期刊
NEUROSCIENTIST
卷 23, 期 5, 页码 499-516出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1073858416667720
关键词
graph theory; small-world network; network neuroscience; connectomics; small-world propensity
资金
- 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 Health [2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, R21-M MH-106799]
- Office of Naval Research
- National Science Foundation [BCS-1441502, CAREER PHY-1554488, BCS-1631550]
- National Institute for Health Research [NF-SI-0513-10051] Funding Source: researchfish
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [1430087] Funding Source: National Science Foundation
It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.
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