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Stochastic blockmodeling of the modules and core of the caenorhabditis elegans connectome
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Pavlovic, Dragana M., Vértes, Petra E., Bullmore, Edward T., Schafer, William R. and Nichols, Thomas E. (2014) Stochastic blockmodeling of the modules and core of the caenorhabditis elegans connectome. PLoS One, Volume 9 (Number 7). Article number e97584. doi:10.1371/journal.pone.0097584
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Official URL: http://dx.doi.org/10.1371/journal.pone.0097584
Abstract
Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QL Zoology | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Caenorhabditis elegans | ||||||||
| Journal or Publication Title: | PLoS One | ||||||||
| Publisher: | Public Library of Science | ||||||||
| ISSN: | 1932-6203 | ||||||||
| Official Date: | 2 July 2014 | ||||||||
| Dates: |
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| Volume: | Volume 9 | ||||||||
| Number: | Number 7 | ||||||||
| Number of Pages: | 16 | ||||||||
| Article Number: | Article number e97584 | ||||||||
| DOI: | 10.1371/journal.pone.0097584 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Medical Research Council (Great Britain) (MRC), GlaxoSmithKline, Wellcome Trust (London, England), National Institute for Health Research (Great Britain) (NIHR) | ||||||||
| Grant number: | MR/K020706/1 (MRC), U54MH091657-03 (NIHR) |
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