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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.

摘要大脑的一个显著特征是它的电路在计算上不是静态的，它能适应经验。理解适应行为的电路机制有两个潜在的好处：揭示大脑的学习规则，并确定关键的行为意义重大的功能节点。这些节点建议有效的地点作为治疗开发的目标，并可能对建议更基本的行为背后的电路原理。使用纹状体回路和习惯学习作为模型系统，我们最近发现了一套范式-- 在纹状体依赖的习惯学习任务中挑战发现。特别是，我们发现了一种新的电路级标记，称为dviLP(直接与间接潜伏期可塑性)，区分从习惯性VS目标导向的动物。Dvilp频移的特点长期以来一直关注着两种主要的投射神经元类型，那些到直接通路和间接通路，来考虑行为适应性信号可能是由它们对射击的相对时间的可塑性产生的。而且，它的起源这种可塑性似乎涉及纹状体快速放电的中间神经元，这是一个高度非规范的位置表现出持久的可塑性。从这个非常新颖的基础开始，我们在这里建议生成一个纹状体依赖学习机制的稳健预测计算模型通过电路建模和实验的迭代过程进行多学科和多层次的分析。在目标1中，我们将全面绘制突触和细胞活动的功能变化图，这些变化定义了在操作杆按压任务中，从目标导向到习惯性的行为转变。我们将使用一套分层的分子遗传学工具分配指定输入、输出、隔间(纹状体/矩阵)的坐标和区域(内侧、背侧)。在目标2中，我们将测量基因指定的成分的活性在行为小鼠的纹状体，识别与目标相关并导致目标转移的动态变化- 指向习惯性行为。我们的团队拥有多学科的优势。卡拉科斯博士和殷博士在习惯行为、可塑性机制和体内电路动力学；是带头进行这一努力的理想选择。这个这一努力的成功和影响将通过紧密结合布鲁内尔博士在用计算机模拟大脑学习机制和Tadross博士的新型药物遗传试剂是测试突触可塑性事件因果关系的理想位置，提供了独特的机会在基因定义的细胞类型中操纵特定的突触受体。最终，我们预计通过高度协作的计划书获得的知识将提供基础资源，以加速了解纹状体适应行为的学习规则。