EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals.

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Medical & Biological Engineering & Computing Pub Date : 2024-10-14 DOI:10.1007/s11517-024-03212-x
João Antônio Marcolan, José Marino-Neto
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Abstract

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.

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EthoWatcher OS:提高实验室动物行为记录的分类和形态/运动学数据的可重复性和质量。
由人类观察者(HOs)通过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。这些模型经常因其易受可重复性问题的影响而受到批评。在此,我们介绍 EthoWatcher-Open Source (EW-OS),其工具和程序包括:使用盲条件分类转录(与跟踪同步进行);在训练和数据收集过程中评估观察者内部和观察者之间的可靠性;制作用于观察者训练的行为类别样本视频剪辑。使用这些工具可以为观察者提供信息并优化其表现,从而提高所获数据的可重复性。分类输出和机器视觉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、形态和运动属性逐帧关联。质量中心(X 和 Y 像素坐标)、动物的面积(平方毫米)、长度和宽度(毫米)以及角度(度)都被记录下来。它还会评估每个形态描述符的变化,以生成运动描述符。虽然最初的测量值是以像素为单位的,但随后会通过图形用户界面,使用用户校准的刻度将其转换为毫米。通过这一过程,可以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 可通过开源平台进行持续合作开发,以支持在行为分析中采用良好的科学实践,包括用于评估数据质量的工具,从而缓解与行为科学中可重复性低有关的问题。
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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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