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: 10708113
• 负责人: Jie Zheng
• 金额: $ 11.56万
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10708113
• 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; Hour; Human; Image; Impairment; Implanted Electrodes; Influentials; Lead; Learning; Lesion; 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; member; memory recognition; movie; multisensory; neural; neural prosthesis; neuromechanism; novel; 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.

项目摘要 随着时间的流逝，我们的生活不断发展，将丰富，动态和多感官信息编织成连续的体验。 但是，我们记得这是一系列离散事件。例如，两小时电影的记忆 由与主要故事相关的一些令人难忘的时刻组成。在编码期间，我们分割了偏差事件和 相关事件。在检索期间，我们利用编码事件之间的临时关联进行搜索 有关特定的内存信息。海马（HPC），前额叶皮层（PFC）和黑质Nigra（SN）为 被认为支持认知计算（即概念预测，预测错误检测，临时 关联）对于情节记忆的编码和检索至关重要。但是，这三个地区如何 共同努力以促进人类情节记忆的构建尚不清楚。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元来解决这一问题 事件细分的机制及其与人类情节记忆的关系。 中心假设是事件分割，在情节记忆形成和检索中具有影响力 从HPC感知预测和接收的感觉输入之间的差异中出现 在SN中由多巴胺能神经元跟踪，以更新PFC中存储的事件模型。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现场潜在信号，而患者 为临床目的植入的深度电极，编码和检索半现实体验的记忆 由控制良好的视频剪辑创建。我还将建立一个可以严格复制的计算模型 观察到的行为和神经信号。训练有素的模型将用作HPC-PFC-SN的代理 通过模拟来研究该三方网络与记忆行为之间的因果关系的网络 计算“病变”，它将为真实的电刺激提供有见地的指导。 为了实现拟议的目标，我将接受一群不同领域的专家，包括 术中记录（Ziv Williams博士和Adam Mamelak博士），区域间神经的分析 动力学和电刺激（Ueli Rutishauser博士）和计算建模（Gabriel Kreiman博士）。 从行为测量，侵入性神经记录中旋转的全面分析方法 显微镜和介绍水平，计算建模和电刺激都将提供值 加强我们对人类情节记忆系统的理解的机会。预期的结果 该建议将有可能提高与记忆有关疾病的理论干预措施的发展。