Circuits for Emotion Research Group, Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Martinsried, Germany.
Graduate School of Systemic Neurosciences, Ludwig-Maximilians University, Munich, Germany.
Science. 2020 Apr 3;368(6486):89-94. doi: 10.1126/science.aaz9468.
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.
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，这在动物模型中仍然是一个主要挑战。我们发现，老鼠在对情绪相关的神经元回路进行有针对性的操作时，会对情绪相关的事件做出刻板的面部表情。我们使用机器学习对面部表情进行分类，并反映出在不同的稳态或情感条件下遇到相同感觉刺激时内在价值的变化。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，这些活动可能编码不同的情绪。因此，面部表情为推断老鼠的情绪状态及其神经元相关性提供了一种手段。
