Multi-scale brain network mechanisms of working memory and short-term memory

短时记忆(STM)和工作记忆(WM)是核心的认知过程，其容量因个体而异，也因试验而异。短时记忆指的是感觉信息的在线保持，工作记忆(WM)指的是对该信息的操纵。STM和WM由多个组成部分组成，例如，感觉信息的持续维持及其注意和执行控制。这些功能背后的神经元处理是平行的，并分布在大脑解剖结构中，形成功能模块化的皮质网络，其中神经元活动的特点是神经元在许多频率上同时振荡。关键的挑战是解决什么机制将这种解剖和时间分布的加工整合到主观上连贯的STM和WM中，区分这两种记忆功能并限制它们的容量。尽管存在明显的相关性，但协调和整合这种分布式处理以及设定STM和WM容量限制的系统级神经元底物却知之甚少。在这个项目中，我们将使用最先进的多模式神经成像结合磁/脑电(M/EEG)和联合经颅磁刺激(TMS)-EEG，并利用对复杂脑网络的尖端分析来建立健康受试者在STM和WM中维持信息的系统级神经元机制。我们的首要目标是揭示STM和WM的多尺度脑振荡网络机制。频段内的神经元相位同步(PS)被提出用来协调解剖分布的处理。我们将结合M/EEG、先进的源连通性分析和我们首创的网络理论来识别大脑皮层范围的PS网络及其在STM和WM中的作用。我们的第一个目标是利用尖端来源连通性分析来建立多尺度大规模网络同步可能是一种整合的系统水平的机制，以协调STM和WM中分布在解剖上的神经元回路的处理，并解决这些振荡网络相互作用的复杂性是否区分STM和WM。频率内PS不能协调也分布在不同频率的多个振荡网络上的神经元处理。我们已经提出并提供了跨振荡频率同步的初步证据。跨频率同步(CFS)服务于跨振荡和跨大脑皮层层次的整合。我们的第二个目标是建立在STM和WM期间分布在不同频率的神经元处理通过CFS整合。我们认为，通过在振荡频率上连接PS网络，CFS可以为不同认知过程的整合奠定基础。由于M/EEG只为同步协调行为表现的功能意义提供了相关证据，我们将通过TMS-EEG的组合获得因果证据，这是我们团队率先使用的。利用独特的M/EEG网络分析引导节律TM(RhTMS)来刺激网络中枢和引导同步，我们的目标是调节记忆性能。这将因此能够解决多尺度网络同步在协调STM和WM性能方面的机械作用。这个项目将发现分布式神经元处理如何整合到主观连贯的STM和WM中。这将进一步为网络同步在人类记忆性能中的重要性提供第一次因果证据。该项目具有弥合神经生理学和心理学之间的鸿沟的开创性潜力。由于STM和WM缺陷是许多大脑疾病的特征，这项工作将为疾病机制提供新的见解，并为记忆障碍的新疗法铺平道路。

Short-term memory (STM) and working memory (WM) are core cognitive processes with a limited capacity varying across individuals and from trial-to-trial. STM refers to online retention of sensory information and working memory (WM) to the manipulation of that information. STM and WM are comprised of multiple components e.g. sustained maintenance of sensory information, and its attentional and executive control. Neuronal processing underlying these functions is parallel and distributed across brain anatomy into functionally modular cortical networks, where neuronal activity is characterized by neuronal oscillations concurrently in many frequencies. The key challenge is to resolve what mechanisms integrates this both anatomically and temporally distributed processing into subjectively coherent STM and WM, differentiate these two memory functions and limit their capacity. Despite the obvious relevance, the systems-level neuronal substrates coordinating and integrating this distributed processing and setting the capacity limits of STM and WM are poorly understood. In this project, we will use state-of-the art multimodal neuroimaging with combined magneto/ electroencephalography (M/EEG) and combined transcranial magnetic stimulation (TMS)-EEG and exploit cutting-edge analyses of complex brain networks to establish the systems-level neuronal mechanisms underlying the maintenance of information in STM and WM in healthy human subjects. Our overarching goal is to reveal the multi-scale brain oscillatory network mechanisms of STM and WM. Neuronal phase synchronization (PS) within frequency bands has been proposed to coordinate anatomically distributed processing. We will use combined M/EEG with advanced source-connectivity analyses and network theory pioneered by us to identify cortex-wide PS networks and their role in STM and WM. Our first goal is to use cutting-edge source connectivity analysis to establish that multi-scale large-scale network synchronization could be an integrative systems-level mechanism for coordinating processing across anatomically distributed neuronal circuits in STM and WM and resolve whether complexity of these oscillatory network interactions differentiate STM and WM. Within-frequency PS cannot coordinate neuronal processing that is also distributed across multiple oscillatory networks at distinct frequencies. We have proposed and provided initial evidence for that synchronization across oscillatory frequencies, a.k.a. cross-frequency synchronization (CFS), serves integration across oscillations and across cortical hierarchies. Our second goal is to establish that neuronal processing distributed across frequencies during STM and WM are integrated via CFS. We propose that by connecting PS networks across oscillatory frequencies, CFS could underlie the integration of distinct cognitive processes. As M/EEG yield only correlative evidence for the functional significance of synchronization coordinating behavioral performance, we will obtain causal evidence with combined TMS-EEG, the usage of which our team has pioneered. Employing unique M/EEG network analyses guided rhythmic TMS (rhTMS) to stimulate network hubs and to entrain synchronization, we aim to modulate memory performance. This will thus enable resolving the mechanistic role of multi-scale network synchronization in coordinating STM and WM performance. This project will discover how distributed neuronal processing is integrated into subjectively coherent STM and WM. This will further yield first-time causal evidence for significance of network synchronization in human memory performance. This project has a groundbreaking potential to bridge the gap between neurophysiology and psychology. As STM and WM deficits characterize many brain diseases, this work will give novel insights into the disease mechanisms and pave the way for novel treatments for memory disorders.