Capturing continuous, long timescale behavioral changes in $\textit{Drosophila melanogaster}$ postural data

TL;DR

This study captures and analyzes long-term, continuous postural data of individual Drosophila melanogaster over several days, revealing circadian patterns and behavioral changes associated with aging.

Contribution

The paper introduces a high-resolution, long-duration behavioral dataset for Drosophila, enabling analysis of long-timescale behavioral dynamics previously inaccessible.

Findings

Identified circadian rhythms in stereotyped behaviors.

Observed behavioral changes as flies age and weaken.

Created a comprehensive 2-billion pose dataset.

Abstract

Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual $\textit{Drosophila melanogaster}$ at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.