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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年7月15日

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

摘要

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.