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情景记忆形成和回忆过程中突显、默认模式及额顶叶网络的电生理动力学:一项多实验颅内脑电图复制研究

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication.

作者信息

Das Anup, Menon Vinod

机构信息

Department of Biomedical Engineering, Columbia University, New York, NY 10027.

Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305.

出版信息

bioRxiv. 2024 Sep 23:2024.02.28.582593. doi: 10.1101/2024.02.28.582593.

Abstract

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

摘要

大规模脑网络之间的动态相互作用支撑着人类认知过程,但其电生理机制仍不清楚。包含突显网络(SN)、默认模式网络(DMN)和额顶网络(FPN)的三重网络模型为理解这些相互作用提供了一个框架。我们分析了177名参与者在四个不同的情景记忆实验中的颅内脑电图记录,每个实验都包括编码和回忆阶段。相位转移熵分析显示,从关键的SN节点前脑岛(AI)到DMN和FPN节点的定向信息流始终更高。与静息状态相比,这种定向影响在记忆任务期间明显更强,突出了AI在协调大规模网络相互作用中的特定任务作用。这种模式在外部驱动的记忆编码和内部主导的自由回忆中都持续存在。使用额下回(IFG)的对照分析显示出相反的模式,即DMN和FPN对IFG的影响更大,这突出了AI的独特作用。我们观察到在记忆编码期间,DMN的后扣带回皮质/楔前叶节点的高伽马功率受到特定任务的抑制,但在回忆期间没有。至关重要的是,这些结果在跨越言语和空间记忆领域的所有四个实验中都得到了重复,且具有高贝叶斯重复因子。我们的研究结果推进了对协调的神经网络相互作用如何支持记忆过程的理解,突出了AI在记忆编码和检索过程中协调大规模脑网络动态方面的关键作用。通过阐明情景记忆中三重网络相互作用的电生理基础,我们的研究深入了解了记忆功能背后的神经回路动态,并为研究记忆相关障碍中的网络破坏提供了一个框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b8/11423045/5b4cb5f4bb4c/nihpp-2024.02.28.582593v4-f0001.jpg

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