Investigating the role of memory reactivation during REM sleep for plasticity

研究快速眼动睡眠期间记忆重新激活对可塑性的作用

• 批准号: 10192124
• 负责人: Elizabeth Ann McDevitt
• 金额: $ 11.39万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10192124
• 关键词: Address; Affect; Basic Science; Behavior; Behavioral; Brain; Classification; Clinical; Complement; Computer Models; Data; Electroencephalography; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Human; Individual; Influentials; Intervention; Learning; Machine Learning; Measurement; Measures; Mediating; Memory; Memory impairment; Modeling; Modification; Multivariate Analysis; Neural Network Simulation; Neuronal Plasticity; Outcome; Pattern; Phase; Physiological; Play; Population; Process; Public Health; REM Sleep; Research; Retrieval; Rodent; Role; Running; Shapes; Sleep; Sleep Disorders; Sleep Stages; Sleep disturbances; Synaptic plasticity; System; Techniques; Testing; Training; United States; Wakefulness; Work; base; behavior measurement; behavioral study; career; data modeling; experience; experimental study; improved; innovation; learning classifier; long term memory; memory process; memory retention; memory retrieval; nervous system disorder; neural patterning; neuropathology; non rapid eye movement; novel; rapid eye movement; relating to nervous system; repaired; sleep health; theories

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.

项目总结