生物医学研究中的再现性危机(或复制危机)是行为神经科学领域中一个特别存在且未得到解决的问题,where,尽管努力标准化测试和分析方案,几个已知和未知的混杂环境因素来源增加了差异。人为干扰是实验室内部和跨实验室变化的主要原因,以及新奇引起的焦虑。减少人为干扰和测量受试者中更多“自然”行为的尝试导致了自动化家庭笼监控系统的发展。这些系统可以实现长时间和纵向记录,并提供可以跨多个时间尺度分析的自发行为的大型连续测量。在这次审查中,一个不同的神经科学家和产品开发人员团队分享他们使用这种自动监控系统的经验,该系统结合了NoldusPhenoTyper®家用笼子和基于视频的跟踪软件,EthoVision®XT,提取运动的数字生物标志物,情感,社会和认知行为。在介绍了我们对“家笼”的工作定义之后,我们将家庭笼子测试与更传统的笼子外测试进行比较(例如,开放领域),并概述前者的各种优势,包括受试者内分析和评估昼夜节律和超昼夜节律活动的机会。接下来,我们解决与获取行为数据有关的技术问题,例如跟踪软件的微调以及与生物遥测和光遗传学整合的潜力。最后,我们提供指导,强调哪些行为措施,如何过滤,段,并分析行为,以及如何使用分析脚本。我们总结了PhenoTyper如何应用于研究神经药理学以及神经退行性和神经精神疾病的动物模型。展望未来,我们研究当前的挑战和新发展的影响。例子包括特定行为的自动识别,在社会背景下明确跟踪个人,开发更多以动物为中心的行为衡量标准和处理大型数据集的方法。一起,我们主张通过采用像PhenoTyper这样的标准化家庭笼监控平台,我们准备直接评估与再现性有关的问题,更重要的是,在更加道德相关的场景下测量啮齿动物行为的特征。
The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more \"natural\" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus
PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a \"home-cage\", we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the
PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the
PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.