Small-World Propensity and Weighted Brain Networks.

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TitleSmall-World Propensity and Weighted Brain Networks.
Publication TypeJournal Article
Year of Publication2016
AuthorsMuldoon, SFeldt, Bridgeford, EW, Bassett, DS
JournalSci Rep
Volume6
Pagination22057
Date Published2016 Feb 25
ISSN2045-2322
KeywordsAlgorithms, Animals, Brain, Cluster Analysis, Humans, Models, Biological, Neural Networks (Computer)
Abstract

Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.

DOI10.1038/srep22057
Alternate JournalSci Rep
PubMed ID26912196
PubMed Central IDPMC4766852
Grant ListR01 DC009209 / DC / NIDCD NIH HHS / United States
R01 HD086888 / HD / NICHD NIH HHS / United States
1R01HD086888-01 / HD / NICHD NIH HHS / United States
2-R01-DC-009209-11 / DC / NIDCD NIH HHS / United States