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: 9894860
• 负责人: Anna Jafarpour
• 金额: $ 9.62万
• 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/9894860
• 关键词: Address; Age; Algorithms; Behavior; Behavioral; Bilateral; Brain; Brain region; Buffaloes; California; Communication; Comprehension; Computer Models; Coupling; Data; Data Analyses; Dependence; Detection; Development; Electrodes; Electroencephalography; Electrophysiology (science); Elements; Epilepsy; Episodic memory; Event; Frequencies; Goals; Grouping; Hippocampus (Brain); Human; Impairment; Laboratories; Lead; Lesion; Link; Memory; Memory impairment; Mentors; Mentorship; Methodology; Modeling; Monitor; Natural Language Processing; Operative Surgical Procedures; Participant; Patients; Pattern; Phase; Play; Prefrontal Cortex; Process; Research; Retrieval; Role; Seizures; Signal Transduction; Sister; Speech; Stimulus; Techniques; Therapeutic Intervention; Training; Universities; Validation; Washington; Work; advanced analytics; deviant; encephalography; experience; indexing; insight; neural circuit; neural prosthesis; neurodevelopment; neuromechanism; nonhuman primate; programs; relating to nervous system; skills

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 these neural dynamics using intracranial encephalography (iEEG) acquired from the hippocampus and PFC of epileptic patients, who have electrodes implemented for pre-surgical seizure monitoring. I will model associations of words using Natural Language Processing algorithms, and I will combine the extracted features with advanced data analysis techniques including multivariate pattern analysis to determine neural dynamics engaged during encoding. I will use speech as a model with an identical flow of events in the speech stimuli across participants. This consistency will allow validation of effects across a group of participants. Algorithms for identifying features of speech are well developed and freely available. I will specifically use elements of speech that distinguish context, word dependencies, and reference points of pronouns for modeling concurrent changes in patterns of activity in the local field potential recorded from the hippocampus and PFC. 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. To achieve the proposal’s goals, I will pursue training under the mentorship of Dr. Elizabeth Buffalo (University of Washington) that will focus on the advanced analysis of local field potentials. The advanced study of human iEEG data will include comparable electrophysiology signal analyses that have been applied to the recordings from the hippocampus of non-human primates in Buffalo’s memory lab. This skill-set along with ongoing mentoring from Dr. Robert Knight (University of California, Berkeley), who has an established laboratory for human iEEG, and my previous work on human iEEG will provide a vigorous methodological, conceptual, and analytical basis for developing an independent research program. The combination of iEEG, Natural Language Processing 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.

我们体验世界是一个连续的事件序列，但我们记忆中的事件是分段的 插曲(例如，我姐姐的婚礼)。在编码过程中，我们将一系列相关事件和片段关联起来 不正常的事件。在检索时，情节记忆利用编码的关联来重放事件流。这个 经过编码的联想能够更好地记住一集内发生的事件的顺序 跨段的事件流。海马体和前额叶皮质(PFC)是 神经回路，用于分割、链接和检索相关事件的记忆。这项建议旨在 识别支持编码自然事件流的海马体-PFC回路中的神经动力学，即， 单词的顺序。 我们将使用从脑部获取的脑电图(IEEG)来确定这些神经动力学 有电极用于术前癫痫发作的癫痫患者的海马区和PFC 监控。我将使用自然语言处理算法对单词的关联进行建模，我将结合 利用先进的数据分析技术提取的特征，包括多变量模式分析 确定编码过程中涉及的神经动力学。我将使用语音作为模型，具有相同的流 受试者的言语刺激中的事件。这种一致性将允许在整个组中验证效果 参与者的数量。用于识别语音特征的算法已经开发得很好，并且可以免费获得。这就做 特别是使用区分上下文、单词从属关系和 代词用于模拟从记录的局部场势中的活动模式的同时变化 海马体和PFC。中心假设是海马区之间的双向通讯 和PFC支持事件序列的编码和成功的后续记忆。解决一个因果关系 海马区功能与事件分割的关系，我将研究言语理解和 发育性遗忘症患者海马区受损并有困难的言语记忆 跟踪演讲中的参考点。 为了实现提案的目标，我将在伊丽莎白·布法罗博士(大学)的指导下进行培训 这将侧重于对当地场势的高级分析。关于人类的高级研究 IEEG数据将包括已应用于记录的可比电生理信号分析 来自布法罗记忆实验室中非人类灵长类动物的海马体。此技能集以及正在进行的 来自罗伯特·奈特博士(加州大学伯克利分校)的指导，他拥有成熟的实验室 人类iEEG，以及我以前对人类iEEG所做的工作将提供一种强有力的方法论、概念性和 制定独立研究计划的分析基础。 IEEG、自然语言处理建模和患者行为数据的组合将提供 对预测后续记忆的有效语音编码的神经动力学的有价值的见解，这 可将发展信息转化为治疗干预措施。