Simulation of Robustness against Lesions of Cortical Networks

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.

arXiv commentsubmitted to European Journal of Neuroscience (under review)

References in corpus (10)

• Emergence of scaling in random networks
• Community structure in social and biological networks
• Error and attack tolerance of complex networks
• Lethality and centrality in protein networks
• Hierarchical organization of modularity in metabolic networks
• Classes of behavior of small-world networks
• Scale-free brain functional networks
• Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
• Edge vulnerability in neural and metabolic networks
• Predicting the connectivity of primate cortical networks from topological and spatial node properties

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