Spatiotemporal dynamics of locus coeruleus circuits during learned behavior

蓝斑（LC）是一个小的脑干核团，是神经调质的主要来源 去甲肾上腺素（NE）在大脑中。LC从广泛的大脑区域接收输入，并在整个大脑中投射 前脑脑干小脑和脊髓LC神经元紧张性地释放NE以调节基线唤醒， 以及在各种感觉运动和行为功能的背景下的阶段性。然而，尽管它的大脑- 广泛的影响，LC-NE神经元相激活的条件和NE的作用方式 对行为的理解很少。一种流行的理论认为NE起着控制输出增益的作用 电路，从而通过增强或抑制对刺激的反应来调节任务性能。然而，在这方面， 另一种理论认为，皮层输出区域的NE释放起到重置网络活动的作用，使任务- 学习新的规则或学习新的规则。这些理论都不能充分解释许多观察到的作用， LC-NE系统在学习和行为中的作用我们提出了一个新的LC函数假设，即时空 动力学和模块化电路使LC在行为执行和强化中的角色分离 在学习行为中。在这里，我们建议使用创新的方法来检查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 识别的神经元和轴突，LC神经元和亚群的光遗传学操纵，以及计算 定义神经元对任务变量编码的方法。在目标1中，我们将记录和操作活动 LC神经元在小鼠执行工具条件任务，其中他们检测听觉音调， 可变强度，执行响应，并接受积极或消极的强化。使用目标录音 从LC-NE神经元以及新发现的LC-GABA神经元，我们将检验以下假设： LC-NE和LC-GABA神经元的子集编码任务执行信号或强化信号。使用此 信息，我们将使用细胞类型特异性光遗传学来激活或抑制LC-NE或LC-GABA神经元， 特定任务时期，同时测量对行为的影响。在目标2中，我们将评估 LC投射到运动皮层或前额叶皮层（PFC），并检查假设， 执行或强化反应优先投射到运动皮层或PFC。随后，我们将 调节投射到这些目标的LC神经元的活动，并测量对执行的影响， 学习在目标3中，我们将检验运动皮层和海马中NE释放的差异整合的假设。 PFC分别促进任务执行和学习。我们将监测NE释放的快速动力学在运动 皮质和PFC使用基因编码的NE传感器，并测量沉默NE对行为的影响 使用LC-NE轴突的光遗传学沉默在这些皮质靶标中的活性。这些数据将提供重要的 信息的计算知情理论的作用，LC在认知，并提供了一个机械的基础 了解LC-NE功能障碍在一系列神经精神疾病中的作用。

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.