Global excitability and network structure in the human brain

TL;DR

This study uses Wilson-Cowan models to explore how human brain network structure influences global excitability, revealing a balance between wiring cost and functional capacity, and differences from randomized networks.

Contribution

It demonstrates the relationship between brain network structure and excitability using simulations, highlighting the unique topological features of biological networks.

Findings

Brain networks show a trade-off between wiring cost and functionality.

Biological networks exhibit a sharp transition from inactivity to excitation.

Shuffled networks lack the same transition properties.

Abstract

We utilize a model of Wilson-Cowan oscillators to investigate structure-function relationships in the human brain by means of simulations of the spontaneous dynamics of brain networks generated through human connectome data. This allows us to establish relationships between the global excitability of such networks and global structural network quantities for connectomes of two different sizes for a number of individual subjects. We compare the qualitative behavior of such correlations between biological networks and shuffled networks, the latter generated by shuffling the pairwise connectivities of the former while preserving their distribution. Our results point towards a remarkable propensity of the brain's to achieve a trade-off between low network wiring cost and strong functionality, and highlight the unique capacity of brain network topologies to exhibit a strong transition from an inactive state to a globally excited one.