Structural Properties of the Caenorhabditis elegans Neuronal Network

By L. Varshney, Beth L. Chen, Eric Paniagua, David H. Hall, Dmitri "Mitya" Chklovskii. 2009

942 citations recorded by the source corpus.

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.

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Citations in the corpus

Weight Agnostic Neural Networks (2019, background): “While it is a challenge to map out the human connectome [102], with our 90 billion neurons and 150 trillion synapses, the connectome of simple organisms such as roundworms [112, 114] has been constructed, and recent works [20, 105] mapped out the entire brain of a small fruit ﬂy.”
Exploring Randomly Wired Neural Networks for Image Recognition (2019, background): “” Studies [51, 49] have observed that the neural network of a nematode (a worm) with about 300 neurons is a graph with small-world properties [19].”
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions (2013, background): “The second, moderately ﬁtting dataset is the number of connections each neuron has in the nematode worm C. elegans [8,9].”
Beyond the connectome: How neuromodulators shape neural circuits (2012, methodology): “The draft connectome was published in 1986 [8], with a more complete version appearing over 20 years later [9].”
Connectomic reconstruction of the inner plexiform layer in the mouse retina (2013, background): “(2) All contacts with bipolar cells were reconstructed on several pieces of ganglion cell de.”
Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “elegans (Varshney et al., 2011; Watts and Strogatz, 1998) and extrapolated for the cortical”
Quantitative neuroanatomy for connectomics in Drosophila (2015, methodology): “From the adjacency matrix describing the connectivity, we measure the signal flow and perform graph partitioning via spectral graph analysis (Varshney et al., 2011).”
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018, background): “…but brieﬂy, C. elegans is a nematode (worm) whose biological neural conﬁguration – the connectome – is stereotypical; i.e. specimens always posses the same number of somatic neurons (282) (Varshney et al., 2011), and the ways those neurons connect and interact changes little from worm to worm.”
Multilayer networks (2013, background): “For example, a multilayer interbank network can include different types of credit relations in different layers [218], a social network can include different types of social relations in different layers [219,220], a Twitter network can include different hashtags in different layers [197,198], a com”
Micro-connectomics: probing the organization of neuronal networks at the cellular scale (2017, background): “excitatory circuit motifs, feedforward excitation (4), feedback excitation (5) and pathway convergence and divergence (6) (see the figure, part d).”
Network control principles predict neuron function in the Caenorhabditis elegans connectome (2017, background): “To date C. elegans is the only organism for which the wiring diagram of its complete nervous system has been mapped with reasonable accuracy at the cellular level4–6.”
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction (2013, background): “To date, only a few neuronal circuits have been analyzed comprehensively—with the connectivity map of the entire nervous system of Caenorhabditis elegans , comprising 302 neurons 8,9 , being the most notable and largest for decades.”
The human connectome: Origins and challenges (2013, background): “(Sohn et al., 2011; Varshney et al., 2011) and in linking network features to physiological processes in specific behavioral domains”
A visual motion detection circuit suggested by Drosophila connectomics (2013, unknown): “Indeed,thedistributionof connectionweightinourconnectomeisheavytailed(Fig.3b,inset, andSupplementaryFig.3),ashasbeenfoundinotherorganisms 21,22 .”
Network hubs in the human brain. (2013, background): “…established in virtually all brain networks across a wide range of species, ranging from the mammalian [32,61,63,67,73,143,161], rodent [162], and avian [163] brain to cellular networks in the zebrafish larva [164], the fruit fly Drosophila [165], and the nematode Caenorhabditis. elegans [166,167].”
Modular Brain Networks (2015, background): “Since its reconstruction in the early 1980s (White et al. 1986), this network has been the subject of many graph-theoretical analyses (Varshney et al. 2011), including several that have attempted to elucidate its division into communities.”