A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome

PLoS One. 2021 Oct 27;16(10):e0249846. doi: 10.1371/journal.pone.0249846. eCollection 2021.

Abstract

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Caenorhabditis elegans / physiology*
  • Connectome / methods
  • Male
  • Models, Neurological
  • Nerve Net / physiology*
  • Neurons / physiology
  • Sensorimotor Cortex / physiology
  • Synapses / physiology

Grants and funding

Research reported in this publication was supported by DARPA Grant: Lifelong Learning Machines (L2M) program of DARPA/MTO: Cooperative Agreement HR0011-18-2-0019 (https://www.darpa.mil/program/lifelong-learning-machines). The funders did not play any role in designing the study, collecting and analyzing the data, decision to publish, or preparing the manuscript.