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Sohn, Yunkyu,Choi, Myung-Kyu,Ahn, Yong-Yeol,Lee, Junho,Jeong, Jaeseung Public Library of Science 2011 PLoS computational biology Vol.7 No.5
<▼1><P>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 <I>Caenorhabditis elegans</I> (<I>C. elegans</I>) 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 <I>C. elegans</I> 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.</P></▼1><▼2><P><B>Author Summary</B></P><P><I>Caenorhabditis elegans</I> (<I>C. elegans</I>) is a tiny worm whose neuronal network is fully revealed. Since the modular organization in a network of individual neurons interwoven through synapses is not yet fully explored owing to incredibly complex connectivity architecture, this study is designed to investigate hierarchically organized modules in this complete wiring diagram (connectome) of this worm. We used the modularity-based community detection algorithm and found that <I>C. elegans</I> had 5 anatomical clusters in the <I>C. elegans</I> connectome, which corresponded to experimentally-identified functional circuits. We found that the hierarchical organization of the 5 clusters explains the systemic cooperation including mechanosensation, chemosensation, and navigation that occurs among the structurally-segregated biological circuits to produce higher-order complex behaviors.</P></▼2>