Real-time control of memory encoding

内存编码实时控制

• 批准号: 9977812
• 负责人: Megan Teresa deBettencourt
• 金额: $ 6.93万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977812
• 关键词: Acquaintances; Automobile Driving; Behavior; Brain; Clinical; Contralateral; Coupling; Cues; Data; Data Collection; Detection; Electroencephalography; Event; Event-Related Potentials; Failure; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Goals; Intervention; Laboratories; Link; Location; Maintenance; Measures; Memory; Mental disorders; Modeling; Monitor; Names; Occupational; Participant; Performance; Reaction Time; Reporting; Research; Rewards; Role; Shapes; Short-Term Memory; Signal Transduction; Stimulus; Sum; System; Testing; Time; Visual; Volition; base; brain computer interface; experimental study; improved; lens; long term memory; memory encoding; memory retention; nervous system disorder; neurofeedback; neurotransmission; relating to nervous system; stimulus interval; success; temporal measurement; time use; visual information

Project Summary Fluctuations of neural activity impact memory. Subsequent memory analyses have demonstrated that particular neural states predict better working memory and long-term memory behavior. These analyses are typically conducted after data collection, but monitoring neural fluctuations in real time would enable more direct and timely interventions. We will use real-time electroencephalography (EEG) to track moment-to- moment fluctuations of neural activity in order to more directly link brain signals with behavior, and to enhance memory performance. In this proposal, we focus on two key moments for memories: pre-stimulus (Aim 1) and active maintenance during a retention interval (Aim 2). In Aim 1, we will test the hypothesis that pre-stimulus neural signals (oscillatory alpha and theta) predict memory encoding success. In Experiment 1, we will vary the point of time when the stimuli appear based on real-time calculations of alpha and theta power. We will use neural activity as the independent variable to “trigger” stimulus presentation when the brain is in either advantageous states (low alpha, high theta) or disadvantageous states (high alpha, low theta). We predict that better brain states will predict better working memory and long-term memory precision in a sensitive continuous report task. In Experiment 2, we will provide neurofeedback to reward advantageous pre-stimulus brain states (low alpha, high theta). We predict that up-regulating these advantageous states will lead to enhanced memory performance (more precise memories). In Aim 2, we will test the hypothesis that sustained activity tracks active maintenance of information. Sustained activity is a key signature of working memory, but recent evidence has questioned its role through the demonstration of activity-silent working memory. In Experiment 3, we will use real-time measures of sustained activity (contralateral delay activity, multivariate alpha topography) to adjust the duration of a retention interval and the identity of working memory probes. We predict that performance will be better (quicker reaction times, more precise memories) when memory probes are triggered based on higher sustained activity. In Experiment 4, we will provide neurofeedback during the retention interval to reward greater sustained activity. We predict that up-regulating these advantageous states will lead to greater memory precision. Across these experiments, we will explore memory encoding via the lens of real-time EEG to trigger information (Experiments 1 & 3) and provide feedback (Experiments 2 & 4). The proposed research will characterize the fate of mnemonic representations by tracking and driving neural activity both pre-encoding (Aim 1) and post-encoding (Aim 2), in order to understand how neural fluctuations give rise to what we remember.

项目摘要 神经活动的波动会影响记忆。随后的记忆分析表明， 特定的神经状态预示着更好的工作记忆和长期记忆行为。这些分析 通常在数据收集后进行，但在真实的时间监测神经波动将使更多的 直接和及时的干预。我们将使用实时脑电图（EEG）来跟踪 为了更直接地将大脑信号与行为联系起来， 内存性能在这个建议中，我们关注记忆的两个关键时刻：刺激前（目标1）和 在保留间隔期间进行主动维护（目标2）。在目标1中，我们将检验预刺激 神经信号（振荡的α和θ）预测记忆编码的成功。在实验1中，我们将改变 基于α和θ功率的实时计算，刺激出现的时间点。我们将使用 神经活动作为自变量，以“触发”刺激呈现，当大脑处于 有利状态（低α，高θ）或不利状态（高α，低θ）。我们预测 更好的大脑状态将预测更好的工作记忆和长期记忆精度在一个敏感的 连续报告任务。在实验2中，我们将提供神经反馈来奖励有利的前刺激 大脑状态（低α，高θ）。我们预测，上调这些有利状态将导致 增强的记忆性能（更精确的记忆）。在目标2中，我们将测试持续的假设， 活动跟踪信息的主动维护。持续的活动是工作记忆的一个关键特征， 最近的证据通过活动沉默工作记忆的展示对其作用提出了质疑。在 实验3，我们将使用持续活动的实时测量（对侧延迟活动，多变量 阿尔法地形图）来调整保持间隔的持续时间和工作记忆探针的身份。我们 预测当内存探测时，性能会更好（更快的反应时间，更精确的记忆） 都是基于更高的持续活动而触发的。在实验4中，我们将提供神经反馈， 保持间隔，以奖励更大的持续活动。我们预测，上调这些有利状态 将导致更高的记忆精度。在这些实验中，我们将通过透镜探索记忆编码 的实时EEG触发信息（实验1和3）和提供反馈（实验2和4）。的 拟议中的研究将通过跟踪和驱动神经元， 活动编码前（目的1）和编码后（目的2），以了解神经波动如何 让我们想起了什么