NCS-FO: Collaborative Research: Sleep's role in determining the fate of individual memories

确定认知、计算和神经机制是人类思维和大脑研究人员面临的众多重大挑战之一，这些机制决定了为什么一些记忆会在另一些记忆消失时存活下来。人们普遍认为，睡眠在长期记忆中起着关键作用，然而，睡眠中究竟发生了什么，从而影响了记忆的持久性，这一点在很大程度上还不清楚。这个项目汇集了一个研究团队，他们将整合认知神经科学、认知心理学和计算机科学中多个独立的工作领域，以调查最近形成的记忆表征在人睡觉时经历的精确机制，以及这些机制如何决定哪些记忆存活下来，哪些记忆消失。脑电和神经成像数据的尖端神经数据分析方法、基本人类记忆理论和神经网络建模的拟议集成使非侵入性地跟踪人类大脑中的个体记忆成为可能，因为它们相互竞争并在睡眠期间被修改。这项工作的潜在进展可能会影响教育、培训情况和公共健康，因为它促进了新策略的开发，以确保重要的记忆在最初学习后仍然存在。研究表明，记忆竞争神经空间，因此重新激活一种特定的记忆可能会对其他相关记忆造成“附带损害”。换句话说，访问一个存储器可能是以稍后能够访问网络空间中的其他附近存储器为代价的。拟议中的研究测试了这样一个假设，即重要性通过选择性地促进记忆重新激活来塑造睡眠期间的神经动力学；这种激活确保了重要记忆在睡眠期间击败了相关记忆，导致重要记忆得到加强，而不那么重要的记忆被削弱。为了验证这一假设，在睡眠期间，通过播放声音提示来引发记忆之间的竞争，每个声音提示都(在清醒时)链接到两个不同的图片位置记忆。多种相互关联的方法将跟踪睡眠期间的记忆竞争如何塑造记忆的持久性与衰退性。神经网络模型将被用来预测编码过程中的奖励反应如何影响睡眠中的竞争动态，以及这些竞争动态如何决定竞争记忆的最终命运。预测将通过使用fMRI来测量编码过程中与奖励处理相关的神经活动，使用EEG来测量睡眠期间的大脑活动，以及使用模式分类器来解码睡眠EEG数据中的记忆激活来进行测试。观察睡眠中的竞争动态将与后来的记忆表现和记忆变化的多变量功能磁共振测量相关。该项目有可能首次提供对记忆获取、睡眠过程中的处理以及最终回忆过程的全面了解。我们将获得关于编码时奖赏加工的差异如何影响睡眠重放动态，以及睡眠重放动态如何影响后续记忆性能和神经表征结构的关键知识。

Identifying the cognitive, computational and neural mechanisms responsible for determining why some memories survive when others fade is one of the many grand challenges facing researchers of the human mind and brain. It is widely understood that sleep plays a critical role in long-term remembering, yet what exactly happens during sleep to affect the persistence of memories remains largely unknown. This project brings together a team of researchers who will integrate multiple independent lines of work in cognitive neuroscience, cognitive psychology, and computer science in order to investigate the precise mechanisms undergone by recently-formed memory representations as a person sleeps and how these mechanisms determine which memories survive and which fade. The proposed integration of cutting-edge neural data analysis methods for EEG and neuroimaging data, basic human memory theory, and neural network modeling make possible the ability to non-invasively track individual memories in the human brain as they compete with each other and are modified during sleep. The potential advances from this work could impact education, training situations, and public health by facilitating the development of new strategies for ensuring that important memories survive after initial learning.Research suggests that memories compete for neural space such that reactivating one particular memory can exert "collateral damage" on other related memories. In other words, accessing one memory can come at the expense of later being able to access other nearby memories in the network space. The proposed studies test the hypothesis that importance shapes neural dynamics during sleep by selectively boosting memory reactivation; this boost ensures that important memories out-compete related memories during sleep, resulting in strengthening of important memories and weakening of less-important memories. To test this hypothesis, competition between memories will be elicited during sleep by playing sound cues, each of which was linked (during wake) to two different picture-location memories. Multiple interlocking approaches will track how memory competition during sleep shapes a memory's persistence versus fading. Neural network models will be used to generate predictions about how reward responses during encoding shape competitive dynamics during sleep, and how these competitive dynamics determine the eventual fates of competing memories. Predictions will be tested by using fMRI to measure neural activity associated with reward processing during encoding, EEG to measure brain activity during sleep, and pattern classifiers to decode memory activation from the sleep EEG data. Observations of competitive dynamics during sleep will then be related to later memory performance and to multivariate fMRI measures of memory change. The project has the potential to provide, for the first time, a comprehensive look "under the hood" at the life of a memory as it is acquired, processed during sleep, and eventually recalled. Pivotal knowledge will be gained about how variance in reward processing at encoding influences sleep replay dynamics, and about how sleep replay dynamics affect subsequent memory performance and the structure of neural representations.