Hippocampal and prefrontal contributions to memory integration

海马和前额叶对记忆整合的贡献

• 批准号: 9261395
• 负责人: Alison R Preston
• 金额: $ 38.04万
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9261395
• 关键词: Address; Algorithms; Behavior; Behavioral; Biological Neural Networks; Brain; Code; Cognitive; Data; Development; Event; Foundations; Functional Magnetic Resonance Imaging; 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 Intervention; Update; Vertebral column; Work; adjudicate; base; benefit sharing; cohesion; effective intervention; effective therapy; experience; flexibility; improved; nervous system disorder; neuroimaging; novel; public health relevance; 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，新的学习发生在现有知识的背景。在新的经历中检索先前的知识，可以将新的信息整合到现有的记忆中，从而形成丰富的、有凝聚力的记忆网络，将离散的事件联系起来。这种整合过程被提出来促进新的学习，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的内容重新激活的记忆在新的经验。为了解决这一关键差距，拟议的研究采用了一种新的实验范式，使用高度敏感的模式分类器算法应用于功能性神经影像数据，以量化新事件编码过程中的偶然记忆再激活。量化记忆的重新激活使我们能够测试关于过去记忆如何影响现在学习的机械预测。目的1将使用这一范式来检验海马和腹内侧前额叶皮层（VMPFC）协同工作，以支持新的学习记忆整合的假设。我们认为，通过将新信息与完善的记忆联系起来，这种由campal-VMPFC介导的编码过程可以改善新的学习，并对不同事件之间的关系做出新的判断。目标2将研究时间背景和记忆强度如何影响相关事件的整合记忆痕迹的形成。我们建议，在相同的时间背景下，学习重叠的事件，促进记忆整合，通过增强记忆的再激活和招聘的campal-VMPFC编码过程。我们还将在对立的学习理论观点之间做出裁决，这些观点对强记忆或弱记忆是否会导致记忆整合的增强做出相互竞争的预测。目标3将使用高分辨率功能磁共振成像集中在内侧颞叶，以确定精确的海马计算和编码策略，记忆整合的基础。我们将确定记忆再激活和海马失配反应之间的关系，即当前事件和现有记忆之间的信号差异，以检验失配反应触发记忆整合的假设。我们也将使用模式信息分析来检验海马体通过为相关事件形成重叠的神经代码来创造整合记忆的假设。总的来说，这项工作将确定内部生成的内容如何影响新的学习，并将隔离作为记忆整合基础的精确神经网络，计算和编码策略。理解大脑如何利用先前的经验来理解新的信息将为整合的翻译工作奠定基础，其功能意义严重缺乏，主要是由于需要获得和维持新行为的治疗性精神和神经疾病的干预措施有效。