ArticleNetwork neuroscience (Cambridge, Mass.)2019
Topological reinforcement as a principle of modularity emergence in brain networks.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 41 citations in OpenAlex.
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
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- Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP.PLoS computational biology · 2025Article
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- The hidden community architecture of human brain networks.Scientific reports · 2022Article
- Two classes of functional connectivity in dynamical processes in networks.Journal of the Royal Society, Interface · 2021Article
- A generative network model of neurodevelopmental diversity in structural brain organization.Nature communications · 2021Observational
- On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances.Frontiers in systems neuroscience · 2021Review
- Structural determinants of dynamic fluctuations between segregation and integration on the human connectome.Communications biology · 2020Article
- Topological reinforcement as a principle of modularity emergence in brain networks.Network neuroscience (Cambridge, Mass.) · 2019Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
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Abstract
Modularity is a ubiquitous topological feature of structural brain networks at various scales. Although a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, that is, iteratively allowing connections between non-neighbor nodes with high neighborhood similarity. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial "proto-modules," thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity (co-activations) between nodes. Results produced by the activity-based model are consistent with the ones from the purely topological rule in terms of the final network configuration and modules composition. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism contributing to modularity emergence in brain networks.
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