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.

项目总结/摘要 这个项目将提供一个更微妙和机械的模型，睡眠在记忆巩固中的作用， 特别是当其涉及非人灵长类动物模型中的程序性运动技能获得时。运动技能 通过增强的速度，自动性和准确性描述的学习与持续时间密切相关。 非快速眼动睡眠（NREM）白天神经活动的神经再激活优先发生在NREM期间， NREM睡眠的中断会对记忆巩固产生负面影响。因为神经激活并不完美 白天活动的副本，目前还不清楚是什么具体的信息，行为和技能的获得是 在睡眠中重新激活。再激活是否反映了运动行为的某些部分或运动学变量 在白天进行？这些重新激活的变化是否预示着未来运动技能的某些特征 改进？我们将开发一个模型，参数化之间的关系重新激活和记忆 通过测量运动技能学习对再激活次数的依赖性， 重新激活，最重要的是，这些重新激活的解码能力，每天晚上和随后的 个晚上.也就是说，我们将构建解码算法，从神经网络准确预测上肢运动 活动，然后使用这些算法来确定是否特定于某些运动学 变量优先被重新激活。我们将使用逆行干扰的自然过程， 受试者在不同的任务间间隔学习第一技能之后的第二运动技能以操纵 重新激活和技能获得，以更因果地将重新激活与运动技能获得联系起来。最后，我们的模型 将使用基于网络科学的工具来识别电路， 水平变化：特别强调表层和深层神经元之间的高阶关系 可以预测运动技能学习。为此，我们将使用来自运动皮层的无线神经记录 (M1)在无拘无束的绒猴（Callithrix jacchus）将检查运动技能的获得和睡眠- 诱导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 我们系统地参数化的相互依赖性的再激活和网络的变化，在皮层 M1板层与运动技能表现。在目标1中，我们将测量M1种群动态的变化 当猴子从事自然和人工运动技能获取任务时，在目标2中， 将描述睡眠期间M1中与技能相关的神经元活动模式的重新激活，重点关注 使用群体解码方法的这些再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再激活 来自然地操纵重新激活和技能获取。这些目标将提供一个第一和最 对睡眠诱导的行为相关多神经元再激活作用的综合研究 灵长类动物获得运动技能的活动模式。