Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

TL;DR

This paper investigates how brain network features like modules and hubs contribute to complex neural dynamics, showing that preserving these features maximizes functional complexity and proposing a new hierarchical network model.

Contribution

It demonstrates the importance of modules and hubs in brain dynamics, compares empirical and surrogate connectomes, and introduces a novel hierarchical network model combining modularity and rich-clubs.

Findings

Functional complexity decreases when modules or hubs are destroyed.

The human brain's resting state reflects maximal anatomical complexity.

The proposed hierarchical model exhibits more complex dynamics than existing benchmarks.

Abstract

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.