Action detection using a neural network elucidates the genetics of mouse grooming behavior.
Action detection using a neural network elucidates the genetics of mouse grooming behavior.
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DOI：
10.7554/elife.63207
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发表时间：
2021-03-17
期刊：
影响因子：
7.7
通讯作者：
Kumar V
中科院分区：
文献类型：
--
作者：
Geuther BQ;Peer A;He H;Sabnis G;Philip VM;Kumar V
复杂动物行为的自动检测在神经科学中仍然是一个具有挑战性的问题，特别是对于由不同顺序运动组成的行为。理毛是一种典型的刻板行为，经常被用作精神遗传学中的内表型。在这里，我们以小鼠梳理行为为例，开发了一种通用神经网络架构，能够以人类行为水平进行动态动作检测，并在数十种具有高度视觉多样性的小鼠品系中运行。我们提供了实现这种性能所需的人类注释训练数据量的见解。我们调查了62个品系的2457只小鼠在开阔场地的梳理行为，确定了其遗传成分，进行了GWAS以概述其遗传结构，并进行了PheWAS以通过共享的潜在遗传学将人类精神病学特征联系起来。我们的通用机器学习解决方案可以自动对大型数据集中的复杂行为进行分类，这将有助于对行为机制进行系统研究。行为是人体中枢神经系统的最终和最复杂的输出之一，它控制着运动，情感和情绪。它也受到一个人的基因的影响。研究行为和遗传学之间联系的科学家经常使用动物进行实验，动物的行为比人类更容易描述。然而，这涉及到记录数小时的视频片段，通常是老鼠或苍蝇。然后，研究人员必须为这些镜头添加标签，在进一步分析之前识别某些行为。对视频剪辑进行注释的任务--类似于图像字幕--对调查人员来说非常耗时。但它可以通过应用机器学习算法来自动化，并使用足够的数据进行训练。一些计算机程序已经被用来检测行为模式，然而，有一些限制。这些程序可以检测修剪视频剪辑中的动物行为（苍蝇和老鼠），但不能检测原始镜头，并且不能总是适应不同的照明条件或实验设置。在这里，Geuther等人着手改进这些先前的自动化视频注释的努力。为此，他们使用了经验丰富的研究人员注释的1，250多个视频片段来开发一个用于检测小鼠行为的通用神经网络。经过充分的训练后，计算机模型可以像人类观察者一样在未经修剪的原始视频剪辑中检测老鼠的梳理行为。它还在野外动物试验中对不同毛色、体型和大小的小鼠进行了研究。使用新的计算机模型，Geuther等人还研究了支持行为的遗传学--比以前可能的要彻底得多--以解释为什么老鼠表现出不同的梳理行为。该算法分析了2,250小时的视频，其中包括60多种老鼠和数千种其他动物。研究发现，实验室培育的老鼠比最近从野外收集的老鼠梳理得少。进一步的分析还确定了与小鼠梳理特征相关的基因，并在人类中发现了与行为障碍相关的基因。使用机器学习模型自动化视频注释可以减轻运行冗长行为实验的成本，并提高研究结果的可重复性。后者对于将小鼠的行为研究结果转化为人类至关重要。这项研究还提供了开发高性能计算机模型所需的人类注释训练数据量的见解，沿着对遗传学如何塑造行为的新理解。
Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms. Behavior is one of the ultimate and most complex outputs of the body’s central nervous system, which controls movement, emotion and mood. It is also influenced by a person’s genetics. Scientists studying the link between behavior and genetics often conduct experiments using animals, whose actions can be more easily characterized than humans. However, this involves recording hours of video footage, typically of mice or flies. Researchers must then add labels to this footage, identifying certain behaviors before further analysis. This task of annotating video clips – similar to image captioning – is very time-consuming for investigators. But it could be automated by applying machine learning algorithms, trained with sufficient data. Some computer programs are already in use to detect patterns of behavior, however, there are some limitations. These programs could detect animal behavior (of flies and mice) in trimmed video clips, but not raw footage, and could not always accommodate different lighting conditions or experimental setups. Here, Geuther et al. set out to improve on these previous efforts to automate video annotation. To do so, they used over 1,250 video clips annotated by experienced researchers to develop a general-purpose neural network for detecting mouse behaviors. After sufficient training, the computer model could detect mouse grooming behaviors in raw, untrimmed video clips just as well as human observers could. It also worked with mice of different coat colors, body shapes and sizes in open field animal tests. Using the new computer model, Geuther et al. also studied the genetics underpinning behavior – far more thoroughly than previously possible – to explain why mice display different grooming behaviors. The algorithm analyzed 2,250 hours of video featuring over 60 kinds of mice and thousands of other animals. It found that mice bred in the laboratory groom less than mice recently collected from the wild do. Further analyses also identified genes linked to grooming traits in mice and found related genes in humans associated with behavioral disorders. Automating video annotation using machine learning models could alleviate the costs of running lengthy behavioral experiments and enhance the reproducibility of study results. The latter is vital for translating behavioral research findings in mice to humans. This study has also provided insights into the amount of human-annotated training data needed to develop high-performing computer models, along with new understandings of how genetics shapes behavior.