Smith Hannah C, Yu Zhe, Yazigi Patrick, Turley Benjamin, Swiercz Adam P, Park Jeanie, Marvar Paul J
Departments of Pharmacology and Physiology, George Washington University, Washington, DC, 20052, USA.
School of Medicine, Georgetown University, Washington, DC, 20057, USA.
Neurobiol Stress. 2025 Jun 19;37:100742. doi: 10.1016/j.ynstr.2025.100742. eCollection 2025 Jul.
Post-traumatic stress disorder (PTSD) is a complex and prevalent neuropsychiatric condition that arises in response to exposure to a traumatic event. A common diagnostic criterion for PTSD includes heightened physiological reactivity to trauma-related sensory cues, in safe or familiar environments. Understanding complex PTSD criteria requires new pre-clinical paradigms and technologies that integrate sensory physiology (e.g., auditory, visual, olfactory) with behavior. Here we present a novel Pavlovian-based paradigm using an open-source software plus deep learning-based pose estimation to investigate the effects of a recurrent conditioned stimulus (CS) on fear behaviors in pair-housed mice within the home cage. Simultaneous home cage video recording and analysis of CS-evoked freezing behaviors were performed using a deep learning model, with consideration for light-dark circadian cycles. Fear-conditioned dyad mice exhibited high CS-evoked freezing, with evidence of extinction learning (characterized by low freezing) during the mid-phase of the 2-week paradigm. Females exhibited reduced CS-evoked home cage freezing compared to males with circadian differences between the light (low freezing) and dark (high freezing) periods. Following the 2-week paradigm, fear-conditioned mice, compared to controls, exhibited heightened context-dependent freezing, while males but not females showed heightened startle reactivity. Taken together, these results demonstrate a novel software application for examining conditioned defensive and fear behaviors over time in mouse dyads within an ethologically relevant environment. Future applications could be used for more integrative analysis and understanding of neural circuits and heightened sensory threat reactivity, potentially improving the understanding and treatment of PTSD.
创伤后应激障碍（PTSD）是一种复杂且普遍的神经精神疾病，由暴露于创伤性事件引发。PTSD的一个常见诊断标准包括在安全或熟悉的环境中，对与创伤相关的感官线索产生增强的生理反应。理解复杂的PTSD标准需要新的临床前范式和技术，将感官生理学（如听觉、视觉、嗅觉）与行为整合起来。在这里，我们提出了一种基于巴甫洛夫范式的新方法，使用开源软件加上基于深度学习的姿态估计，来研究反复呈现的条件刺激（CS）对同笼饲养小鼠在家笼中的恐惧行为的影响。利用深度学习模型同时进行家笼视频记录和对CS诱发的僵住行为的分析，并考虑明暗昼夜节律周期。恐惧条件化的成对小鼠表现出较高的CS诱发僵住反应，在为期2周的范式中期有消退学习的证据（以低僵住为特征）。与雄性相比，雌性的CS诱发家笼僵住反应降低，且在光照期（低僵住）和黑暗期（高僵住）之间存在昼夜节律差异。在为期2周的范式之后，与对照组相比，恐惧条件化的小鼠表现出增强的情境依赖性僵住反应，而雄性而非雌性表现出增强的惊吓反应性。综上所述，这些结果证明了一种新的软件应用，可用于在行为学相关环境中随时间检查小鼠成对中的条件性防御和恐惧行为。未来的应用可用于对神经回路和增强的感官威胁反应性进行更综合的分析和理解，有可能改善对PTSD的理解和治疗。
