Digital behavioral phenotyping and multi-region electrophysiology to determine behavioral and neural network changes underlying the stress response in mice

数字行为表型和多区域电生理学，以确定小鼠应激反应背后的行为和神经网络变化

• 批准号: 10397657
• 负责人: Brendon O Watson
• 金额: $ 69.09万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10397657
• 关键词: Addictive Behavior; Address; Affect; Anhedonia; Animal Behavior; Antidepressive Agents; Anxiety Disorders; Autopsy; Behavior; Behavior monitoring; Behavioral; Big Data; Biological Markers; Biology; Body Temperature; Brain; Brain imaging; Brain region; Chronic; Chronic stress; Circadian Rhythms; Clinical; Code; Communication; Coupled; Coupling; Data; Data Science; Decision Making; Diet Habits; Dimensions; Dose; Eating; Electrophysiology (science); Elements; Equilibrium; Evolution; Eye; Foundations; Future; Goals; Grooming; Hippocampus (Brain); Histologic; Human; Implant; Intervention; Ketamine; Knowledge; Libraries; Link; Machine Learning; Major Depressive Disorder; Measures; Medial; Mediating; Mental Health; Mental disorders; Molecular; Monitor; Moods; Mus; Nature; Neurobiology; Neurosciences; Nucleus Accumbens; Phenotype; Physiological; Physiology; Play; Predictive Analytics; Prefrontal Cortex; Process; Psychological Stress; REM Sleep; Recovery; Resolution; Response Elements; Rodent; Role; Site; Sleep; Solid; Stress; Structure; Substance Use Disorder; Sucrose; Suggestion; Synapses; System; Techniques; Testing; Time; Vision; Work; actigraphy; analytical method; base; behavior measurement; behavioral phenotyping; biological adaptation to stress; circadian; cohesion; data structure; digital; field study; forced swim test; hands-on learning; improved; innovation; insight; instrumentation; large datasets; machine learning method; neural circuit; neural network; neuroregulation; novel; preference; relating to nervous system; response; stressor; transmission process

ABSTRACT: Chronic psychological stress triggers and exacerbates major depressive disorder (MDD) and many other psychiatric conditions – causing changes in sleep, eating habits, addictive behaviors, activity levels, circadian rhythms, mood and other domains. The rodent stress response shares many behavioral and physiologic alterations with that of humans. Chronic stress also has broad effects on the brain. But major gaps exist in our knowledge in regard to the integrated behavior and physiology as well as the corresponding brain circuit changes with chronic stress. Prior work has found many behavioral and physiologic phenotypes of stress, but we lack a cohesive sense of how these variables co-evolve over time. Our first aim is to delineate this co-evolution of stress response elements in stressed versus unstressed mice. We will accomplish this by examining mice under a chronic unpredictable stress (CUS) paradigm versus controls in our new naturalistic observation system the “Digital Homecage”. This system allows us to monitor over 50 behavioral measures simultaneously over weeks. Mice will live in these homecages for 8 weeks: 2 weeks baseline, 4 weeks CUS and 2 weeks of recovery. An exploratory element of that aim is to use machine learning to determine a coherent mouse stress biomarker for future quantitative studies. Our next goal is to determine electrophysiologic signatures of chronic stress. It is known that chronic stress alters brain circuit synaptic structure and neuromodulatory balance. It is known that the behavior is controlled by the electrophysiologic state of brain networks and that those networks operate both locally within regions and via coordinated multi-regional transmission. Therefore, we aim to study changes in electrophysiology both within and across regions. We focus on the medial prefrontal cortex, the ventral hippocampus and infralimbic medial prefrontal cortex given their strong involvement in chronic stress. We will implant tetrode arrays into these regions and will record over 8 weeks as above. In Aim 2, we will determine the effects of chronic stress on within-region spiking tendencies including spike rate variability and excitatory- inhibitory balance. In a second part of this aim we will use machine learning applied to a wider variety of within- region dynamical measures to determine a potentially more complete set of differences between CUS and control mice. In our final Aim, we will assess cross-regional coordination between these 3 regions. We will test the hypothesis that pairwise coupling between regions will be altered in a manner consistent with MDD by measuring coupling using both spiking and LFP. Again, we will then use machine learning methods on our large dataset to detect further inter-regional dynamics un-revealed in our hypothesis-driven testing. This mixture of behavior and electrophysiology is done to generate new understanding about chronic stress. We also have a long-term vision of creating large dataset for future analysis, a fully-refined Digital Homecage system for future studies, with an eye towards developing interventions based on natural electrophysiologic circuit function.

抽象的： 慢性心理压力会引发并恶化重度抑郁症（MDD）和许多其他疾病 精神疾病——导致睡眠、饮食习惯、成瘾行为、活动水平、昼夜节律的变化 节律、情绪和其他领域的啮齿动物应激反应有许多共同的行为和生理特征。 慢性压力也会对大脑产生广泛的影响，但我们的大脑却存在重大差距。 有关综合行为和生理学以及相应脑回路变化的知识 先前的工作已经发现了许多压力的行为和生理表型，但我们缺乏一种方法。 我们的首要目标是描述压力的这种共同演化。 我们将通过在压力下检查小鼠来实现这一点。 慢性不可预测压力（CUS）范式与我们新的自然观察系统中的控制 “数字家庭笼”让我们能够在数周内同时监控 50 多种行为指标。 小鼠将在这些笼子中生活 8 周：2 周基线、4 周 CUS 和 2 周恢复期。 该目标的探索性要素是利用机器学习来确定一致的小鼠应激生物标志物 我们的下一个目标是确定慢性压力的电生理特征。 已知慢性压力会改变大脑回路突触结构和神经调节平衡。 该行为是由大脑网络的电生理状态决定的，并且这些网络同时运作 因此，我们的目标是研究区域内的局部变化和协调的多区域传播。 我们关注区域内和区域间的电生理学。 海马体和边缘下内侧前额叶皮层与慢性压力密切相关。 将四极阵列植入这些区域并将记录超过 8 周，在目标 2 中，我们将确定 慢性压力对区域内尖峰倾向的影响，包括尖峰率变异性和兴奋性 在这个目标的第二部分中，我们将使用机器学习应用于更广泛的内部。 区域动态措施，以确定 CUS 和 CUS 之间可能更完整的差异集 在我们的最终目标中，我们将评估这 3 个区域之间的跨区域协调。 假设区域之间的成对耦合将与 MDD 保持一致 使用尖峰和 LFP 测量耦合，然后我们将再次使用机器学习方法。 数据集，以进一步检测我们的假设驱动测试中未揭示的区域间动态。 行为和电生理学的研究是为了产生对慢性压力的新认识。 创建用于未来分析的大型数据集的长期愿景，面向未来的全面完善的数字家庭笼系统 研究，着眼于开发基于自然电生理回路功能的干预措施。