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： 慢性心理压力触发和加剧重大抑郁症（MDD）和许多其他 精神病疾病 - 导致睡眠变化，饮食习惯，添加剂行为，活动水平，昼夜节律 节奏，情绪和其他领域。啮齿动物应力反应具有许多行为和生理性 人类的改变。慢性压力也对大脑也有广泛的影响。但是我们的 关于综合行为和生理学以及相应的大脑回路的知识变化 有慢性压力。先前的工作发现压力的许多行为和生理表型，但我们缺乏 这些变量如何随着时间的流逝而共同进化的结合感。我们的第一个目的是描述这种压力的共同发展 压力与无重理小鼠的反应元素。我们将通过在一个下检查小鼠来实现这一目标 慢性不可预测的压力（CUS）范式与我们的新自然主义观察系统中 “数字归宿”。该系统允许我们仅在数周内监视50多种行为措施。 小鼠将在这些家庭中生活8周：基线2周，4周CUS和2周的恢复。一个 该目的的探索性元素是使用机器学习来确定相干的小鼠压力生物标志物 未来的定量研究。我们的下一个目标是确定慢性应激的电生理特征。这是 知道慢性应力会改变脑电路的合成结构和神经调节平衡。众所周知 该行为由大脑网络的电生理状态控制，并且这些网络都可以运作 在区域内并通过协调的多区域传输。因此，我们旨在研究变更 在区域内部和跨区域的电生理学。我们专注于媒体前额叶皮层，腹侧 海马和额叶介质前额叶皮层的强烈参与慢性应激。我们将 植入四极阵列进入这些区域，并将在上述8周内记录。在AIM 2中，我们将确定 慢性应激对区域内尖峰趋势的影响，包括峰值速率变异性和兴奋性 - 抑制平衡。在此目标的第二部分中，我们将使用适用于多种内部的机器学习 区域动态度量，以确定CUS和 控制小鼠。在我们的最终目标中，我们将评估这三个区域之间的跨区域协调。我们将测试 区域之间成对耦合将以与MDD一致的方式改变的假设 使用尖峰和LFP测量耦合。同样，我们将在我们的大型上使用机器学习方法 在我们的假设驱动的测试中，数据集检测进一步的区域间动力学。这种混合物 行为和电生理学是为了产生对慢性压力的新理解。我们也有一个 创建大型数据集以供将来分析的长期愿景，这是一个完整的数字归乡系统，供未来 研究，着眼于基于天然电生理电路功能开发干预措施。