Circuit Dynamics for encoding and remembering sequence of events

项目摘要 我们把世界看作一个连续的事件序列，但我们记忆中的事件是 分段插曲(例如，我姐姐的婚礼)。在编码过程中，我们将相关的序列 事件和分段异常事件。在检索时，情节记忆利用编码的关联来 重播事件流。编码的关联导致记住事件的顺序， 发生在一集内的事件比跨片断的事件流更好。海马体和 前额叶皮质(PFC)是神经回路的重要组成部分，用于分割、连接和提取 对相关事件的记忆。 这项建议旨在确定在海马体-PFC回路中支持编码 自然的事件流，即单词的序列。我们将用来确定神经动力学 从癫痫患者的海马区和PFC获得的颅内脑电图(IEEG)，谁是 安装电极用于术前癫痫监测。我们的实验要求患者 听一段叙述，然后回忆事件的流程。在过去的一年里，我开发了一种天然的 语言处理(NLP)算法，它根据 叙事语境。我将使用NLP模型对iEEG数据进行注释，以确定神经动力学 在对单词序列进行编码时使用。 我们的研究计划(主要是数据收集)，像所有其他人类主题研究一样，一直是 受到COVID缓解努力的严重影响。我们无法收集所需的iEEG数据 在过去的一年中完成计划中的K99阶段的目标。在研究人员和研究人员的接种下 医护人员在入院前对患者进行筛查，我们的数据收集方案是 带着考虑重新开张。K99的扩展将使我们能够赶上数据收集 这是计划中的高级数据分析培训所必需的。 中心假设是海马区和前额叶核之间的双向通讯 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 发育性遗忘症患者海马区受损并有 在演讲中跟踪参照点时遇到困难。IEEG、NLP建模和患者模型的结合 行为数据将为有效语音编码的神经动力学提供有价值的见解 预测随后的记忆，这可能会为治疗干预提供信息。

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.