Role of coordinated multi-area reactivations during transitions between automatic and flexible behaviors.

在自动行为和灵活行为之间转换期间协调的多区域重新激活的作用。

• 批准号: 10721280
• 负责人: MAKSIM V BAZHENOV
• 金额: $ 296.51万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721280
• 关键词: Area; Back; Behavior; Behavioral; Brain; Brain region; Cells; Computer Models; Corpus striatum structure; Coupled; Coupling; Data; Dystonia; Electrophysiology (science); Event; Failure; Fire - disasters; Goals; Investigation; Knowledge; Lead; Learning; Link; Maintenance; Medial; Memory; Mental disorders; Modeling; Motor Cortex; Motor Skills; Movement; Neurologic; Neurons; Obsessive-Compulsive Disorder; Parkinson Disease; Pathway interactions; Pattern; Performance; Population Dynamics; Prefrontal Cortex; Process; Production; Rattus; Role; Site; Sleep; Slow-Wave Sleep; Task Performances; Testing; Thalamic structure; Time; Training; Work; addiction; awake; cell type; experience; flexibility; forgetting; grasp; insight; memory consolidation; motor skill learning; neural; non rapid eye movement; novel; novel therapeutics; optogenetics; prevent; sleep spindle; stroke recovery

Abstract Sleep occupies a large part of our lives and is widely believed to perform essential functions. During sleep, the neuronal rules of engagement and population dynamics are clearly different than waking. There is extensive evidence that one primary function of sleep is to consolidate memories formed during waking. Recent work, however, suggests that sleep may also actively alter neural connections to achieve forgetting (‘unlearning’). How the brain balances learning and forgetting, exactly how sleep contributes, and the ultimate effects on ensembles and behavior are unknown. The goal of this proposal is to determine how neuronal population dynamics during non-rapid-eye- movement sleep (NREMS) achieve learning vs forgetting. We specifically aim to examine how activity during NREMS regulates coupling across the cortex and the striatum, trial-to-trial variability of neural ensembles (“representational variance”) and thereby behavioral automaticity versus flexibility. Automaticity – the consistent production of a contextually-driven, fast and accurate action – is associated with increased cortico-striatal coupling and reliable ensemble activity (low variance). In contrast, weaker cortico-striatal coupling is associated with flexible, exploratory states and increased representational variance. We will use a rat model of motor skill learning (reach-to-grasp), together with multi-site electrophysiology, identification of specific neuronal subtypes, closed-loop perturbations, and detailed computational modelling. We will determine how specific NREMS dynamics differentially regulate the reactivation of neuronal ensembles, to enable distinct forms of activity-dependent plasticity. Based on extensive preliminary data, we hypothesize that global slow oscillations coupled to spindles trigger globally coordinated reactivations that are broadly coordinated between cortical and striatal subregions. These coordinated reactivations strengthen functional connections and produce earlier firing of ‘direct pathway’ striatal neurons, promoting automaticity. By contrast, local up-states occurring during delta waves produce uncoordinated reactivations, leading to functional decoupling of corticostriatal connections and earlier firing of ‘indirect pathway’ striatal neurons to promote exploration and flexibility. Our closely integrated electrophysiological, optogenetic and computational investigations will provide novel mechanistic insights into precisely how brain dynamics during sleep achieve both consolidation and unlearning of specific behavioral patterns. This knowledge has great potential to help us better understand and treat the wide range of neurological and psychiatric illnesses associated with alterations in cortico-striatal processing.

抽象的 睡眠占据我们一生的很大一部分，被广泛认为可以发挥基本功能。期间 睡眠，互动和人口动态的神经元规则明显不同于醒来。 有广泛的证据表明，睡眠的一个主要功能是巩固在 醒来。然而，最近的工作表明睡眠也可能会主动改变神经联系 实现忘记（“学习”）。大脑如何平衡学习和忘记，确切的睡眠方式 贡献，对合奏和行为的最终影响尚不清楚。 该提案的目的是确定非rapid-eye-中的神经元种群动态如何 运动睡眠（NREMS）实现学习与忘记。我们特别旨在研究如何活动 在NREM期间，调节​​跨皮质和纹状体的耦合，中性的试验变异性 合奏（“代表性方差”），从而行为自动与灵活性。 自动化 - 上下文驱动，快速准确的动作的一致生产 - 是 与增加的皮质 - 纹状体耦合和可靠的集合活性（较低方差）有关。 对比，皮质 - 纹状体耦合较弱与柔性，探索状态相关，并增加 代表性方差。 我们将使用大鼠的运动技能学习模型（接触到抓地力），并使用多站点 电生理学，特定神经元亚型的鉴定，闭环扰动和详细的 计算建模。我们将确定特定的NREM动态如何不同地调节 神经元合奏的重新激活，以实现不同形式的活性依赖性可塑性。基于 广泛的初步数据，我们假设全球缓慢的振荡与纺锤体触发相连 在皮质和纹状体子区域之间广泛协调的全球协调重新激活。 这些协调的重新激活增强了功能连接，并产生了较早的直接触发 途径的纹状体神经元，促进自动化。相比之下，在三角洲期间发生的本地向上国家 波产生了不协调的重新激活，导致皮质纹状体的功能脱钩 连接和更早的“间接途径”纹状体神经元的触发，以促进探索和灵活性。 我们紧密整合的电生理，光遗传学和计算研究将提供 对睡眠过程中脑动力学如何实现的新机械洞察 和特定行为模式的学习。这些知识具有帮助我们更好的潜力 了解和治疗与之相关的广泛神经和精神病 皮质 - 纹状体加工的改变。