Simulation of robustness against lesions of cortical networks

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 interareal 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 structural decay of the brain networks is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as a structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of connectivity of the brain networks.

结构决定功能，因此对大脑的结构描述将有助于理解其功能，并可能为大脑系统的许多特性提供深入见解，从其稳健性和受损后的恢复能力，到其动态性，甚至其进化过程。复杂网络分析的进展为理解大脑网络的结构和功能特性提供了有用的新方法。最近所描述的网络结构特性允许将其归类为小世界、随机（指数）和无标度网络。它们补充了在大脑连接性背景下已被探索的其他一系列特性，例如拓扑结构、神经纤维通路、聚类和层级组织。在此，我们将新的网络分析方法应用于猫和猕猴大脑的皮质区域间连接网络。我们使用两种分析策略，将这些皮质 - 皮质纤维网络与重新布线的、小世界、无标度和随机网络进行基准比较，在这些策略中我们测量节点和连接的去除对皮质网络结构特性的影响。大脑网络的结构衰退在大多数方面与无标度网络相似。研究结果表明，高度连接的枢纽节点和瓶颈连接是大脑系统某些条件稳健性的结构基础。这为理解大脑网络连接性的发展提供了信息。