A spectrum of routing strategies for brain networks
A spectrum of routing strategies for brain networks
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DOI：
10.1371/journal.pcbi.1006833
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发表时间：
2019-03-01
影响因子：
4.3
通讯作者：
Sporns, Olaf
中科院分区：
文献类型：
--
作者：
Avena-Koenigsberger, Andrea;Yan, Xiaoran;Sporns, Olaf
复杂网络中节点之间的信号通信带来了效率和成本的基本问题。沿着最短路径路由消息需要有关拓扑的全局信息，而根据局部拓扑特征进行扩散的传播信息廉价但效率低下。我们引入了一种用于网络通信的随机模型，该模型结合了有关网络拓扑的本地和全局信息，以在网络上生成有偏差的随机游走。该模型生成连续的动态谱，在极限条件下收敛到最短路径和随机游走（扩散）通信过程。我们在两组人类连接组网络上实现了该模型，并研究了改变全局信息偏差对网络通信成本的影响。我们确定了接近（高效）最短路径通信过程的路由策略，并且对系统动态的全局信息偏差相对较小。此外，我们表明，从中心节点路由消息到中心节点的路由成本随着驱动系统动态的全局信息偏差的函数而变化。最后，我们实现该模型，从沟通动态的角度识别个体主体差异。本框架背离了经典的最短路径与扩散二分法，将两种模型统一在一个动态过程家族中，这些过程的不同之处在于网络拓扑的全局信息影响穿过网络的神经信号的路由模式的程度。 作者摘要 脑网络通信通常从推断路径的长度以及构建和维护网络连接的成本的角度来进行。然而，这些分析通常忽略网络上发生的动态过程以及这些过程产生的额外成本。在这里，我们引入了一个框架来研究建模为有偏随机游走的广泛通信过程的通信成本权衡。我们通过对网络拓扑的不同程度的了解来偏置节点的路由策略，从而控制系统的动态性，该动态性决定了穿过网络的消息流。在人类连接组上，该框架揭示了一系列动态通信过程，其中一些可以以较低的信息成本实现有效的路由策略。
Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally cheap but inefficient. We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bias on the network's communication cost. We identify routing strategies that approach a (highly efficient) shortest-path communication process with a relatively small global information bias on the system's dynamics. Moreover, we show that the cost of routing messages from and to hub nodes varies as a function of the global information bias driving the system's dynamics. Finally, we implement the model to identify individual subject differences from a communication dynamics point of view. The present framework departs from the classical shortest paths vs. diffusion dichotomy, unifying both models under a single family of dynamical processes that differ by the extent to which global information about the network topology influences the routing patterns of neural signals traversing the network.Author summary Brain network communication is typically approached from the perspective of the length of inferred paths and the cost of building and maintaining network connections. However, these analyses often disregard the dynamical processes taking place on the network and the additional costs that these processes incur. Here, we introduce a framework to study communication-cost trade-offs on a broad range of communication processes modeled as biased random walks. We control the system's dynamics that dictates the flow of messages traversing a network by biasing node's routing strategies with different degrees of knowledge about the topology of the network. On the human connectome, this framework uncovers a spectrum of dynamic communication processes, some of which can achieve efficient routing strategies at low informational cost.