Center for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
PLoS One. 2022 Dec 27;17(12):e0279550. doi: 10.1371/journal.pone.0279550. eCollection 2022.
The zebrafish (Danio rerio) is widely used as a promising high-throughput model organism in neurobehavioral research. The mobility of zebrafish can be dissected into multiple behavior endpoints to assess its neurobehavioral performance. However, such facilities on the market are expensive and clumsy to be used in laboratories. Here, we designed a low-cost, automatic zebrafish behavior assay apparatus, barely without unintentional human operational errors. The data acquisition part, composed of Raspberry Pi and HQ Camera, automatically performs video recording and data storage. Then, the data processing process is also on the Raspberry Pi. Water droplets and inner wall reflection of multi-well cell culture plates (used for placing zebrafish) will affect the accuracy of object recognition. And during the rapid movement of zebrafish, the probability of zebrafish tracking loss increased significantly. Thus, ROI region and related thresholds were set, and the Kalman filter algorithm was performed to estimate the best position of zebrafish in each frame. In addition, all functions of this device are realized by the custom-written behavior analysis algorithm, which makes the optimization of the setup more efficient. Furthermore, this setup was also used to analyze the behavioral changes of zebrafish under different concentrations of alcohol exposure to verify the reliability and accuracy. The alcohol exposure induced an inverted U-shape dose-dependent behavior change in zebrafish, which was consistent with previous studies, showcasing that the data obtained from the setup proposed in this study are accurate and reliable. Finally, the setup was comprehensively assessed by evaluating the accuracy of zebrafish detection (precision, recall, F-score), and predicting alcohol concentration by XGBoost. In conclusion, this study provides a simple, and low-cost package for the determination of multiple behavioral parameters of zebrafish with high accuracy, which could be easily adapted for various other fields.
斑马鱼（Danio rerio）广泛用作神经行为研究中的一种有前途的高通量模式生物。斑马鱼的运动能力可以分解为多个行为终点，以评估其神经行为表现。然而，市场上的此类设备昂贵且在实验室中使用不便。在这里，我们设计了一种低成本、自动的斑马鱼行为分析装置，几乎没有人为操作失误的可能。数据采集部分由 Raspberry Pi 和 HQ 相机组成，可自动执行视频录制和数据存储。然后，数据处理过程也在 Raspberry Pi 上进行。多井细胞培养板（用于放置斑马鱼）中的水滴和内壁反射会影响物体识别的准确性。而且，在斑马鱼快速运动时，斑马鱼跟踪丢失的概率显著增加。因此，我们设置了 ROI 区域和相关阈值，并执行了卡尔曼滤波算法，以估计每个帧中斑马鱼的最佳位置。此外，该设备的所有功能都是通过自定义编写的行为分析算法实现的，这使得设置的优化更加高效。此外，该装置还用于分析不同浓度酒精暴露下斑马鱼的行为变化，以验证其可靠性和准确性。酒精暴露导致斑马鱼的行为呈倒 U 形剂量依赖性变化，与之前的研究一致，表明从本研究提出的装置中获得的数据是准确可靠的。最后，我们通过评估斑马鱼检测的准确性（精确性、召回率、F 分数）和通过 XGBoost 预测酒精浓度，对该装置进行了全面评估。总之，本研究提供了一种简单、低成本的方案，可高精度地确定斑马鱼的多个行为参数，并且易于适应各种其他领域。
