Multilayer network analysis of C. elegans: Looking into the locomotory circuitry

TL;DR

This study models the neuronal and muscular network of C. elegans as a multilayer system, using computational methods to predict and analyze locomotion, revealing synchronization effects crucial for coordinated movement.

Contribution

It introduces a multilayer network model incorporating neurotransmitters and neuropeptides, combined with dynamic simulations, to understand C. elegans locomotion in detail.

Findings

Neuronal network can be predicted with logistic regression.

Synchronization effects are key for coordinated locomotion.

Silencing specific neurons affects movement coordination.

Abstract

We investigate how locomotory behavior is generated in the brain focusing on the paradigmatic connectome of nematode Caenorhabditis elegans (C. elegans) and on neuronal activity patterns that control forward locomotion. We map the neuronal network of the worm as a multilayer network that takes into account various neurotransmitters and neuropeptides. Using logistic regression analysis, we predict the neurons of the locomotory subnetwork. Combining Hindmarsh-Rose equations for neuronal activity with a leaky integrator model for muscular activity, we study the dynamics within this subnetwork and predict the forward locomotion of the worm using a harmonic wave model. The application of time-delayed feedback control reveals synchronization effects that contribute to a coordinated locomotion of C. elegans. Analyzing the synchronicity when the activity of certain neurons is silenced informs us about their significance for a coordinated locomotory behavior. Since the information processing is the same in humans and C. elegans, the study of the locomotory circuitry provides new insights for understanding how the brain generates motion behavior.