A deep learning analysis of <i>Drosophila</i> body kinematics during magnetically tethered flight

Flying <i>Drosophila</i> 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 <i>Drosophila</i> 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.飞行的果蝇（Drosophila）依靠视觉感知视觉目标并调整飞行轨迹。尽管这类果蝇能够稳定注视深色垂直条状物，但我们对其背后的视觉运动神经环路的认知仍十分有限，部分原因在于难以在灵敏的行为学实验范式中分析其精细的身体运动学特征。本研究采用磁束缚飞行实验范式，记录飞行果蝇的身体运动学数据：在该范式中，果蝇可围绕其偏航轴自由旋转，从而获得自然的视觉与本体感受反馈。此外，本研究借助基于深度学习的视频分析方法，对飞行果蝇的多部位运动学特征进行定量表征。通过整合这套行为实验与分析流程，本研究对两种视觉条件下果蝇快速飞行转向（或称saccades，飞行扫视）过程中的精细身体运动学特征进行了表征：静态屏幕背景下的自发性飞行扫视，以及追踪旋转垂直条时的条状物注视性扫视。研究发现，两类飞行扫视均涉及多身体部位的运动，且整体动力学特征具有可比性。本研究凸显了灵敏行为学实验范式与分析工具在解析复杂视觉行为中的重要价值。