Xin Jinru, Wang Xinmiao, Meng Xuechun, Liu Ling, Liu Mingqing, Xiong Huangrui, Liu Aiping, Liu Ji
National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.
Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230026, China.
Neurosci Bull. 2025 Jul 15. doi: 10.1007/s12264-025-01455-8.
In modern society, people are increasingly exposed to chronic stress, leading to various mental disorders. However, the activities of brain regions, especially neural firing patterns related to specific behaviors, remain unclear. In this study, we introduce a novel approach, NeuroSync, which integrates open-field behavioral testing with electrophysiological recordings from emotion-related brain regions, specifically the central amygdala and the paraventricular nucleus of the hypothalamus, to explore the mechanisms of negative emotions induced by chronic stress in mice. By applying machine vision techniques, we quantified behaviors in the open field, and signal processing algorithms elucidated the neural underpinnings of the observed behaviors. Synchronizing behavioral and electrophysiological data revealed significant correlations between neural firing patterns and stress-related behaviors, providing insights into real-time brain activity underlying behavioral responses. This research combines deep learning and machine learning to synchronize high-resolution video and electrophysiological data, offering new insights into neural-behavioral dynamics under chronic stress conditions.
在现代社会，人们越来越多地面临慢性压力，这会导致各种精神障碍。然而，大脑区域的活动，尤其是与特定行为相关的神经放电模式，仍不清楚。在本研究中，我们引入了一种新方法——神经同步（NeuroSync），该方法将旷场行为测试与来自与情绪相关的大脑区域（特别是中央杏仁核和下丘脑室旁核）的电生理记录相结合，以探索慢性应激诱导小鼠产生负面情绪的机制。通过应用机器视觉技术，我们对旷场中的行为进行了量化，信号处理算法阐明了所观察到行为的神经基础。行为数据与电生理数据的同步揭示了神经放电模式与应激相关行为之间的显著相关性，为行为反应背后的实时大脑活动提供了见解。这项研究结合了深度学习和机器学习来同步高分辨率视频和电生理数据，为慢性应激条件下的神经行为动力学提供了新的见解。
