Marcolan João Antônio, Marino-Neto José
Laboratory of Computational Neuroscience, Institute of Biomedical Engineering, IEB-UFSC, EEL-CTC, Federal University of Santa Catarina, Florianópolis, SC, 88040-900, Brazil.
Med Biol Eng Comput. 2025 Feb;63(2):511-523. doi: 10.1007/s11517-024-03212-x. Epub 2024 Oct 14.
Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.
由人类观察者（HOs）对视频记录进行注释的行为记录是神经生物学疾病临床前动物行为模型的基本组成部分。这些模型常常因其易受可重复性问题影响而受到批评。在此，我们展示了EthoWatcher-开源版（EW-OS），它具备工具和程序，可用于在跟踪的同时进行条件盲分类转录，用于在训练和数据收集期间评估HOs的观察者内和观察者间可靠性，用于生成对观察者训练有用的行为类别样本的视频片段。使用这些工具可以为观察者的表现提供信息并进行优化，从而有利于所获数据的可重复性。分类输出和机器视觉衍生输出以开放数据格式呈现，以提高与其他应用程序的互操作性，其中行为类别与动物图像的跟踪、形态学和运动学属性逐帧相关联。记录了质心（X和Y像素坐标）、以平方毫米为单位的动物面积、以毫米为单位的长度和宽度以及以度为单位的角度。它还评估每个形态学描述符的变化以生成运动学描述符。虽然初始测量以像素为单位，但随后会使用用户通过图形用户界面校准的比例转换为毫米。这一过程能够创建适用于机器学习处理和行为药理学研究的数据库。EW-OS构建用于持续的协作开发，可通过开源平台获取，以支持在行为分析中采用良好科学实践的倡议，包括用于评估数据质量的工具，这些工具可以缓解行为科学中与低可重复性相关的问题。
