Simulation of Robustness against Lesions of Cortical Networks

TL;DR

This study uses network analysis to simulate and compare the robustness of cortical brain networks against lesions, revealing that brain networks share properties with scale-free networks and highlighting the importance of hub nodes for robustness.

Contribution

It introduces new methods to analyze cortical connectivity networks and compares their robustness to various benchmark network models, providing insights into brain resilience.

Findings

Brain networks exhibit robustness similar to scale-free networks.

Hub nodes and bottleneck connections are critical for network resilience.

Structural decay patterns suggest scale-free properties in brain connectivity.

Abstract

Structure entails function and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage, to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering, and hierarchical organization. Here we apply new network analysis methods to cortical inter-areal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks, using two analysis strategies, in which we measure the effects of the removal of nodes and connections on the structural properties of the cortical networks. The brain networks' structural decay is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of brain networks' connectivity.