A deep learning analysis of Drosophila body kinematics during magnetically tethered flight

Abstract

Flying Drosophila rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our understanding of the underlying visuomotor neural circuits remains limited, in part due to difficulties in analyzing detailed body kinematics in a sensitive behavioral assay. In this study, we observed the body kinematics of flying Drosophila using a magnetically tethered flight assay, in which flies are free to rotate around their yaw axis, enabling naturalistic visual and proprioceptive feedback. Additionally, we used deep learning-based video analyses to characterize the kinematics of multiple body parts in flying animals. By applying this pipeline of behavioral experiments and analyses, we characterized the detailed body kinematics during rapid flight turns (or saccades) in two different visual conditions: spontaneous flight saccades under static screen and bar-fixating saccades while tracking a rotating bar. We found that both types of saccades involved movements of multiple body parts and that the overall dynamics were comparable. Our study highlights the importance of sensitive behavioral assays and analysis tools for characterizing complex visual behaviors.

Keywords:

• Drosophila
• object fixation
• saccade dynamics
• magnetically tethered flight assay
• DeepLabCut

Acknowledgements

The authors thank all the lab members for the discussion and comments on the manuscript, Taeseung Lee, Eunkyong Park and HyungWook Kim for their assistance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Requests of data, analysis code, and hardware design should be directed to and will be fulfilled by the corresponding author, Anmo Kim ([email protected]).

Additional information

Funding

This research was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)), and National Research Foundation of Korea (NRF) grants funded by the Korea Government (MSIT) (NRF2020R1A4A1016840, NRF2021M3E5D2A01023888, NRF2022R1A2C2007599, NRF2022M3E5E8081195).