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: 9753679
• 负责人: Anna Jafarpour
• 金额: $ 10.35万
• 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 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753679
• 关键词: Address; Age; Algorithms; Behavior; Behavioral; Bilateral; Brain; Brain region; Buffaloes; California; Communication; Comprehension; Computer Simulation; 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; 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.

我们对世界的体验是一系列连续的事件，但我们对这些事件的记忆是分段的 情节（例如，妹妹的婚礼）。在编码过程中，我们将一系列相关事件和片段 异常事件在提取时，情景记忆利用编码的关联来重放事件流。的 编码的关联导致记忆一个事件中发生的事件的顺序， 跨部门的事件流。海马体和前额叶皮质（PFC）是大脑的重要组成部分， 神经回路用于分割、连接和检索相关事件的记忆。这项建议旨在 识别支持对事件的自然流进行编码的前额叶皮层-前额叶皮层回路中的神经动力学，即， 单词的序列。 我们将使用从脑内获得的颅内脑电图（iEEG）来确定这些神经动力学。 癫痫患者的海马和PFC，其具有用于手术前癫痫发作的电极 监测.我将使用自然语言处理算法对单词的关联进行建模，并将联合收割机 提取的特征与先进的数据分析技术，包括多元模式分析， 确定编码过程中的神经动力学。我将用言语作为一个模型， 参与者之间的言语刺激事件。这种一致性将允许验证整个组的效果 的参与者。用于识别语音特征的算法已经得到了很好的开发，并且可以免费获得。我会 特别是使用语音元素，区分上下文，单词依赖性和参考点， 代词，用于模拟从记录的局部场电位中的活动模式的并发变化。 海马和PFC。中心假设是海马之间的双向通信， 和PFC支持事件序列的编码和成功的后续记忆。为了解决因果关系， 海马功能和事件分割之间的关系，我将研究语音理解和 海马损伤并有困难的发展性遗忘症患者的言语记忆 跟踪语音中的参考点。 为了实现这个计划的目标，我将在伊丽莎白布法罗博士（大学）的指导下接受培训 的华盛顿），将侧重于当地的领域潜力的先进分析。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 从布法罗记忆实验室的非人类灵长类动物的海马体中提取的。这种技能组合沿着正在进行的 罗伯特·奈特博士（加州大学伯克利分校）的指导，他拥有一个成熟的实验室， 人类iEEG，和我以前对人类iEEG的工作将提供一个有力的方法，概念， 为开发独立的研究计划提供分析基础。 iEEG、自然语言处理建模和患者行为数据的结合将提供 对有效语音编码的神经动力学的有价值的见解，预测随后的记忆， 可以为治疗干预的发展提供信息。