Department of Biomedical Engineering, Columbia University, New York, United States.
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, United States.
Elife. 2024 Nov 18;13:RP99018. doi: 10.7554/eLife.99018.
Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (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.
大脑网络的大规模动态相互作用是人类认知过程的基础，但它们的电生理机制仍难以捉摸。三重网络模型包括突显网络（SN）、默认模式网络（DMN）和额顶网络（FPN），为理解这些相互作用提供了一个框架。我们分析了来自 177 名参与者的四个不同情景记忆实验的颅内脑电图（EEG）记录，每个实验都包括编码和回忆阶段。相位传递熵分析显示，来自前岛叶（AI）的定向信息流始终更高，AI 是 SN 的一个关键节点，流向 DMN 和 FPN 节点。与静息状态相比，在记忆任务期间这种定向影响明显更强，突出了 AI 在协调大规模网络相互作用方面的特定任务作用。这种模式在外部驱动的记忆编码和内部控制的自由回忆中都存在。使用下额前回（IFG）的控制分析显示出相反的模式，DMN 和 FPN 对 IFG 的影响更高，突出了 AI 的独特作用。我们观察到 DMN 的后扣带回/楔前叶节点在记忆编码期间出现高伽马功率的任务特异性抑制，但在回忆时没有。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复制，具有高贝叶斯复制因子。我们的发现提高了对协调神经网络相互作用如何支持记忆过程的理解，突出了 AI 在记忆编码和检索期间协调大规模大脑网络动力学方面的关键作用。通过阐明情景记忆中三重网络相互作用的电生理基础，我们的研究为记忆功能的神经回路动力学提供了深入了解，并为研究记忆相关障碍中的网络中断提供了框架。
