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Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome

Overview of attention for article published in PLoS Computational Biology, May 2011
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Title
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
Published in
PLoS Computational Biology, May 2011
DOI 10.1371/journal.pcbi.1001139
Pubmed ID
Authors

Yunkyu Sohn, Myung-Kyu Choi, Yong-Yeol Ahn, Junho Lee, Jaeseung Jeong

Abstract

The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 5%
Germany 2 2%
Netherlands 1 <1%
India 1 <1%
United Kingdom 1 <1%
Hungary 1 <1%
Korea, Republic of 1 <1%
Singapore 1 <1%
Greece 1 <1%
Other 1 <1%
Unknown 113 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 32%
Researcher 24 18%
Student > Master 15 12%
Professor > Associate Professor 10 8%
Professor 9 7%
Other 23 18%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 28%
Engineering 13 10%
Computer Science 13 10%
Neuroscience 12 9%
Physics and Astronomy 10 8%
Other 31 24%
Unknown 14 11%