Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
Department of Orthopedics, Movement System Injury and Repair Research Center, Xiangya Hospital, Central South University, Changsha, Hunan, China.
BMC Neurosci. 2023 Mar 27;24(1):23. doi: 10.1186/s12868-023-00787-3.
Rodent social behavior is a commonly used preclinical model to interrogate the mechanisms underpinning various human neurological conditions. To investigate the interplay between neural systems and social behaviors, neuroscientists need a precise quantitative measure for multi-rodent tracking and behavior assessment in laboratory settings. However, identifying individual differences across multiple rodents due to visual occlusion precludes the generation of stable individual tracks across time.
To overcome the present limitations of multi-rodent tracking, we have developed an Ear Detection and Dual Siamese Network for Multiple Rodent Tracking (EDDSN-MRT). The aim of this study is to validate the EDDSN-MRT system in mice using a publicly available dataset and compare it with several current state-of-the-art methods for behavioral assessment. To demonstrate its application and effectiveness in the assessment of multi-rodent social behavior, we implemented an intermittent fasting intervention experiment on 4 groups of mice (each group is with different ages and fasting status and contains 8 individuals). We used the EDDSN-MRT system to track multiple mice simultaneously and for the identification and analysis of individual differences in rodent social behavior and compared our proposed method with Toxtrac and idtracker.ai.
The locomotion behavior of up to 4 mice can be tracked simultaneously using the EDDSN-MRT system. Unexpectedly, we found intermittent fasting led to a decrease in the spatial distribution of the mice, contrasting with previous findings. Furthermore, we show that the EDDSN-MRT system can be used to analyze the social behavior of multiple mice of different ages and fasting status and provide data on locomotion behavior across multiple mice simultaneously.
Compared with several state-of-the-art methods, the EDDSN-MRT system provided better tracking performance according to Multiple Object Tracking Accuracy (MOTA) and ID Correct Rate (ICR). External experimental validation suggests that the EDDSN-MRT system has sensitivity to distinguish the behaviors of mice on different intermittent fasting regimens. The EDDSN-MRT system code is freely available here: https://github.com/fliessen/EDDSN-MRT .
啮齿动物的社会行为是一种常用的临床前模型，用于探究各种人类神经状况的潜在机制。为了研究神经系统和社会行为之间的相互作用，神经科学家需要一种精确的定量方法来在实验室环境中对多只啮齿动物进行跟踪和行为评估。然而，由于视觉遮挡，很难在多个啮齿动物之间识别个体差异，因此无法在整个时间范围内生成稳定的个体轨迹。
为了克服多只啮齿动物跟踪的现有局限性，我们开发了一种用于多只啮齿动物跟踪的耳朵检测和双孪生网络（EDDSN-MRT）。本研究的目的是使用公开的数据集验证 EDDSN-MRT 系统在小鼠中的有效性，并将其与几种当前最先进的行为评估方法进行比较。为了展示其在多只啮齿动物社会行为评估中的应用和有效性，我们在 4 组小鼠（每组包含不同年龄和禁食状态的 8 只个体）上实施了间歇性禁食干预实验。我们使用 EDDSN-MRT 系统同时跟踪多只老鼠，并对啮齿动物社会行为的个体差异进行识别和分析，将我们提出的方法与 Toxtrac 和 idtracker.ai 进行了比较。
EDDSN-MRT 系统可同时跟踪多达 4 只老鼠的运动行为。出乎意料的是，我们发现间歇性禁食导致老鼠的空间分布减少，与之前的发现相反。此外，我们表明 EDDSN-MRT 系统可用于分析不同年龄和禁食状态的多只老鼠的社会行为，并提供多只老鼠的运动行为数据。
与几种最先进的方法相比，EDDSN-MRT 系统根据多目标跟踪精度（MOTA）和身份识别准确率（ICR）提供了更好的跟踪性能。外部实验验证表明，EDDSN-MRT 系统对区分不同间歇性禁食方案下的小鼠行为具有敏感性。EDDSN-MRT 系统的代码可在此处免费获取：https://github.com/fliessen/EDDSN-MRT。
