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Lookup NU author(s): Klaas Stephan
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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. © 2003 Elsevier Ltd. All rights reserved.
Author(s): Kotter R, Stephan KE
Publication type: Article
Publication status: Published
Journal: Neural Networks
Year: 2003
Volume: 16
Issue: 9
Pages: 1261-1275
ISSN (print): 0893-6080
ISSN (electronic): 1879-2782
Publisher: Pergamon
URL: http://dx.doi.org/10.1016/j.neunet.2003.06.002
DOI: 10.1016/j.neunet.2003.06.002
PubMed id: 14622883
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