Kötter Rolf, Stephan Klaas E
Institute of Anatomy II and C & O Vogt Brain Research Institute, Heinrich Heine University, Moorenstrasse 5, D-40225 Düsseldorf, Germany.
Neural Netw. 2003 Nov;16(9):1261-75. doi: 10.1016/j.neunet.2003.06.002.
We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the interesting property of linking local features of individual network components to distributed properties that arise within the network as a whole. We use connectivity data on large-scale cortical networks to demonstrate the virtues of this approach and highlight some interesting features that had not been brought up in previously published material. Some implications of our approach for defining network characteristics relevant to functional segregation and functional integration, for example, from functional imaging studies are discussed.
我们提出了一组指标，这些指标基于连通性数据来刻画网络节点如何参与更大的网络，以及在给定感兴趣的特定子网的情况下它可能扮演的角色。这些网络参与指标源自简单的图论度量，具有将单个网络组件的局部特征与整个网络中出现的分布式属性相联系的有趣特性。我们使用大规模皮质网络的连通性数据来证明这种方法的优点，并突出一些在先前发表的材料中未提及的有趣特征。例如，我们讨论了我们的方法对于定义与功能分离和功能整合相关的网络特征（如来自功能成像研究的特征）的一些含义。
