Spatiotemporal dynamics of locus coeruleus circuits during learned behavior

蓝斑(LC)是一种小的脑干核团，是神经调节剂的主要来源 大脑中的去甲肾上腺素(NE)。LC接受来自广泛的大脑区域和贯穿整个大脑的投射的输入 前脑、脑干、小脑和脊髓。LC神经元释放去甲肾上腺素调节基础觉醒， 并阶段性地在各种感觉-运动和行为功能的背景下进行。然而，尽管它的大脑- 广谱效应、LC-NE神经元相激活的条件和NE的作用方式 在行为过程中，人们对此知之甚少。一种流行的理论认为，去甲肾上腺素控制产量的增加 回路，从而通过增强或抑制对刺激的反应来调节任务绩效。然而， 另一种理论认为，皮质输出区的NE释放起到重置网络活动的作用，使任务- 改变或学习新规则。这两种理论都不能很好地解释 学习和行为方面的LC-NE系统。我们提出了LC函数的一个新的假设，即时空 动力学和模块化电路使LC在行为执行和强化中扮演分离的角色 在习得的行为中。在这里，我们建议使用Innovative来检验这一假设的多个特征 结合特定神经元群体的光学标记记录、先进的双光子成像的方法 已识别的神经元和轴突，LC神经元和亚群的光遗传操作，以及计算 定义神经元对任务变量进行编码的方法。在目标1中，我们将记录和操作活动 在执行仪器条件任务的小鼠的LC神经元中，它们检测到 可变强度，执行响应，并接受积极或消极的强化。使用定向录音 从LC-NE神经元以及新发现的LC-GABA神经元，我们将检验这一假设 LC-NE和LC-GABA神经元的子集编码任务执行信号或强化信号。使用这个 信息，我们将使用细胞类型的特定光遗传学来激活或抑制LC-NE或LC-GABA神经元 在衡量对行为的影响时，具体的任务纪元。在目标2中，我们将评估 LC投射到运动皮质或前额叶皮质(PFC)，并检验神经元与 执行或强化反应优先投射到运动皮质或PFC。随后，我们将 调节投射到这些靶点的LC神经元的活动，并测量对执行和 学习。在目标3中，我们将检验这一假设，即运动皮质和大脑皮质中NE释放的差异整合 PFC分别促进了任务的执行和学习。我们将监测发动机中去甲肾上腺素释放的快速动力学 皮层和PFC使用遗传编码的NE传感器，并测量对静默NE行为的影响 利用LC-NE轴突的光遗传沉默在这些皮质靶点中的活动。这些数据将提供必要的 为LC在认知中的作用的计算知情理论提供信息，并提供机械基础 以了解LC-NE功能障碍在一系列神经精神障碍中的作用。

The locus coeruleus (LC), a small brainstem nucleus, is the primary source of the neuromodulator norepinephrine (NE) in the brain. LC receives input from widespread brain regions and projects throughout the forebrain, brainstem, cerebellum, and spinal cord. LC neurons release NE tonically to regulate baseline arousal, and phasically in the context of a variety of sensory-motor and behavioral functions. However, despite its brain- wide effects, the conditions under which LC-NE neurons are phasically activated and the modes of NE action during behavior are poorly understood. One prevailing theory suggests that NE acts to control the gain of output circuits, thereby modulating task performance by enhancing or dampening responses to stimuli. However, another theory suggests that NE release in cortical output regions acts to reset network activity, enabling task- switching or learning of new rules. Neither of these theories adequately explains the many observed roles of the LC-NE system in learning and behavior. We propose a new hypothesis of LC function, that spatiotemporal dynamics and modular circuits enable dissociated roles for the LC in behavioral execution and reinforcement during learned behaviors. Here, we propose to examine multiple features of this hypothesis using innovative approaches combining optically-tagged recordings of specific neuronal populations, advanced 2-photon imaging of identified neurons and axons, optogenetic manipulation of LC neurons and subpopulations, and computational approaches to define encoding of task variables by neurons. In Aim 1, we will record and manipulate the activity of LC neurons in mice performing an instrumentally conditioned task in which they detect auditory tones of variable intensity, execute a response, and receive positive or negative reinforcement. Using targeted recordings from LC-NE neurons as well as newly discovered LC-GABA neurons, we will examine the hypothesis that subsets of LC-NE and LC-GABA neurons encode task execution signals or reinforcement signals. Using this information, we will use cell-type specific optogenetics to activate or inhibit LC-NE or LC-GABA neurons during specific task epochs while measuring the effects on behavior. In Aim 2, we will assess anatomical modularity of LC projections to motor cortex or the prefrontal cortex (PFC), and examine the hypothesis that neurons with execution or reinforcement responses project preferentially to motor cortex or PFC. Subsequently, we will modulate the activity of LC neurons projecting to these targets and measure the effects on execution and learning. In Aim 3, we will examine the hypothesis that differential integration of NE release in motor cortex and PFC facilitates task execution and learning, respectively. We will monitor the fast kinetics of NE release in motor cortex and PFC using a genetically encoded NE sensor, and measure the impact on behavior of silencing NE activity in these cortical targets using optogenetic silencing of LC-NE axons. These data will provide essential information for a computationally informed theory of the role of LC in cognition, and provide a mechanistic basis for understanding the role of LC-NE dysfunction in a range of neuropsychiatric disorders.