Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
J Neurosci. 2019 Aug 21;39(34):6728-6736. doi: 10.1523/JNEUROSCI.2798-18.2019. Epub 2019 Jun 24.
Retrieval of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, noninvasive, content-specific indicators of memory retrieval remains a central challenge. Here, we attempted to decode the content of retrieved memories in the EEG during sleep. During encoding, male and female human subjects learned to associate spatial locations of visual objects with left- or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left- and right-hand movements before bringing subjects back for a final postnap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left- vs right-sided trials during wake) to predict learning content when we cued the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that retrieval leads to memory stabilization. Moreover, these lateralized signals increased with postcue sleep spindle power, demonstrating that retrieval has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline retrieval. Memories are thought to be retrieved during sleep, leading to their long-term stabilization. However, there has been relatively little work in humans linking neural measures of retrieval of individual memories during sleep to subsequent memory performance. This work leverages the prominent electrophysiological signal triggered by lateralized movements to robustly demonstrate the retrieval of specific cued memories during sleep. Moreover, these signals predict subsequent memory and are correlated with sleep spindles, neural oscillations that have previously been implicated in memory stabilization. Together, these findings link memory retrieval to stabilization and provide a powerful tool for investigating memory in a wide range of learning contexts and human populations.
检索与学习相关的神经活动模式被认为可以促进记忆的稳定。然而，找到可靠的、非侵入性的、特定于内容的记忆检索指标仍然是一个核心挑战。在这里，我们试图在睡眠期间的 EEG 中解码检索到的记忆的内容。在编码过程中，男性和女性人类受试者学会将视觉物体的空间位置与左手或右手运动联系起来，并且每个物体都伴随着内在相关的声音。在下午小睡期间的慢波睡眠中，我们呈现了一半与左手和右手运动相关的声音提示（在清醒时），然后让受试者回来进行最后的小睡后测试。我们在睡眠 EEG 数据（专注于区分清醒时左右侧试验的侧化 EEG 特征）上训练了一个分类器，以在睡眠期间提示记忆时预测学习内容。辨别性能明显高于随机水平，并预测了随后的记忆，支持了检索导致记忆稳定的想法。此外，这些侧化信号随着记忆后睡眠纺锤波功率的增加而增加，表明检索与纺锤波密切相关。这些结果表明，可以从睡眠 EEG 中解码与个体记忆相关的侧化活动，为离线检索提供有效的指标。人们认为记忆是在睡眠中检索的，从而导致其长期稳定。然而，在人类中，将睡眠期间个体记忆检索的神经测量与随后的记忆表现联系起来的工作相对较少。这项工作利用了由侧化运动引发的显著的电生理信号，有力地证明了在睡眠中检索特定提示的记忆。此外，这些信号预测了随后的记忆，并与睡眠纺锤波相关，先前的研究表明，睡眠纺锤波与记忆稳定有关。这些发现将记忆检索与稳定联系起来，并为在广泛的学习环境和人类群体中研究记忆提供了一个强大的工具。
