Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.

由于各种疾病固有的变量在人类中无法直接控制，因此在模型生物体中研究了行为功能障碍，从而更好地了解其潜在机制。然而，由于动物运动的空间和时间尺度在物种之间差异很大，传统的统计分析不能用于从运动数据中发现知识。我们提出了一种通过领域对抗性深度神经网络自动发现动物物种之间共享的运动特征的程序。我们的神经网络配备了一个功能，可以解释运动片段的含义，其中通过将注意力机制纳入神经网络（被视为黑匣子）来隐藏跨物种特征。它使我们能够制定关于跨物种运动特征的人类可解释的规则，并使用统计测试对其进行验证。我们通过识别多巴胺缺乏的不同物种（即人类、小鼠和蠕虫）所共有的运动特征来证明该过程的多功能性，尽管它们在进化上存在差异。