Please use this identifier to cite or link to this item:
https://doi.org/10.1038/srep27249
DC Field | Value | |
---|---|---|
dc.title | The topology of large Open Connectome networks for the human brain | |
dc.contributor.author | Gastner, Michael T | |
dc.contributor.author | Odor, Geza | |
dc.date.accessioned | 2020-05-28T00:53:07Z | |
dc.date.available | 2020-05-28T00:53:07Z | |
dc.date.issued | 2016-06-07 | |
dc.identifier.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 | |
dc.identifier.issn | 20452322 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/168554 | |
dc.description.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. | |
dc.language.iso | en | |
dc.publisher | NATURE PUBLISHING GROUP | |
dc.source | Elements | |
dc.subject | Science & Technology | |
dc.subject | Multidisciplinary Sciences | |
dc.subject | Science & Technology - Other Topics | |
dc.subject | STATE FUNCTIONAL CONNECTIVITY | |
dc.subject | SMALL-WORLD | |
dc.subject | SCALE-FREE | |
dc.subject | GRIFFITHS PHASES | |
dc.subject | ORGANIZATION | |
dc.subject | CRITICALITY | |
dc.subject | MODEL | |
dc.subject | INFERENCE | |
dc.type | Article | |
dc.date.updated | 2020-05-27T08:12:09Z | |
dc.contributor.department | YALE-NUS COLLEGE | |
dc.description.doi | 10.1038/srep27249 | |
dc.description.sourcetitle | SCIENTIFIC REPORTS | |
dc.description.volume | 6 | |
dc.description.issue | 1 | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
The topology of large Open Connectome networks for the human brain.pdf | Published version | 684.6 kB | Adobe PDF | OPEN | Published | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.