Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep27249
Title: The topology of large Open Connectome networks for the human brain
Authors: Gastner, Michael T 
Odor, Geza
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
STATE FUNCTIONAL CONNECTIVITY
SMALL-WORLD
SCALE-FREE
GRIFFITHS PHASES
ORGANIZATION
CRITICALITY
MODEL
INFERENCE
Issue Date: 7-Jun-2016
Publisher: NATURE PUBLISHING GROUP
Citation: Gastner, Michael T, Odor, Geza (2016-06-07). The topology of large Open Connectome networks for the human brain. SCIENTIFIC REPORTS 6 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/srep27249
Abstract: The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to ≃ 106 nodes and ≃108 edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient £m of these networks. While £m suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.
Source Title: SCIENTIFIC REPORTS
URI: https://scholarbank.nus.edu.sg/handle/10635/168554
ISSN: 20452322
DOI: 10.1038/srep27249
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