Parameterizing the relationship between motor cortical reactivation during sleep and motor skill acquisition in the freely behaving marmoset

参数化睡眠期间运动皮层重新激活与自由行为狨猴运动技能习得之间的关系

• 批准号: 10658109
• 负责人: Nicholas G Hatsopoulos
• 金额: $ 208.51万
• 依托单位: UNIVERSITY OF CHICAGO
• 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/10658109
• 关键词: Affect; Algorithms; Behavior; Behavioral; Callithrix; Callithrix jacchus jacchus; Dependence; Electrophysiology (science); Event; Frequencies; Future; Hour; Human; Human body; Learning; Limb structure; Link; Literature; Measures; Memory; Methodology; Methods; Modeling; Monkeys; Motor Cortex; Motor Skills; Movement; Network-based; Neurons; Order Coleoptera; Pattern; Performance; Population; Population Dynamics; Positioning Attribute; Primates; Process; Reaction Time; Recurrence; Research; Role; Science; Sleep; Sleep Architecture; Speed; Stress; Structure; System; Techniques; Testing; Time; Titrations; Training; Update; Upper limb movement; Variant; improved; improvement on sleep; insight; kinematics; memory consolidation; motor behavior; motor learning; motor skill learning; multi-electrode arrays; neural; non rapid eye movement; nonhuman primate; population based; role model; skill acquisition; skills; tool; touchscreen; wireless; wireless transmission

Project Summary/Abstract This project will provide a more nuanced and mechanistic model of the role of sleep in memory consolidation, particularly as it pertains to procedural motor skill acquisition in a non-human primate model. Motor skill learning delineated by enhanced speed, automaticity, and accuracy of a correlate strongly with the duration of non-REM (NREM) sleep. Neural reactivations of daytime neural activity preferentially occur during NREM, and disruptions in NREM sleep negatively impacts memory consolidation. Since neural reactivations are not perfect copies of daytime activity it is unclear what specific information about behavior and skill acquisition is being reactivated during sleep. Do reactivations reflect certain parts or kinematic variables of the motor behavior conducted during the day? Do changes in these reactivations predict certain features of future motor skill improvements? We will develop a model that parameterizes the relationship between reactivation and memory by measuring the dependence of motor skill learning on the number of reactivations, the fidelity of reactivations, and, most importantly, the decodability of these reactivations each night and over subsequent nights. That is, we will build decoding algorithms that accurately predict upper limb movements from neural activity during the day and then use these algorithms to identify if spiking that is specific to certain kinematic variables are preferentially reactivated. We will use the natural process of retrograde interference when a subject learns a second motor skill following the first skill at various inter-task intervals to manipulate reactivation and skill acquisition to more causally link reactivation to motor skill acquisition. Finally, our model will enhance the standard sleep-consolidation framework using network science based tools to identify circuit level changes: with a particular emphasis on higher order relationships between superficial and deep neurons that are predictive of motor skill learning. To do so we will use wireless neural recordings from motor cortex (M1) in unrestrained marmoset monkeys (Callithrix jacchus) will examine motor skill acquisition and sleep- induced memory consolidation of these skills. Multi-electrode arrays with multiple contacts in depth will allow us to systematically parameterize the interdependence of reactivations and network changes across cortical lamina in M1 with motor skill performance. In Aim 1, we will measure changes in M1 population dynamics across cortical lamina as monkeys engage in naturalistic and artificial motor skill acquisition tasks. In Aim 2, we will characterize reactivations of skill-related neuronal activity patterns in M1 during sleep with a focus on the behaviorally-relevant information content of these reactivations using population decoding methods and functional network techniques. Finally, in Aim 3, we will examine retrograde interference and sleep reactivation to naturally manipulate reactivation and skill acquisition. These aims will provide one of the first and most comprehensive examinations of the role of sleep-induced reactivations of behaviorally relevant multineuronal activity patterns in motor skill acquisition of the primate.

项目概要/摘要 该项目将为睡眠在记忆巩固中的作用提供一个更细致、更机械的模型， 特别是因为它涉及非人类灵长类动物模型中的程序性运动技能获取。运动技能 学习的速度、自动化程度和准确性的提高与学习的持续时间密切相关 非快速眼动 (NREM) 睡眠。白天神经活动的神经重新激活优先发生在 NREM 期间，并且 NREM 睡眠中断会对记忆巩固产生负面影响。由于神经重新激活并不完美 白天活动的副本尚不清楚有关行为和技能获取的具体信息是什么 睡眠时重新激活。重新激活是否反映了运动行为的某些部分或运动学变量 白天进行？这些重新激活的变化是否可以预测未来运动技能的某些特征 改进？我们将开发一个模型来参数化重新激活和记忆之间的关系 通过测量运动技能学习对重新激活次数的依赖性， 重新激活，最重要的是，每晚和随后的这些重新激活的可解码性 夜晚。也就是说，我们将构建解码算法，从神经网络中准确预测上肢运动。 白天的活动，然后使用这些算法来识别是否特定于某些运动学的尖峰 变量优先重新激活。当出现异常情况时，我们将使用逆行干扰的自然过程 受试者在各种任务间间隔学习第一个技能之后学习第二个运动技能来进行操作 重新激活和技能习得，将重新激活与运动技能习得更因果地联系起来。最后，我们的模型 将使用基于网络科学的工具来识别电路来增强标准睡眠巩固框架 水平变化：特别强调浅层和深层神经元之间的高阶关系 这是运动技能学习的预测。为此，我们将使用来自运动皮层的无线神经记录 （M1）不受约束的狨猴（Callithrix jacchus）将检查运动技能的获得和睡眠- 诱发这些技能的记忆巩固。具有多个深度接触的多电极阵列将允许 我们系统地参数化跨皮质的重新激活和网络变化的相互依赖性 M1 中的椎板具有运动技能表现。在目标 1 中，我们将测量 M1 种群动态的变化 当猴子从事自然主义和人工运动技能习得任务时，大脑皮层会发生变化。在目标 2 中，我们 将表征睡眠期间 M1 中与技能相关的神经元活动模式的重新激活，重点关注 使用群体解码方法和这些重新激活的行为相关信息内容 功能网络技术。最后，在目标 3 中，我们将研究逆行干扰和睡眠重新激活 自然地操纵重新激活和技能获取。这些目标将提供第一个也是最重要的目标之一 全面检查睡眠诱导的行为相关多神经元再激活的作用 灵长类动物运动技能习得的活动模式。