Network participation indices: characterizing component roles for information processing in neural networks

• Published in: Neural networks Vol. 16; no. 9; pp. 1261 - 1275
• Main Authors: Kötter, Rolf, Stephan, Klaas E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-11-2003
• Subjects: Anatomical connectivity; Cerebral cortex; Cluster analysis; Connectivity matrix; Functional impact; Graph theory; Large-scale connectivity; Mental Processes - physiology; Motor Cortex - physiology; Neural network; Neural Networks (Computer); Prefrontal Cortex - physiology; Visual Cortex - physiology; Cluster analysis; Functional impact; Cerebral cortex; Large-scale connectivity; Anatomical connectivity; Graph theory; Neural network; Connectivity matrix
• ISSN: 0893-6080; 1879-2782
• 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.