubiquitous topological feature of structural brain networks at various scales. While a variety of 2 potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural 3 networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a 4 purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap 5 between nodes, iteratively connecting a randomly selected node to a non-neighbor with the highest topological 6 overlap, while pruning another network link at random. This rule reliably evolves synthetic random networks 7 toward a modular architecture. Such final modular structure reflects initial 'proto-modules', thus allowing 8 to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle 9 might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable 10 dynamics, where the plasticity rule acts based on the functional connectivity between nodes represented by 11 co-activations. Results produced by the activity-based model are consistent with the ones from the purely 12 topological rule, showing a consistent final network configuration. Our findings suggest that the selective 13 reinforcement of topological overlap may be a fundamental mechanism by which brain networks evolve toward 14 modular structure. 15