Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China; University of the Chinese Academy of Sciences, Beijing, 100049, China.
Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
Biochem Biophys Res Commun. 2021 Jun 25;559:1-7. doi: 10.1016/j.bbrc.2021.03.125. Epub 2021 Apr 28.
Various animal models of anxiety have been developed to evaluate anxiety and anxiolytic drugs. However, non-uniform measuring paradigms, variability in apparatus use and individual differences in animals confound study results. In this study, when all animals were included in the data analysis, we found no significant differences between control and stressed mice using standard behavioral paradigms for assessing anxiety (elevated plus maze and open field test). To provide a better assessment of anxiety, we therefore used a machine learning approach to analyze the behavioral patterns of each animal, and selected typical subjects in each group for use as a training set according to classical anxiety parameters. Spontaneous behaviors in these animals were captured by multi-view cameras and decomposed into sub-second modules using Behavior Atlas, and six behavioral features providing statistically significant difference between stressed and control mice were identified. Combined with low-dimensional embedding and clustering, new features were used to discriminate stressed mice from controls, in both the training set and all objects. Our results show Behavior Atlas is a powerful approach for identifying new potential biomarkers in an unbiased fashion. Our approach can complement classical measuring paradigms to objectively and comprehensively evaluate anxiety-like behaviors.
已经开发了各种焦虑动物模型来评估焦虑和抗焦虑药物。然而，非统一的测量范式、仪器使用的可变性以及动物的个体差异使研究结果复杂化。在这项研究中，当所有动物都包括在数据分析中时，我们使用评估焦虑的标准行为范式（高架十字迷宫和旷场试验）发现对照组和应激组小鼠之间没有显著差异。为了更好地评估焦虑，我们因此使用机器学习方法来分析每个动物的行为模式，并根据经典的焦虑参数选择每组中的典型动物作为训练集。使用多视图摄像机捕获这些动物的自发行为，并使用 Behavior Atlas 将其分解为亚秒级模块，并确定了六个在应激和对照组小鼠之间具有统计学差异的行为特征。结合低维嵌入和聚类，使用新特征来区分训练集中和所有对象中的应激组和对照组。我们的结果表明，Behavior Atlas 是一种以无偏方式识别新的潜在生物标志物的强大方法。我们的方法可以补充经典的测量范式，客观全面地评估类似焦虑的行为。
