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: 10577805
• 负责人: Brendon O Watson
• 金额: $ 70.39万
• 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/10577805
• 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; Foundations; Future; Goals; Grooming; Hippocampus; Histologic; Home; 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; Sleep Deprivation; Solid; Stress; Structure; Substance Use Disorder; Sucrose; Synapses; System; Techniques; Testing; Time; Work; actigraphy; analytical method; behavior measurement; behavioral phenotyping; biological adaptation to stress; circadian; data structure; digital; digital measure; field study; forced swim test; forging; hands-on learning; improved; innovation; insight; instrumentation; large datasets; machine learning method; neural; neural circuit; neural network; neuroregulation; novel; preference; response; stress reduction; 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.

摘要： 慢性心理压力触发并加剧了重度抑郁症（MDD）和许多其他抑郁症。 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 节奏、情绪和其他领域。啮齿类动物的应激反应有许多行为和生理上的共同点， 与人类的变化。慢性压力对大脑也有广泛的影响。但是，我们在这方面存在着重大差距， 关于综合行为和生理以及相应的脑回路变化的知识 慢性压力先前的工作已经发现了许多压力的行为和生理表型，但我们缺乏一个明确的解释。 这些变量如何随着时间的推移共同演变的凝聚力。我们的第一个目标是描述这种压力的共同进化 应激与非应激小鼠的反应元件。我们将通过在一个 慢性不可预测的压力（CUS）范式与控制在我们的新的自然观察系统， 数字家庭这个系统允许我们在数周内同时监测50多个行为指标。 小鼠将在这些饲养笼中生活8周：2周基线，4周CUS和2周恢复期。一个 该目标探索性元素是使用机器学习来确定用于以下的一致的小鼠应激生物标志物： 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。是 已知慢性压力会改变脑回路突触结构和神经调节平衡。已知的是 行为是由大脑网络的电生理状态控制的，而这些网络既运行 在区域内局部地以及经由协调的多区域传输。因此，我们的目标是研究 区域内和跨区域的电生理学。我们关注内侧前额叶皮层， 海马和边缘下内侧前额叶皮质，因为它们与慢性应激密切相关。我们将 将四极管阵列植入这些区域，并如上所述记录8周以上。在目标2中，我们将确定 慢性应激对区域内峰电位倾向的影响，包括峰电位频率变异性和兴奋性- 抑制平衡在这个目标的第二部分，我们将使用机器学习应用于更广泛的各种内部- 区域动态测量，以确定CUS和 对照小鼠。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 假设区域之间的成对耦合将以与MDD一致的方式改变， 使用尖峰和LFP测量耦合。同样，我们将在我们的大型机器上使用机器学习方法， 数据集来检测我们的假设驱动测试中未揭示的进一步区域间动态。的这种混合物 行为和电生理学的研究，以产生对慢性压力的新认识。我们也有一个 长期的愿景是为未来的分析创建大型数据集，一个完全完善的数字家庭系统， 研究，着眼于开发基于自然电生理回路功能的干预措施。