Investigating the role of memory reactivation during REM sleep for plasticity

项目摘要 在清醒和睡眠期间都发生的记忆重新激活是一种主要的候选机制， 新的信息是如何整合形成长期记忆的。有影响力的理论提出互补 在非快速眼动（NREM）和快速眼动（REM）期间， 运动（REM）睡眠-NREM睡眠加强皮层网络中的新记忆（系统巩固） 快速眼动睡眠在局部网络中重组记忆（突触可塑性）。在过去的二十年里， 无数的研究有助于我们理解NREM睡眠和系统巩固。的作用 然而，快速眼动睡眠在很大程度上仍然是一个谜，可能是因为以前的工作还没有调整到检测快速眼动 睡眠依赖性记忆效应我们认为，快速眼动睡眠在促进表征中起着至关重要的作用， 改变，特别是通过减少相关记忆之间的神经重叠（分化），这减少了 在随后的记忆检索中竞争。我们将进行实验，辅之以神经 网络建模，以测试NREM和REM睡眠期间的竞争性学习和记忆再激活如何影响 记忆如何在表征空间中相互关联，以及它们在随后的过程中有多少竞争。 学习和检索。我们的第一个目标是进行实验，我们实验性地操纵 清醒学习期间的竞争。为了追踪学习过程中特定记忆重新激活的证据， 在随后的睡眠中，我们将“注入”可解码的信息到学习体验中，并使用机器学习 分类器来区分这些学习条件从睡眠脑电图大脑活动单独。我们将使用功能性磁共振成像 跟踪竞争动态，量化代表性变化，并展示行为 这种无差异的神经重叠的结果应该减少干扰并促进新的学习。 我们的第二个目标将使用计算模型来测试关于神经表征如何变化的预测 在NREM和REM睡眠期间，以及这些变化如何影响行为。该模型将使用Aim 1进行调整 实验数据，并将生成预测，通知目标3实验。我们的第三个目标是进行实验 我们在实验中偏向于NREM睡眠期间与学习相关的再激活内容。我们将研究 我们的NREM再激活操作如何在随后的REM睡眠中调节再激活，以及是否 快速眼动睡眠是这种非快速眼动操作对记忆结果产生影响所必需的。拟议 研究使用了最先进的方法的新组合，包括：（1）机器学习解码 睡眠EEG脑活动的记忆内容;（2）fMRI数据的多变量模式分析，以跟踪 表征变化;（3）表征变化敏感的行为测量;（4）神经网络 建模以识别NREM和REM相关可塑性的潜在机制;以及（5）基于NREM的 有针对性的记忆再激活，以测试NREM和REM睡眠期间的记忆处理如何相互作用。

Project Summary Memory reactivation, which occurs during both wakefulness and sleep, is a leading candidate mechanism for how new information is consolidated to form long-term memories. Influential theories propose complementary roles for how reactivation benefits consolidation during both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep—NREM sleep strengthens new memories in cortical networks (systems consolidation) and REM sleep reorganizes memories within local networks (synaptic plasticity). Over the last two decades, myriad studies have contributed to our understanding of NREM sleep and systems consolidation. The role of REM sleep, however, largely remains a mystery, possibly because prior work has not been tuned to detect REM sleep-dependent memory effects. We propose that REM sleep plays a crucial role in promoting representational change, specifically by decreasing neural overlap between related memories (differentiation), which reduces competition during subsequent memory retrievals. We will conduct experiments, complemented by neural network modeling, to test how competitive learning and memory reactivation during NREM and REM sleep affect how memories relate to each other in representational space and how much they compete during subsequent learning and retrieval. Our first aim is to run experiments where we experimentally manipulate levels of competition during wakeful learning. To track evidence of learning-specific memory reactivation during subsequent sleep, we will “inject” decodable information into the learning experience and use machine learning classifiers to distinguish these learning conditions from sleeping EEG brain activity alone. We will use fMRI to track competitive dynamics and quantify representational change, and demonstrate the behavioral consequences of this differentiation—less neural overlap should reduce interference and promote new learning. Our second aim will use computational models to test predictions about how neural representations change during NREM and REM sleep, and how these changes impact behavior. The model will be tuned using Aim 1 experimental data, and will generate predictions to inform Aim 3 experiments. Our third aim is to run experiments where we experimentally bias the learning-related content of reactivation during NREM sleep. We will examine how our NREM reactivation manipulation modulates reactivation during subsequent REM sleep, and whether REM sleep is necessary for this NREM manipulation to have an effect on memory outcomes. The proposed studies use a novel combination of state-of-the-art approaches, including: (1) machine learning to decode memory content from sleeping EEG brain activity; (2) multivariate pattern analysis of fMRI data to track representational change; (3) behavioral measures sensitive to representational change; (4) neural network modeling to identify the mechanisms underlying NREM- and REM-related plasticity; and (5) NREM-based targeted memory reactivation to test how memory processing during NREM and REM sleep interact.