Topological reinforcement as a principle of modularity emergence in brain networks

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.

On the importance of being flexible: dynamic brain networks and their potential functional significances

“…One possible way of understanding and modelling neuronal activity is in terms of its hypothesized computational bases. This means that neuronal activity, seemingly, unfolds in highly fixed structures such as domain-specific modules highly connected by the transference of information (Arbib et al, 2003;Damicelli et al, 2019;Mattar & Bassett, 2019;Yan & Hricko, 2017). Even single neurons may be understood as modules, supposedly representing their respective parameters, e.g., state variables.…”

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

“…Notable network features of the connectome are its community structure and its interconnected hubs. Brain regions within communities are densely connected to each other [3][4][5] , supporting local communication within functionally segregated systems in the brain. In parallel, some brain regions connect with many others across diverse communities.…”

Graph analysis of looming-selective networks in the tectum, and its replication in a simple computational model

“…This match between the model and the experiment suggests that at the very least, our model captured the nature of network development under the influence of synaptic competition and spike-time-dependent plasticity (STDP). Synaptic competition promoted connectivity in weakly connected neurons, while "punishing" overconnected cells, which created light-frame, openwork graph structures (Fiete et al, 2010), and STDP coordinated activity within sub-networks, increasing modularity (Stam et al, 2010;Litwin-Kumar and Doiron, 2014), similar to how it was previously described for other types of plasticity (Damicelli et al, 2018;Triplett et al, 2018). We did not observe changes in the number of neuronal ensembles (Avitan et al, 2017;Pietri et al, 2017), but we believe this is because our experiments were not suited for ensemble detection, as we worked with strong shared inputs that reliably activated almost every neuron in the network.…”