Striatal Plasticity in Habit Formation as a Platform to Deconstruct Adaptive Learning

习惯形成中的纹状体可塑性作为解构适应性学习的平台

基本信息

批准号：
10451714
负责人：
NICOLE CALAKOS
金额：
$ 101.85万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10451714
关键词：
Acute Adaptive Behaviors Animals Attention Automobile Driving Axon Behavior Behavioral Biological Models Brain Calcium Cells Commuting Competence Complement Complex Computer Models Corpus striatum structure Cre driver Discipline Disease Dopamine Dorsal Dystonia Electrophysiology (science)Elements Etiology Event Excitatory Synapse Exhibits Fire - disasters Foundations Functional disorder Generations Genetic Gilles de la Tourette syndrome Goals Habits Handwashing Huntington Disease Image Instruction Interneurons Knowledge Learning Link Maps Measurement Measures Medial Mediating Methods Modeling Molecular Genetics Monitor Motor Movement Mus Neurons Obsessive-Compulsive Disorder Output Parkinson Disease Pathway interactions Pharmaceutical Preparations Pharmacogenetics Play Population Positioning Attribute Preparation Process Production Reagent Reporter Resources Role Series Signal Transduction Site Slice Specific qualifier value Substance abuse problem Synapses Synaptic Receptors Synaptic plasticity Testing Thalamic structure Time Ursidae Family Weight Work Yin adaptive learning addiction autism spectrum disorder cell type cohesion compulsion density design experience experimental study habit learning in vivo interest learned behavior maladaptive behavior motor skill learning multidisciplinary nerve supply nervous system disorder network models neuropsychiatric disorder neuropsychiatric symptom novel novel therapeutic intervention postsynaptic predictive modeling presynaptic relating to nervous system response striosome success symptomatology targeted treatment therapeutic target therapy development tool

项目摘要

ABSTRACT A distinguishing feature of the brain is that its circuitry isn’t computationally static, it adapts to experience. Understanding the circuit mechanisms for adaptive behavior carries two-fold potential benefits - revealing the brain’s learning rules and identifying key behaviorally significant functional “nodes”. These nodes suggest potent sites to target for therapy development and may also be instructive to suggest more basic circuit principles underlying behavior. Using striatal circuitry and habit learning as a model system, we recently uncovered a set of paradigm- challenging findings in a striatum-dependent habit learning task. In particular, we discovered a new circuit-level signature, termed dviLP (direct vs indirect Latency Plasticity), which distinguishes striatal slices prepared from habitual vs goal-directed animals. The features of dviLP shift long-held attention on rate differences between the two principle projection neuron types, those to the direct and indirect pathways, to consider that behaviorally adaptive signals may be generated by plasticity of their relative timing to fire. Moreover, the origin of this plasticity appears to involve striatal fast-spiking interneurons, a highly non-canonical site for the expression of long-lasting plasticity. Beginning with this highly novel foundation, here we propose to generate a robust predictive computational model for striatal-dependent learning mechanisms by joining multiple disciplines and multiple levels of analysis through an iterative process of circuit modeling and experimentation. In Aim 1, we will comprehensively map functional changes in synaptic and cellular activity that define the behavioral transition from goal-directed to habitual in an operant lever press task. We will use a layered suite of molecular genetic tools to assign coordinates that specify inputs, outputs, compartments (striosome/matrix) and regions (medial, dorsal). In Aim 2, we will measure the activity of genetically specified components of the striatum in behaving mice, identifying the dynamic changes that correlate with and cause the shift from goal- directed to habitual behavior. Our team offers multidisciplinary strengths. Dr. Calakos and Yin have expertise in habit behavior, plasticity mechanisms and in vivo circuit dynamics; ideal for spearheading this effort. The success and impact of this effort will be amplified by tightly incorporating Dr. Brunel’s expertise in computationally modeling brain learning mechanisms and Dr. Tadross’s novel pharmacogenetic reagents that are ideally positioned to test causality of synaptic plasticity events, offering the unique opportunity to manipulate a specific synaptic receptor in a genetically defined cell type. Ultimately, we expect that the knowledge gained through this highly collaborative proposal will provide a foundational resource to accelerate understanding of striatal learning rules for adaptive behavior.

抽象的大脑的一个显着特征是其电路不是计算静态的，它适应经验。了解自适应行为的电路机制具有两倍的潜在优势 - 揭示大脑的学习规则并确定关键在行为上重要的功能“节点”。这些节点提出有效的地点以进行治疗开发，并且可能会有所帮助提出更多基础电路原则的基本行为。使用纹状体回路和习惯学习作为模型系统，我们最近发现了一组范式依赖纹状体的习惯学习任务中的挑战性发现。特别是，我们发现了一个新的电路级签名，称为DVILP（直接与间接延迟可塑性），将纹状体切片与制备的纹状体切片区分开习惯性与目标的动物。 DVILP移动的特征长期以来关注速率差异两种主要投射神经元类型，即直接和间接途径的类型，以考虑行为自适应信号可能是通过其相对时间发射的可塑性来产生的。而且，起源这种可塑性似乎涉及纹状体快速刺激性中间神经元，这是一个高度非规范的位点持久可塑性的表达。从这个高度新颖的基础开始，我们在这里提议生成一个通过加入多个纹状体依赖性学习机制的强大预测计算模型通过电路建模和实验的迭代过程，学科和多个分析级别。在AIM 1中，我们将全面绘制突触和细胞活动的功能变化，以定义在操作杠杆新闻任务中，从目标定向到习惯的行为过渡。我们将使用一套分层的套件分子遗传工具分配指定输入，输出，隔室（striosome/artrix）的坐标和区域（内侧，背）。在AIM 2中，我们将测量一般指定组件的活动行为小鼠的纹状体，确定与目标相关的动态变化并导致从目标转移针对习惯行为。我们的团队提供多学科的优势。 Calakos博士和Yin具有专业知识习惯行为，可塑性机制和体内电路动力学；率先进行这项工作的理想选择。这这项工作的成功和影响将通过紧密编码Brunel博士的专业知识来扩展计算对脑学习机制的建模和塔德罗斯博士的新型药物遗传试剂理想的位置可以测试突触可塑性事件的因果关系，从而提供了独特的机会在一般定义的细胞类型中操纵特定的突触受体。最终，我们期望通过这项高度协作的建议获得的知识将提供基本的资源来加速了解适应性行为的纹状体学习规则。