Understanding the mind of a worm: hierarchical network structure underlying nervous system function in C. elegans

Chatterjee, Nivedita; Sinha, Sitabhra

doi:10.1016/s0079-6123(07)68012-1

Cited by 86 publications

(79 citation statements)

References 27 publications

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“…The k-core of an unweighted graph G is defined as the maximal connected subgraph of nodes in G in which all nodes have at least k connections and in a similar fashion, the s-core of a weighted graph G w is the subgraph of nodes of G w in which all connections show a summed weight of s or higher. s-core decomposition can provide insight into the hierarchical organization of a network (Chatterjee and Sinha, 2008;Hagmann et al, 2008). s-core decomposition proceeds by successively pruning the connections of the network, along the following steps.…”

Section: S-core Decompositionmentioning

confidence: 99%

Rich-Club Organization of the Human Connectome

Heuvel¹,

Sporns²

2011

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The human brain is a complex network of interlinked regions. Recent studies have demonstrated the existence of a number of highly connected and highly central neocortical hub regions, regions that play a key role in global information integration between different parts of the network. The potential functional importance of these "brain hubs" is underscored by recent studies showing that disturbances of their structural and functional connectivity profile are linked to neuropathology. This study aims to map out both the subcortical and neocortical hubs of the brain and examine their mutual relationship, particularly their structural linkages. Here, we demonstrate that brain hubs form a so-called "rich club," characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree, providing important information on the higher-level topology of the brain network. Whole-brain structural networks of 21 subjects were reconstructed using diffusion tensor imaging data. Examining the connectivity profile of these networks revealed a group of 12 strongly interconnected bihemispheric hub regions, comprising the precuneus, superior frontal and superior parietal cortex, as well as the subcortical hippocampus, putamen, and thalamus. Importantly, these hub regions were found to be more densely interconnected than would be expected based solely on their degree, together forming a rich club. We discuss the potential functional implications of the rich-club organization of the human connectome, particularly in light of its role in information integration and in conferring robustness to its structural core.

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Section: S-core Decompositionmentioning

confidence: 99%

Rich-Club Organization of the Human Connectome

Heuvel¹,

Sporns²

2011

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“…epilepsy [179]. Chatterjee and Sinha [180] analyzed the hierarchical structure of the C. elegans nervous system through the k-core decomposition and showed evidence that the assortativity increases as one goes to the innermost core of the network. The authors suggested that such assortative nature of the inner core can help in increasing communication efficiency while turning the network more robust.…”

Section: Da F Costa Et Almentioning

confidence: 99%

Analyzing and modeling real-world phenomena with complex networks: a survey of applications

Costa

Oliveira

Travieso

et al. 2011

Advances in Physics

649

357

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“…Further, there exists some variation in the neural network structure from animal to animal [16]. The neural network of C. elegans has been studied extensively in the literature [17,18]. It was found that perturbed sensory activity and mutations altering the calcium channels or membrane potential affect the axon outgrowth [19] implying that the neural network is not genetically hard wired.…”

Section: Biological Backgroundmentioning

confidence: 99%

Learning and structure of neuronal networks

et al. 2011

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We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of C. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

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Understanding the mind of a worm: hierarchical network structure underlying nervous system function in C. elegans

Cited by 86 publications

References 27 publications

Rich-Club Organization of the Human Connectome

Rich-Club Organization of the Human Connectome

Analyzing and modeling real-world phenomena with complex networks: a survey of applications

Learning and structure of neuronal networks

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