Sleep and neuroplasticity in complex training

该研究计划的主要目标是更深入地了解睡眠期间呈现的声音如何增强学习和记忆。2013年，Ngo等人证明，睡眠期间慢振荡（SO）的强度和纺锤波的密度可以通过精确定时的声音爆发来提高，这种技术被称为闭环听觉刺激（CLAS）。增强与第二天的内存性能改善有关。因此，CLAS提供了一种探测睡眠记忆功能的方法，但声音影响大脑活动的机制尚不清楚，它对现实生活中任务的影响（比迄今为止研究的简单范式复杂得多）也是未知的。我开发了一套独特的技能，适合理解这些过程：复杂训练任务的经验，CLAS，以及使用MEG对清醒期间和睡眠期间任务引起的振荡进行全脑源建模;这项工作将成为我新实验室研究方向的基础。我提出了三个不同的目标：目标一：闭环听觉刺激（CLAS）如何增强慢振荡（SO）？ 我们将在MEG中的NREM睡眠期间将噪声突发传递到SO的目标相位，以检验以下假设：i）从初级听觉皮层到次级区域的信号传播取决于正在进行的SO的相位;并且，使用源定位连接性分析，ii）第二阶段诱发反应和内源性皮质SO之间的关键相互作用发生在皮质与脑干中，通过以下过程：兴奋目的II：NREM睡眠中复杂任务巩固的神经相关性是什么？ 我们将建立一个睡眠依赖性记忆巩固在钢琴为基础的学习范式，我们以前开发的效果（即序列，定时精度），并使用EEG的唤醒任务的性能和睡眠巩固的时空特征。我们预测增加SO和主轴活动，这是成比例的第二天的任务改进。目标三：我们能用CLAS增强复杂学习吗？在我和II中获得的知识武装，我们将使用MEG的更高的空间分辨率来测试的假设，CLAS增强巩固在一个使用依赖的方式。我们假设，CLAS相比，没有刺激将增加SO振幅和纺锤体密度内的任务相关的脑区。由于CLAS在健康应用方面具有相当大的潜力，因此有必要深入了解声音影响内源性大脑活动和记忆的机制。我们的研究结果将与许多试图利用睡眠依赖性巩固的团体直接相关，并可能最终导致开发更好的治疗方法，以改善睡眠的认知益处，这些益处可能会因疾病和衰老而降低。我们的研究结果也将进一步加深我们对睡眠记忆功能的理解。

The principal goal of the research program is to gain a deeper understanding of how learning and memory can be enhanced by sound presented during sleep. In 2013, Ngo at al. demonstrated that the strength of slow oscillations (SOs) and the density of spindles during sleep could be boosted by precisely timed sound bursts, a technique known as closed-loop auditory stimulation (CLAS). Enhancement was related to memory performance improvements the following day. CLAS thus provides a means of probing the memory function of sleep, but the mechanism through which sound affects brain activity is not understood, and its effects on real-life tasks (which are far more complex than the simple paradigms studied to date) are unknown. I have developed a skill set that is uniquely suited to understanding these processes: experience with complex training tasks, CLAS, and whole-brain source modeling of oscillations induced by tasks during wakefulness and those during sleep, using MEG; this work will form the basis of my new lab's research direction. I propose three distinct aims: Aim I: How does closed-loop auditory stimulation (CLAS) enhance slow oscillations (SOs)?  We will deliver noise bursts during NREM sleep in the MEG to target phases of SOs, in order to test the hypotheses: i) that signal propagation from the primary auditory cortex to secondary regions depends on the phase of ongoing SOs; and, using source-localized connectivity analysis, ii) that the critical interaction between evoked responses and endogenous cortical SOs takes place in the cortex vs. in the brainstem via processes of arousal. Aim II: What are the neural correlates of complex task consolidation in NREM sleep?  We will establish an effect of sleep-dependent memory consolidation in a piano-based learning paradigm that we previously developed (i.e. on sequence, timing accuracy), and characterize spatiotemporal signatures of wake task performance and sleep consolidation using EEG. We predict an increase in SO and spindle activity which is proportional to next-day task improvement. Aim III: Can we enhance complex learning with CLAS? Armed with the knowledge gained in I and II, we will use MEG's higher spatial resolution to test the hypothesis that CLAS enhances consolidation in a use-dependent fashion. We hypothesize that CLAS as compared with no stimulation will increase SO amplitude and spindle density within task-related brain regions. Because CLAS has considerable potential for health applications, a deeper understanding of the mechanisms through which sound can influence endogenous brain activity and memory is necessary. Our results will be directly relevant to the many groups who are trying to harness sleep-dependent consolidation, and may eventually lead to the development of better therapies to the cognitive benefits of sleep that can be degraded by disease and aging. Our results will also further our understanding of the memory function of sleep.