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Brain networks: Graph theoretical analysis and development models
Author(s) -
Cho Myoung Won,
Choi M. Y.
Publication year - 2010
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20229
Subject(s) - computer science , graph theory , connectome , graph , power graph analysis , computation , topology (electrical circuits) , artificial neural network , theoretical computer science , complex network , network science , artificial intelligence , neuroscience , functional connectivity , algorithm , mathematics , psychology , combinatorics , world wide web
A trendy method to understand the brain is to make a map representing the structural network of the brain, also known as the connectome , on the scale of a brain region. Indeed analysis based on graph theory provides quantitative insights into general topological principles of brain network organization. In particular, it is disclosed that typical brain networks share the topological properties, such as small‐world and scale‐free, with many other complex networks encountered in nature. Such topological properties are regarded as characteristics of the optimal neural connectivity to implement efficient computation and communication; brains with disease or abnormality show distinguishable deviations in the graph theoretical analysis. Considering that conventional models in graph theory are, however, not adequate for direct application to the neural system, we also discuss a model for explaining how the neural connectivity is organized. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 108–116, 2010