Anshul Choudhary, Brian Q Geuther, Thomas J Sproule, Glen Beane, Vivek Kohar, Jarek Trapszo, Vivek Kumar
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Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (<i>ethograph</i>) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. 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引用次数: 0
摘要
复杂动物行为的自动检测仍然是神经科学领域的一个挑战。计算机视觉的发展极大地促进了自动行为检测,并允许高通量的临床前和机制研究。一个集成的硬件和软件解决方案是必要的,以促进在行为神经遗传学领域的这些进展的采用,特别是对于非计算实验室。我们已经发表了一系列论文,使用开放的领域来注释复杂的行为,如梳理,姿势和步态,以及更高层次的结构,如生物年龄和疼痛。在这里,我们向社区展示了我们的综合啮齿动物表型平台,JAX动物行为系统(JABS),用于数据采集,基于机器学习的行为注释和分类,分类器共享和遗传分析。JABS数据采集模块(JABS- da)通过其3D硬件设计和实时监控和视频数据采集软件的结合,实现了统一的数据采集。主动学习模块(JABS-AL)允许行为注释、分类器训练和验证。我们引入了一种新的基于图的框架(ethograph),使JABS-AL分类器能够进行有效的双向比较。JABS- analysis and Integration Module (JABS- ai)是一个web应用程序,方便用户部署和共享任何在JABS上训练过的分类器,从而减少了行为注释所需的工作量。它支持推断和共享经过训练的JABS分类器和下游遗传分析(遗传性和遗传相关性),这些数据集跨越168个小鼠品系,我们将与本研究一起公开发布。这使得使用遗传学作为正确的行为分类器选择的指导。这个开源工具是一个生态系统,它允许神经科学和遗传学社区共享高级行为分析,并减少进入这个新领域的障碍。
JAX Animal Behavior System (JABS): A genetics informed, end-to-end advanced behavioral phenotyping platform for the laboratory mouse.
Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community for shared advanced behavior analysis and reduces the barrier to entry into this new field.