# Characterizing stage-dependent neuromotor patterns in Drosophila melanogaster larvae through a graph construction approach

## TL;DR

The study uses a mathematical model to analyze how neuromotor activity in fruit fly larvae changes during development, revealing differences in neural activity patterns between early and later larval stages.

## Contribution

A novel graph-based framework is introduced to quantitatively compare neuromotor patterns across developmental stages in Drosophila larvae.

## Key findings

- L1 larvae show higher frequencies of non-propagating neural activity compared to L3 larvae.
- L3 larvae exhibit efficient neural activity propagation along the ventral nerve cord, leading to longer activity chains.
- Asymmetrical neural activity in L3 larvae correlates with turning behaviors and improved navigation.

## Abstract

We investigated developmental changes in neuromotor activity patterns in Drosophila melanogaster larvae by combining calcium imaging with a novel graph-based mathematical framework. This allows to perform relevant quantitative comparisons between first (L1) and early third (L3) instar larvae. We found that L1 larvae exhibit higher frequencies of spontaneous neural activity that fail to propagate, indicating a less mature neuromotor system. In contrast, L3 larvae show efficient initiation and propagation of neural activity along the entire ventral nerve cord (VNC), resulting in longer activity chains. The time of chain propagation along the entire VNC is shorter in L1 than in L3, probably reflecting the increased length of the VNC. On the other hand, the time of peristaltic waves through the whole body during locomotion is much faster in L3 than in L1, so correlating with higher velocities and greater dispersal rates. Hence, the VNC-body interaction determines the characteristics of peristaltic waves propagation in crawling larvae. Further, asymmetrical neuronal activity, predominantly in anterior segments of L3 larvae, was associated with turning behaviors and enhanced navigation. These findings illustrate that the proposed quantitative model provides a systematic method to analyze neuromotor patterns across developmental stages, for instance, helping to uncover the maturation stages of neural circuits and their role in locomotion.