Circuit Dynamics for encoding and remembering sequence of events

用于编码和记忆事件序列的电路动力学

• 批准号: 10375038
• 负责人: Anna Jafarpour
• 金额: $ 7.87万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10375038
• 关键词: Address; Affect; Algorithms; Behavior; Behavioral; Brain; Communication; Comprehension; Data; Data Analyses; Data Collection; Development; Electrodes; Electrophysiology (science); Epilepsy; Episodic memory; Event; Hippocampus (Brain); Hospitalization; Human; Human Subject Research; Lead; Lesion; Link; Medical Staff; Memory; Memory impairment; Modeling; Monitor; Natural Language Processing; Operative Surgical Procedures; Patients; Phase; Prefrontal Cortex; Research; Research Personnel; Retrieval; Seizures; Sister; Speech; Techniques; Therapeutic Intervention; Training; Vaccination; advanced analytics; coronavirus disease; deviant; encephalography; experience; experimental study; insight; neural circuit; neural prosthesis; neurodevelopment; patient screening; programs; relating to nervous system

Project Summary We experience the world as a continuous sequence of events, but we remember the events as segmented episodes (e.g., my sister’s wedding). During encoding, we associate a sequence of relevant events and segment deviant events. At retrieval, episodic memory utilizes the encoded associations to replay the flow of events. The encoded associations lead to remembering the sequence of events that occurred within an episode better than the flow of events across segments. The hippocampus and the prefrontal cortices (PFC) are essential parts of the neural circuit for segmenting, linking and retrieving memories of associated events. This proposal aims to identify neural dynamics in the hippocampus-PFC circuit that support encoding a naturalistic flow of events, i.e., sequences of words. We will determine the neural dynamics using intracranial encephalography (iEEG) acquired from the hippocampus and PFC of epileptic patients, who have electrodes implemented for pre-surgical seizure monitoring. Our experiment requires patients to listen to a narrative and later recall the flow of events. During the past year, I developed a Natural Language Processing (NLP) algorithm that quantifies the associations of words depending on the narrative context. I will use NLP model for annotation of the iEEG data to determine neural dynamics engaged during encoding sequences of words. Our research program (mainly data collection), like all other human subject research, has been significantly affected by the COVID mitigation efforts. We were not able to collect the required iEEG data to fulfill the aims of the planned K99 phase during the past year. With the vaccination of researchers and medical staff and screening of patients before the hospitalization, our data collection program is reopening with considerations. The extension to the K99 will allow us to catch up with the data collection that is required for the planned training on advanced data analysis. The central hypotheses are that bidirectional communications between the hippocampus and PFC support the encoding of sequences of events and successful subsequent memory. To address a causal relationship between hippocampal function and event segmentation, I will study speech comprehension and speech memory in developmental amnesic patients who suffer from hippocampal damage and have trouble tracking reference points in a speech. The combination of iEEG, NLP modeling, and patients’ behavioral data will provide valuable insights into the neural dynamics of effective speech encoding that predicts subsequent memory, which may inform development into therapeutic interventions.

项目摘要 我们将世界视为一系列事件，但我们记得这些事件 分段情节（例如，我姐姐的婚礼）。在编码期间，我们将一系列相关的顺序关联 事件和细分市场事件。在检索时，情节内存利用编码的关联到 重播事件的流程。编码的关联导致记住事件的顺序 发生在一集中比各个细分市场的事件流动更好。海马和 前额叶皮层（PFC）是用于分割，链接和检索的神经回路的重要部分 相关事件的记忆。 该建议旨在确定支持编码A的海马PFC电路中的神经动力学 事件的自然流动，即单词序列。我们将使用 从海马和PFC获得癫痫患者的颅内脑造影（IEEG）， 具有用于手术前癫痫发作监测的电极。我们的实验要求患者 听叙事，后来回想起事件的流动。在过去的一年中，我开发了一个自然的 语言处理（NLP）算法，该算法量化了单词的关联 叙事背景。我将使用NLP模型来注释IEEG数据来确定神经动力学 在编码单词序列中参与。 与所有其他人类学科研究一样，我们的研究计划（主要是数据收集）一直是 受到相互作用缓解工作的影响很大。我们无法收集所需的IEEG数据 在过去一年中实现计划中的K99阶段的目标。与研究人员和 医务人员和住院前患者的筛查，我们的数据收集计划是 重新开放考虑因素。 K99的扩展将使我们能够赶上数据收集 这是对高级数据分析的计划培训所必需的。 中心假设是海马和PFC之间的双向通信 支持事件序列和成功后续内存的编码。解决因果关系 海马功能与事件细分之间的关​​系，我将研究语音理解 在发育性健忘症患者中遭受海马损害并具有的语音记忆 在演讲中跟踪参考点的麻烦。 IEEG，NLP建模和患者的组合 行为数据将为有效语音编码的神经动态提供宝贵的见解 预测随后的记忆，这可能会为治疗干预措施的发展提供信息。