A Perimotor Framework Reveals Functional Segmentation in the Motoneuronal Network Controlling Locomotion in Caenorhabditis elegans

Abstract

The neuronal connectivity dataset of the nematode Caenorhabditis elegans attracts wide attention from computational neuroscientists and experimentalists. However, the dataset is incomplete. The ventral and dorsal nerve cords of a single nematode were reconstructed halfway along the body and the posterior data are missing, leaving 21 of 75 motoneurons of the locomotor network with partial or no connectivity data. Using a new framework for network analysis, the perimotor space, we identified rules of connectivity that allowed us to approximate the missing data by extrapolation. Motoneurons were mapped into perimotor space in which each motoneuron is located according to the muscle cells it innervates. In this framework, a pattern of iterative connections emerges which includes most (0.90) of the connections. We identified a repeating unit consisting of 12 motoneurons and 12 muscle cells. The cell bodies of the motoneurons of such a unit are not necessarily anatomical neighbors and there is no obvious anatomical segmentation. A connectivity model, composed of six repeating units, is a description of the network that is both simplified (modular and without noniterative connections) and more complete (includes the posterior part) than the original dataset. The perimotor framework of observed connectivity and the segmented connectivity model give insights and advance the study of the neuronal infrastructure underlying locomotion in C. elegans. Furthermore, we suggest that the tools used herein may be useful to interpret, simplify, and represent connectivity data of other motor systems.