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From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency

Lookup NU author(s): Professor Marcus Kaiser



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


The connectome, or the entire connectivity of a neural system represented by a network, ranges across various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether the connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of Caenorhabditis elegans and the fibre tract network of human brains obtained through diffusion spectrum imaging. We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of both C. elegans and human connectomes are higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution, and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in the C. elegans connectome or each region of interest in the human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared with the alternative arrangements. This implies that fewer genes are needed to encode for the organization of neural systems. While the first two findings show that the neural topologies are efficient in information processing, this suggests that they are also efficient from a developmental point of view. Together, these results show that neural systems are organized in such a way as to yield efficient features beyond those given by their modularity alone.

Publication metadata

Author(s): Kim JS, Kaiser M

Publication type: Article

Publication status: Published

Journal: Philosophical Transactions of the Royal Society B

Year: 2014

Volume: 369

Issue: 1653

Print publication date: 01/10/2014

Online publication date: 01/09/2014

Date deposited: 30/07/2015

ISSN (print): 0962-8436

ISSN (electronic): 1471-2970

Publisher: Royal Society Publishing


DOI: 10.1098/rstb.2013.0529


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Funder referenceFunder name
EP/K026992/1Human Green Brain Project by EPSRC
R31-10089WCU program through the KOSEF - MEST