How to read the face of a mouse The neuroscientific investigation of emotions is hindered by a lack of rapid and precise readouts of emotion states in model organisms. Dolensek et al. identified facial expressions as innate and sensitive reflections of the internal emotion state in mice (see the Perspective by Girard and Bellone). Mouse facial expressions evoked by diverse stimuli could be classified into emotionlike categories, similar to basic emotions in humans. Machine-learning algorithms categorized mouse facial expressions objectively and quantitatively at millisecond time scales. Intensity, value, and persistence of subjective emotion states could thus be decoded in individual animals. Combining facial expression analysis with two-photon calcium imaging allowed the identification of single neurons whose activity closely correlated with specific facial expressions in the insular cortex, a brain region implicated in affective experiences in humans. Science, this issue p. 89; see also p. 33 In mice, facial expressions can be used to infer an animal’s current emotions and thereby allow mechanistic study of basic emotions. Understanding the neurobiological underpinnings of emotion relies on objective readouts of the emotional state of an individual, which remains a major challenge especially in animal models. We found that mice exhibit stereotyped facial expressions in response to emotionally salient events, as well as upon targeted manipulations in emotion-relevant neuronal circuits. Facial expressions were classified into distinct categories using machine learning and reflected the changing intrinsic value of the same sensory stimulus encountered under different homeostatic or affective conditions. Facial expressions revealed emotion features such as intensity, valence, and persistence. Two-photon imaging uncovered insular cortical neuron activity that correlated with specific facial expressions and may encode distinct emotions. Facial expressions thus provide a means to infer emotion states and their neuronal correlates in mice.

中文翻译：

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小鼠情绪状态的面部表情及其神经元相关性

如何读懂老鼠的脸 模型生物中缺乏对情绪状态的快速准确读数，阻碍了对情绪的神经科学研究。多伦塞克等人。将面部表情识别为小鼠内部情绪状态的先天和敏感反映（参见 Girard 和 Bellone 的观点）。由不同刺激引起的小鼠面部表情可以分为类似情绪的类别，类似于人类的基本情绪。机器学习算法在毫秒时间尺度上客观和定量地对鼠标面部表情进行分类。因此，可以在个体动物中解码主观情绪状态的强度、价值和持久性。将面部表情分析与双光子钙成像相结合，可以识别单个神经元，其活动与岛叶皮层中的特定面部表情密切相关，岛叶皮层是一个与人类情感体验有关的大脑区域。科学，这个问题 p。89; 另见第 33 在老鼠身上，面部表情可用于推断动物当前的情绪，从而可以对基本情绪进行机械研究。理解情绪的神经生物学基础依赖于个体情绪状态的客观读数，这仍然是一个重大挑战，尤其是在动物模型中。我们发现小鼠表现出刻板的面部表情，以响应情绪显着的事件，以及对情绪相关神经元回路的有针对性的操作。面部表情使用机器学习分为不同的类别，并反映了在不同稳态或情感条件下遇到的相同感官刺激的变化内在价值。面部表情揭示了情绪特征，如强度、效价和持久性。双光子成像揭示了与特定面部表情相关的岛叶皮质神经元活动，并可能编码不同的情绪。因此，面部表情提供了一种推断小鼠情绪状态及其神经元相关性的方法。双光子成像揭示了与特定面部表情相关的岛叶皮质神经元活动，并可能编码不同的情绪。因此，面部表情提供了一种推断小鼠情绪状态及其神经元相关性的方法。双光子成像揭示了与特定面部表情相关的岛叶皮质神经元活动，并可能编码不同的情绪。因此，面部表情提供了一种推断小鼠情绪状态及其神经元相关性的方法。