近日，中国科学技术大学刘际团队研制了小鼠负性情绪相关行为与脉冲放电的联合研究。相关论文于2025年7月15日发表在《神经科学通报》杂志上。

在这项研究中，该课题组引入了一种新颖的方法NeuroSync，该方法将开放场行为测试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负性情绪的机制。通过应用机器视觉技术，课题组量化了开放领域的行为，信号处理算法阐明了观察到的行为的神经基础。同步行为和电生理数据揭示了神经放电模式和压力相关行为之间的显著相关性，为了解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。

据悉，在现代社会，人们越来越多地暴露在慢性压力下，导致各种各样的精神障碍。然而，大脑区域的活动，特别是与特定行为相关的神经放电模式，仍不清楚。

附：英文原文

Title: Combined Study of Behavior and Spike Discharges Associated with Negative Emotions in Mice

Author: Xin, Jinru, Wang, Xinmiao, Meng, Xuechun, Liu, Ling, Liu, Mingqing, Xiong, Huangrui, Liu, Aiping, Liu, Ji

Issue&Volume: 2025-07-15

Abstract: 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.

DOI: 10.1007/s12264-025-01455-8

Source: https://link.springer.com/article/10.1007/s12264-025-01455-8