Hayakawa Shota, Kataoka Kosuke, Yamamoto Masanobu, Asahi Toru, Suzuki Takeshi
Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 162-8480, Japan.
Comprehensive Research Organization, Waseda University, Tokyo 162-8480, Japan.
Biol Open. 2024 Apr 15;13(4). doi: 10.1242/bio.060237. Epub 2024 Apr 23.
Circadian rhythms are indispensable intrinsic programs that regulate the daily rhythmicity of physiological processes, such as feeding and sleep. The cricket has been employed as a model organism for understanding the neural mechanisms underlying circadian rhythms in insects. However, previous studies measuring rhythm-controlled behaviours only analysed locomotive activity using seesaw-type and infrared sensor-based actometers. Meanwhile, advances in deep learning techniques have made it possible to analyse animal behaviour and posture using software that is devoid of human bias and does not require physical tagging of individual animals. Here, we present a system that can simultaneously quantify multiple behaviours in individual crickets - such as locomotor activity, feeding, and sleep-like states - in the long-term, using DeepLabCut, a supervised machine learning-based software for body keypoints labelling. Our system successfully labelled the six body parts of a single cricket with a high level of confidence and produced reliable data showing the diurnal rhythms of multiple behaviours. Our system also enabled the estimation of sleep-like states by focusing on posture, instead of immobility time, which is a conventional parameter. We anticipate that this system will provide an opportunity for simultaneous and automatic prediction of cricket behaviour and posture, facilitating the study of circadian rhythms.
昼夜节律是调节诸如进食和睡眠等生理过程每日节律性的不可或缺的内在程序。蟋蟀已被用作理解昆虫昼夜节律背后神经机制的模式生物。然而，以往测量节律控制行为的研究仅使用跷跷板型和基于红外传感器的活动计分析运动活动。与此同时，深度学习技术的进步使得使用无人类偏差且无需对单个动物进行物理标记的软件来分析动物行为和姿势成为可能。在此，我们展示了一种系统，该系统可以使用DeepLabCut（一种基于监督机器学习的身体关键点标记软件）长期同时量化单个蟋蟀的多种行为，如运动活动、进食和类似睡眠的状态。我们的系统成功地以高置信度标记了单个蟋蟀的六个身体部位，并生成了显示多种行为昼夜节律的可靠数据。我们的系统还通过关注姿势而非传统参数静止时间来估计类似睡眠的状态。我们预计该系统将为同时自动预测蟋蟀行为和姿势提供机会，促进昼夜节律的研究。
