Seoul National University College of Medicine, Seoul, South Korea.
Ewha Brain Institute, Ewha W. University, Seoul, South Korea; Department of Brain and Cognitive Sciences, Ewha W. University, Seoul, South Korea.
Med Hypotheses. 2018 Jan;110:155-160. doi: 10.1016/j.mehy.2017.12.003. Epub 2017 Dec 5.
The human connectome is a complex network that transmits information between interlinked brain regions. Using graph theory, previously well-known network measures of integration between brain regions have been constructed under the key assumption that information flows strictly along the shortest paths possible between two nodes. However, it is now apparent that information does flow through non-shortest paths in many real-world networks such as cellular networks, social networks, and the internet. In the current hypothesis, we present a novel framework using the maximum flow to quantify information flow along all possible paths within the brain, so as to implement an analogy to network traffic. We hypothesize that the connection strengths of brain networks represent a limit on the amount of information that can flow through the connections per unit of time. This allows us to compute the maximum amount of information flow between two brain regions along all possible paths. Using this novel framework of maximum flow, previous network topological measures are expanded to account for information flow through non-shortest paths. The most important advantage of the current approach using maximum flow is that it can integrate the weighted connectivity data in a way that better reflects the real information flow of the brain network. The current framework and its concept regarding maximum flow provides insight on how network structure shapes information flow in contrast to graph theory, and suggests future applications such as investigating structural and functional connectomes at a neuronal level.
人类连接组是一个复杂的网络，它在相互关联的大脑区域之间传递信息。使用图论，在信息严格沿着两个节点之间最短路径流动的关键假设下，先前已经构建了大脑区域之间集成的著名网络度量标准。然而，现在很明显，在许多现实世界的网络中，如细胞网络、社交网络和互联网，信息确实会通过非最短路径流动。在当前的假设中，我们提出了一个使用最大流来量化大脑中所有可能路径上信息流的新框架，以便与网络流量进行类比。我们假设大脑网络的连接强度代表了在单位时间内通过连接可以流动的信息量的限制。这使我们能够计算两个大脑区域之间所有可能路径上的最大信息流。使用这种新的最大流框架，扩展了先前的网络拓扑度量标准，以考虑通过非最短路径的信息流。当前使用最大流的方法的最重要优势在于，它可以以一种更好地反映大脑网络实际信息流的方式整合加权连通性数据。当前的框架及其关于最大流的概念提供了关于网络结构如何影响信息流的见解，与图论形成对比，并提出了未来的应用，如在神经元水平上研究结构和功能连接组。
