Circuit dynamics of structuring episodic memories in humans

构建人类情景记忆的电路动力学

• 批准号: 10590796
• 负责人: Jie Zheng
• 金额: $ 11.89万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10590796
• 关键词: Address; Advanced Development; Area; Behavior; Behavioral; Brain; Cells; Clinical; Clip; Cognitive; Computer Models; Data; Detection; Disease; Electric Stimulation; Electrodes; Episodic memory; Euclidean Space; Event; Failure; Feedback; Goals; Hippocampus (Brain); Hour; Human; Image; Impairment; Implanted Electrodes; Influentials; Lead; Lesion; Light; Link; Memory; Memory Disorders; Memory impairment; Mentors; Microscopic; Midbrain structure; Modeling; Monitor; Neurons; Neurosciences; Outcome; Outcome Study; Patients; Phase; Population; Prefrontal Cortex; Process; Property; Proxy; Research; Retrieval; Rewards; Role; Seizures; Series; Shapes; Signal Transduction; Structure; Substantia nigra structure; System; Technical Expertise; Testing; Theoretical model; Therapeutic Intervention; Time; Training; Update; Work; behavior measurement; computational neuroscience; deep brain stimulator; deviant; dopaminergic neuron; experience; high dimensionality; implantation; insight; memory recognition; movie; multisensory; neural prosthesis; neuromechanism; novel; relating to nervous system; response; sensory input

Project summary Our lives unfold over time, weaving rich, dynamic, and multisensory information into a continuous experience. However, we remember this as a series of discrete events. For example, the memory of a two-hour movie consists of a few memorable moments tied to the main story. During encoding, we segment deviant events and associate relevant events. During retrieval, we utilize the temporal association among encoded events to search for specific memory information. The hippocampus (HPC), prefrontal cortex (PFC) and substantia nigra (SN) are thought to support cognitive computations (i.e., conceptual prediction, prediction error detection, temporal association) that are critical for the encoding and retrieval of episodic memory. However, how these three regions work together to facilitate the construction of episodic memory in humans remains unclear. The proposed study aims to address this by identifying neural dynamics in the HPC-PFC-SN network and revealing circuit-level neural mechanisms of event segmentation and its relationship with human episodic memory. The central hypothesis is that event segmentation, which is influential in episodic memory formation and retrieval, emerges from the difference between the HPC perceptual predictions and received sensory inputs, which is tracked by dopaminergic neurons in the SN to update event models stored in the PFC. To test this, I will record both single neuron activity and local field potential signals in the HPC-PFC-SN network while patients, who have depth electrodes implanted for clinical purposes, encode, and retrieve the memory of semi-realistic experience created by well-controlled video clips. I will also build a computational model that can rigorously reproduce the observed behavioral and neural signatures. The trained model will be used as a proxy of the HPC-PFC-SN network to study the causal link between this tripartite network and memory behaviors by simulating computational “lesions”, which will provide insightful guidance for real electrical stimulation. To achieve the proposed goals, I will pursue training mentored by a group of experts in different fields, including the intraoperative recordings (Dr. Ziv Williams and Dr. Adam Mamelak), analyses of inter-regional neural dynamics and electrical stimulation (Dr. Ueli Rutishauser), and computational modeling (Dr. Gabriel Kreiman). The comprehensive analytic approaches spinning from behavior measurements, invasive neural recordings from both microscopic and mesoscopic levels, computational modeling and electrical stimulation will provide valuable opportunities to strengthen our understanding of human episodic memory system. The expected outcomes of this proposal will potentially advance the development of therapeutic interventions for memory-related disorders.

项目概要 我们的生活随着时间的推移而展开，将丰富、动态和多感官的信息编织成持续的体验。 然而，我们记得这是一系列离散事件。例如，两小时电影的记忆 由一些与主要故事相关的令人难忘的时刻组成。在编码过程中，我们对异常事件进行分段并 关联相关事件。在检索过程中，我们利用编码事件之间的时间关联来搜索 获取特定的内存信息。海马体 (HPC)、前额皮质 (PFC) 和黑质 (SN) 是 被认为支持认知计算（即概念预测、预测错误检测、时间 关联）对于情景记忆的编码和检索至关重要。然而，这三个地区如何 共同努力促进人类情景记忆的构建仍不清楚。拟议的研究 旨在通过识别 HPC-PFC-SN 网络中的神经动力学并揭示电路级神经网络来解决这个问题 事件分割机制及其与人类情景记忆的关系。 中心假设是事件分割对情景记忆的形成和检索有影响， 来自 HPC 感知预测和接收到的感官输入之间的差异，即 由 SN 中的多巴胺能神经元跟踪，以更新存储在 PFC 中的事件模型。为了测试这一点，我将记录 HPC-PFC-SN 网络中的单个神经元活动和局部场电位信号，而患者， 用于临床目的植入的深度电极，编码并检索半现实经验的记忆 由精心控制的视频剪辑创建。我还将建立一个计算模型，可以严格重现 观察到的行为和神经特征。训练后的模型将用作 HPC-PFC-SN 的代理 网络通过模拟来研究这个三方网络和记忆行为之间的因果关系 计算“损伤”，将为真正的电刺激提供富有洞察力的指导。 为了实现拟议的目标，我将接受由不同领域的专家组指导的培训，包括 术中记录（Ziv Williams 博士和 Adam Mamelak 博士）、区域间神经分析 动力学和电刺激（Ueli Rutishauser 博士），以及计算建模（Gabriel Kreiman 博士）。 综合分析方法源自行为测量、侵入性神经记录 无论是微观还是介观层面，计算建模和电刺激都将提供有价值的信息 加强我们对人类情景记忆系统的理解的机会。预期成果 该提案将有可能促进记忆相关疾病治疗干预措施的发展。