Journal
SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep22057
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Funding
- John D. and Catherine T. MacArthur Foundation
- Alfred P. Sloan Foundation
- Army Research Laboratory
- Army Research Office [W911NF-10-2-0022, W911NF-14-1-0679]
- National Institute of Mental Health [2-R01-DC-009209-11]
- National Institute of Child Health and Human Development [1R01HD086888-01]
- Office of Naval Research
- National Science Foundation [BCS-1441502, BCS-1430087]
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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 realworld 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.
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