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.

项目总结