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.

摘要 睡眠占据了我们生活的很大一部分，人们普遍认为睡眠发挥着重要的功能。在.期间 睡眠、神经元参与规则和种群动态明显不同于清醒状态。 有大量证据表明，睡眠的一个主要功能是巩固在睡眠过程中形成的记忆 醒着的。然而，最近的研究表明，睡眠也可能会主动改变神经连接，从而 实现遗忘(“忘却”)。大脑是如何平衡学习和遗忘的，到底是如何睡眠的 对合奏和行为的最终影响是未知的。 这项建议的目标是确定在非快视-眼球运动过程中神经元群体动力学 运动睡眠(NREMS)实现学习与遗忘。我们的具体目标是检查活动是如何 在NREMS调节跨皮质和纹状体的耦合期间，神经的试验到试验的变异性 集合(“表象差异”)，因此行为的自动性与灵活性。 自动性--一致地产生受环境驱动的、快速而准确的行动--是 与皮质-纹状体耦合增强和可靠的整体活动(低方差)相关。在……里面 相比之下，较弱的皮质-纹状体连接与灵活的探索性状态和增强的 表象差异。 我们将使用运动技能学习的大鼠模型(伸展到抓取)，并结合多站点 电生理学，特定神经元亚型的鉴定，闭环系统扰动，以及详细 计算建模。我们将确定具体的NREMS动态如何以不同方式监管 重新激活神经元集合，以实现不同形式的依赖活动的可塑性。基于 大量的初步数据，我们假设与主轴耦合的全球慢振荡触发 皮质和纹状体亚区之间广泛协调的全球协调的重新激活。 这些协调的重新激活加强了功能联系，并产生了更早的直接刺激 通路的纹状体神经元，促进自律性。相比之下，在增量期间发生的局部UP状态 波产生不协调的重新激活，导致皮质纹状体功能脱钩 “间接通路”纹状体神经元的连接和更早的放电，以促进探索和灵活性。 我们紧密结合的电生理、光遗传学和计算机研究将提供 对睡眠期间大脑动力学如何实现这两种巩固的新的机械论见解 以及忘记特定的行为模式。这些知识有很大的潜力帮助我们更好地 了解并治疗与以下各项相关的广泛的神经和精神疾病 皮质纹状体突起的改变。