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Decoding memory reprocessing during sleep

睡眠期间解码内存重新处理

基本信息

批准号：
403173771
负责人：
Professorin Dr. Monika Schönauer
金额：
--
依托单位：
Abteilung Neuropsychologie
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2018
资助国家：
德国
起止时间：
2017-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/403173771?language=en
关键词：
Decoding memory reprocessing during sleep

项目摘要

After encoding, new memories have to be transformed to last for the long-term. During the course of this consolidation, the neural substrate supporting new memories shifts from hippocampal to neocortical regions, a process that has been termed systems memory consolidation. Sleep has been proposed to play an important role in this transition. By a reactivation of learning-related neuronal activity, which emanates from the hippocampus and spreads across the cortex, information may be transferred to neural systems that are less plastic than the network that initially encoded the memory, but consequently offer more stable memory storage. Until recently, it has been difficult to study the covert processes that support memory consolidation during sleep in humans. With the introduction of machine learning methods to cognitive neuroscience, it has become possible to detect the hidden patterns in brain activity that reflect processing of learning-related information. In the proposed project, I will examine in more detail the properties of reactivation as a key mechanism underlying the beneficial effects of sleep on memory. Together with my host Prof. Kenneth Norman of the Princeton Computational Memory Lab, who is expert in multivariate pattern analysis, I will develop machine learning approaches to decode memory reprocessing during sleep. A main objective will be to identify promising data transformations that can be used as features for pattern classification analysis. These should keep nuisance variance between individuals and recording sessions minimal, while maximizing the information related to the content of memory processing. These methods will then be applied to study the properties of both spontaneous memory reactivation and targeted memory reactivation using external reactivation cues. In particular, we will (1) investigate oscillatory activity that contributes to memory consolidation, (2) examine at what times of the night different regions of the sleeping brain process learning-related information, and (3) describe how different regions in the sleeping brain interact when processing learning-related information. Our results can help to clarify the covert processes the lead to stable memory storage in the human brain. By identifying which oscillatory events are involved in reprocessing mnemonic material and how this activity interacts across different brain regions, we can get a better understanding of how sleep contributes to systems memory consolidation. In the future, this may help to effectively target these mechanisms to facilitate long-term memory formation.

在编码之后，新的记忆必须被转化才能长期存在。在这个巩固过程中，支持新记忆的神经基质从海马转移到新皮层区域，这一过程被称为系统记忆巩固。睡眠被认为在这一转变中起着重要作用。通过重新激活与学习相关的神经元活动，信息可以被传递到神经系统，这些神经系统的可塑性比最初编码记忆的网络要小，但因此提供了更稳定的记忆存储。直到最近，研究人类睡眠期间支持记忆巩固的内隐过程一直很困难。随着机器学习方法在认知神经科学中的引入，已经可以检测到大脑活动中反映学习相关信息处理的隐藏模式。在提议的项目中，我将更详细地研究重新激活的特性，作为睡眠对记忆有益影响的关键机制。我将与普林斯顿计算记忆实验室的Kenneth Norman教授一起开发机器学习方法来解码睡眠期间的记忆再处理。一个主要目标将是确定有前途的数据转换，可用作模式分类分析的功能。这些应该保持个体和记录会话之间的滋扰差异最小，同时最大限度地增加与记忆处理内容相关的信息。然后，这些方法将被应用到研究的性质，自发的记忆再激活和有针对性的记忆再激活使用外部的再激活线索。特别是，我们将（1）研究有助于记忆巩固的振荡活动，（2）研究睡眠中大脑的不同区域在夜间的什么时间处理与学习相关的信息，（3）描述睡眠中大脑的不同区域在处理与学习相关的信息时如何相互作用。我们的研究结果有助于阐明人脑中导致稳定记忆存储的内隐过程。通过识别哪些振荡事件参与了记忆材料的再加工，以及这种活动如何在不同的大脑区域之间相互作用，我们可以更好地了解睡眠如何有助于系统记忆巩固。未来，这可能有助于有效地针对这些机制，以促进长期记忆的形成。