Hippocampal and prefrontal contributions to memory integration

海马和前额叶对记忆整合的贡献

• 批准号: 9050708
• 负责人: Alison R Preston
• 金额: $ 38.08万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-17 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9050708
• 关键词: Accounting; Address; Algorithms; Behavior; Behavioral; Biological Neural Networks; Brain; Code; Cognitive; Data; Development; Event; Foundations; Functional Magnetic Resonance Imaging; Health; Hippocampus (Brain); Human; Individual; Influentials; Intervention; Judgment; Knowledge; Lead; Learning; Link; Maintenance; Measures; Medial; Mediating; Memory; Mental disorders; Neurobiology; Neurons; Nodal; Pattern; Play; Population; Prefrontal Cortex; Process; Recruitment Activity; Research; Resolution; Retrieval; Role; Signal Transduction; Temporal Lobe; Testing; Therapeutic; Update; Vertebral column; Work; adjudicate; base; benefit sharing; effective intervention; effective therapy; experience; flexibility; improved; nervous system disorder; neuroimaging; novel; relating to nervous system; response; success; theories

DESCRIPTION (provided by applicant): Leading memory theories emphasize that new learning occurs on the background of existing knowledge. Retrieving prior knowledge during new experiences allows new information to be integrated into existing memories, resulting in the formation of rich, cohesive memory networks that relate discrete events. This integration process is proposed to facilitate new learning and enable memories to extend beyond direct experience to anticipate the relationships among events. However, memory for evidence neurobiological inability to directly measure the contents of reactivated memories during new experiences. To address this critical gap, the proposed studies employ a new experimental paradigm that uses highly sensitive pattern classifier algorithms applied to functional neuroimaging data to quantify incidental memory reactivation during new event encoding. Quantifying memory reactivation allows us to test mechanistic predictions about how past memories influence learning in the present. Aim 1 will use this paradigm to test the hypothesis that hippocampus and ventromedial prefrontal cortex (VMPFC) work in concert to support memory integration during new learning. We propose that by linking new information with well-established memories, this hippocampal-VMPFC mediated encoding process improves new learning and enables novel judgments about relationships among distinct events. Aim 2 will examine how temporal context and memory strength influence the formation of integrated memory traces for related events. We propose that learning overlapping events within the same temporal context facilitates memory integration by enhancing memory reactivation and recruiting hippocampal-VMPFC encoding processes. We will also adjudicate between opposing theoretical perspectives of learning that make competing predictions for whether strong or weak memories lead to enhanced memory integration. Aim 3 will use high- resolution fMRI focused on the medial temporal lobe to determine the precise hippocampal computations and coding strategies that underlie memory integration. We will determine the relationship between memory reactivation and hippocampal mismatch responses that signal differences between current events and existing memories to test the hypothesis that mismatch responses trigger memory integration. We will also use pattern- information analysis to test the hypothesis that the hippocampus creates integrated memories by forming overlapping neural codes for related events. Collectively, this work will determine how internally generated content influences new learning and will isolate the precise neural networks, computations, and coding strategies that underlie memory integration. Understanding how the brain uses prior experience to make sense of new information will lay the foundation for translational work onintegration and its functional significance is sorely lacking due primarily to an effective for interventions therapeutic psychiatric and neurological disorders that require acquisition and maintenance of new behaviors.

描述（由申请人提供）：领先的记忆理论强调，新学习发生在现有知识的背景下。在新体验期间检索先验知识可以使新信息集成到现有的记忆中，从而形成了与离散事件相关的丰富，有凝聚力的内存网络。提出了这种集成过程，以促进新的学习，并使记忆超越直接经验，以预测事件之间的关系。然而，记忆的证据神经生物学无法直接测量新体验中重新激活的记忆的内容。为了解决这一关键差距，提出的研究采用了一种新的实验范式，该范式使用应用于功能性神经成像数据的高度敏感的模式分类器算法来量化新事件编码期间的偶然记忆重新激活。量化记忆重新激活使我们能够测试有关过去记忆如何影响目前学习的机理预测。 AIM 1将使用此范式来检验以下假设：海马和腹侧前额叶皮层（VMPFC）协同工作，以支持新学习过程中的内存整合。我们建议，通过将新信息与已建立的记忆联系起来，这种海马-VMPFC介导的编码过程改善了新的学习，并实现了有关不同事件之间关系的新颖判断。 AIM 2将检查时间上下文和记忆力如何影响相关事件的集成记忆痕迹的形成。我们建议在同一时间上下文中学习重叠事件，通过增强内存重新激活并招募海马-VMPFC编码过程来促进内存的集成。我们还将在对立的学习理论观点之间进行裁决，从而对强烈的记忆或弱记忆进行竞争的预测会导致记忆积分的增强。 AIM 3将使用专注于内侧颞叶的高分辨率fMRI来确定基于内存集成的精确海马计算和编码策略。我们将确定记忆重新激活与海马不匹配响应之间的关系，这些响应信号在当前事件和现有内存之间存在差异，以测试不匹配响应触发内存整合的假设。我们还将使用模式信息分析来检验以下假设：海马通过形成相关事件重叠的神经代码来创建集成的记忆。总的来说，这项工作将决定内部生成的内容如何影响新的学习，并将隔离基于内存集成的精确神经网络，计算和编码策略。了解大脑如何利用先前的经验来理解新信息将为转化工作建立构成基础，其功能意义主要是由于需要有效的干预措施治疗精神病和神经系统疾病，因此缺乏其功能意义。