Functional architecture of striatal networks in cue-reward learning

项目摘要 中皮质纹状体网络是适应性奖励过程的核心，也是神经系统疾病的核心。 学习、动机和认知功能失调。随着学习的进展，有一个过渡，从最初的 依赖于背外侧纹状体（DLS）的募集，这推动了一个转变 从目标导向的行为到以快速、刺激驱动的运动为特征的更自动的行为 序列的这种转变被认为涉及一个循环网络，包括密集的多巴胺能神经元。 (DA)来自腹侧被盖区（VTA）/黑质（SNC）的输入和来自皮层的海马能输入。 然而，至关重要的是，这些回路的贡献还没有被直接测试，电路机制 驱动这种基本的神经生物学适应仍然不清楚。在拟议的研究中，我们将使用 几种创新的方法来研究中皮质纹状体网络中不同的环路系统 在体内沟通，以组织条件行为，并从腹侧纹状体控制过渡到背侧纹状体控制， 跨越学习。一个有影响力的解剖学框架，纹状体中膜"螺旋"假说，表明， 在学习过程中，信息通过皮层下回路连续流动，从腹侧被盖区到延髓核， SNC，背外侧纹状体。尽管在该领域被广泛接受，但对纹状体螺旋的支持还没有被广泛接受。 在活体中证明。相反，新出现的证据提出了另一种假设：线索奖励学习 通过黑质-丘脑-皮质回路参与背侧纹状体的渐进性募集，而不是 典型的纹状体螺旋机制在目的1中，我们将联合收割机结合体DA神经元的纤维光度记录 活动在TH-cre大鼠与DA生物传感器在纹状体的同时记录，以表征 提示-奖赏过程中纹状体DA系统四个节点信息流的时空模式 (i.e.,巴甫洛夫）学习，测试螺旋框架的预测。在目标2中，我们将使用跨突触 靶向、光遗传学和光度测定来测试体内的上升螺旋框架， 直接通路神经元解除SNC DA神经元的抑制。我们将使用D1-cre大鼠来研究直接 在不同的学习阶段，NAC和DLS的通路输出神经元。最后，在目标3中，我们将联合收割机 DA光遗传学调控下皮质纹状体和丘脑纹状体回路的光度学记录 TH-cre大鼠中的神经元，以评估丘脑核DA信号传导参与黑质-丘脑-垂体-肾上腺皮质-肾上腺皮质-垂体-肾上腺皮质-肾上腺 皮质环然后，我们将光遗传学操纵投射到丘脑的输入定义的黑质神经元， 确定黑质-丘脑-皮质回路在学习中的功能作用。这些研究将解决 关于纹状体输入-输出回路中信息流的回路机制的长期问题， 建立一个规范的框架，在体内的功能架构的mesocorticostriatal网络。

Project Summary The mesocorticostriatal network is central to adaptive reward processes, as well as diseases of dysfunctional learning, motivation, and cognition. As learning progresses, there is a transition from initial reliance on nucleus accumbens (NAC) to later recruitment of dorsolateral striatum (DLS), which drives a shift from goal-directed behavior to more automatic behaviors characterized by rapid, stimulus-driven movement sequences. This transition is thought to involve a network of recurrent loops, including dense dopaminergic (DA) input from the ventral tegmental area (VTA)/substantia nigra (SNC) and glutamatergic input from cortex. Critically, however, the contributions of these loops have not been directly tested, and the circuit mechanisms driving this fundamental neurobiological adaptation remain undefined. In the proposed studies, we will use several innovative approaches to investigate how different loop systems within the mesocorticostriatal network communicate in vivo to organize conditioned behavior, and transition from ventral to dorsal striatal control, across learning. One influential anatomical framework, the mesostriatal “spiral” hypothesis, suggests that during learning, information flows serially across a subcortical loop, from the VTA to nucleus accumbens to SNC, to dorsolateral striatum. Despite broad acceptance in the field, support for the striatal spiral has not been demonstrated in vivo. Instead, emerging evidence suggests an alternative hypothesis: that cue-reward learning engages progressive recruitment of the dorsal striatum via nigro-thalamo-cortical circuits, rather than the classic striatal spiral mechanism. In Aim 1 we will combine fiber photometry recordings of somatic DA neuron activity in TH-cre rats with simultaneous recordings of a DA biosensor in the striatum, to characterize the spatial and temporal pattern of information flow through four nodes in the striatal DA system during cue-reward (i.e., Pavlovian) learning, testing predictions from the spiral framework. In Aim 2, we will use trans-synaptic targeting, optogenetics, and photometry to test the ascending spiral framework in vivo, determining if NAC direct pathway neurons disinhibit SNC DA neurons. We will use D1-cre rats to investigate the function of direct pathway output neurons in the NAC and DLS at different stages of learning. Finally, In Aim 3 we will combine photometry recordings of corticostriatal and thalamostriatal circuits with optogenetic manipulation of DA neurons in TH-cre rats, to assess the ability of nucleus accumbens DA signaling to engage the nigro-thalamo- cortical loop. We will then optogenetically manipulate input-defined nigral neurons projecting to the thalamus to determine the functional role of the nigro-thalamo-cortical loop in learning. These studies will resolve longstanding questions about the circuit mechanisms of information flow across striatal input-output circuits, establishing a normative framework for the in vivo functional architecture of the mesocorticostriatal network.