Brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation and maintenance, we use the percolation methods of network reliability into brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown related to brain disorders, as well as the reliability design of other technological networks.

中文翻译：

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脑结构网络的渗透分析

脑网络是一种特殊类型的关键基础设施网络，支持生物系统的认知功能。考虑到网络可靠性在系统设计、评估、运行和维护中的重要性，我们将网络可靠性的渗透方法运用到大脑网络中，研究网络抗扰动能力和相关故障模式。在本文中，我们比较了不同物种的大脑网络，包括猫、苍蝇、人类、小鼠和猕猴。结构特征的差异反映了对不同程度的功能专业化和整合的要求，这决定了大脑网络的可靠性。在渗透过程中，我们根据表征网络结构的指标对大脑网络应用不同形式的干扰。我们的研究结果表明，大脑网络的结构设计对于基于随机或基于 k 核的渗透大多是可靠的，但在中介或基于程度的渗透下变得脆弱。我们的结果可能有助于识别和区分与大脑疾病相关的大脑连接故障，以及其他技术网络的可靠性设计。