Topological reinforcement as a principle of modularity emergence in brain networks

Author:

Damicelli Fabrizio1ORCID,Hilgetag Claus C.12,Hütt Marc-Thorsten3,Messé Arnaud1

Affiliation:

1. Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany

2. Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America

3. Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany

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.

Funder

Deutscher Akademischer Austauschdienst

Deutsche Forschungsgemeinschaft

Publisher

MIT Press - Journals

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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