Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0097584
Title: Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome
Authors: Pavlovic D.M. 
Vértes P.E.
Bullmore E.T.
Schafer W.R.
Nichols T.E.
Keywords: article
biological functions
Caenorhabditis elegans
community structure
connectome
Erdos Renyi mixture model
locomotion
nerve cell
nervous system
nonhuman
statistical analysis
stochastic model
synapse
algorithm
animal
biological model
Markov chain
metabolism
nerve cell network
Algorithms
Animals
Caenorhabditis elegans
Connectome
Models, Biological
Nerve Net
Neurons
Stochastic Processes
Issue Date: 2014
Citation: Pavlovic D.M., Vértes P.E., Bullmore E.T., Schafer W.R., Nichols T.E. (2014). Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome. PLoS ONE 9 (7) : e97584. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0097584
Rights: Attribution 4.0 International
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 Erdo{combining double acute accent}s-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 Erdo{combining double acute accent}s-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 Erdo{combining double acute accent}s-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 Erdo{combining double acute accent}s-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems. © 2014 Pavlovic et al.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161402
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0097584
Rights: Attribution 4.0 International
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