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 睡眠占据了我们生活的很大一部分，并且被广泛认为在睡眠期间发挥着重要作用。 睡眠时，参与的神经规则和群体动态与清醒时明显不同。 有大量证据表明，睡眠的一项主要功能是巩固睡眠期间形成的记忆。 然而，最近的研究表明，睡眠也可能主动改变神经连接。 实现遗忘（“忘记学习”）。大脑如何平衡学习和遗忘，确切地说是如何睡眠。 做出贡献，但对整体和行为的最终影响尚不清楚。 该提案的目标是确定非快速眼动期间神经群体动态如何 运动睡眠（NREMS）实现学习与遗忘的效果我们的具体目标是研究活动如何实现学习与遗忘。 在 NREMS 调节皮层和纹状体之间的耦合期间，神经元的试验间变异性 集成（“代表性方差”），从而导致行为自动化与灵活性。 自动化——一致地产生上下文驱动的、快速且准确的动作——是 与增加的皮质纹状体耦合和可靠的整体活动（低方差）相关。 相比之下，较弱的皮质纹状体耦合与灵活的探索状态和增加的状态相关。 代表性方差。 我们将使用大鼠运动技能学习模型（达到掌握），以及多站点 电生理学、特定神经亚型的识别、闭环扰动以及详细的 我们将确定特定的 NREMS 动力学如何差异调节。 神经系统的重新激活，以实现不同形式的活动依赖性可塑性。 广泛的初步数据，我们追踪与主轴触发耦合的全球缓慢振荡 全球协调的重新激活，在皮质和纹状体次区域之间广泛协调。 这些协调的重新激活加强了功能连接并更早地激发了“直接” 通路的纹状体神经元，促进自动化，相比之下，局部上状态发生在三角洲期间。 波产生不协调的重新激活，导致皮质纹状体的功能解耦 连接和“间接通路”纹状体神经元的早期激发，以促进探索和灵活性。 我们紧密集成的电生理学、光遗传学和计算研究将提供 关于睡眠期间大脑动态如何实现巩固的新机制见解 以及忘记特定的行为模式，这些知识有很大的潜力可以帮助我们更好地帮助我们。 了解并治疗与此相关的广泛的神经和精神疾病 皮质纹状体处理的改变。