Causal roles of neural synchrony in signal transmission and cognition in the human brain

健康的认知依赖于我们通过预期、先前的经验和内在目标影响外界感官事件处理的能力。在大脑中，这反映在较高阶额叶和顶叶区域之间微调的相互作用中，这些区域通过影响“自下而上”驱动的较低水平的感觉区域，对感觉处理进行“自上而下”的认知控制。虽然构成自上而下控制的大脑区域在解剖学上有很好的定义，但这些大脑区域和感觉区域之间的双向交流是如何组织的仍然是一个悬而未决的问题。在动物模型中，时间相关的神经元活动，即神经元同步，被提出用来协调解剖分布的处理，并调节不同频率通道中感觉信息前馈流动的反馈控制。然而，在人脑中只有很少的证据支持这一假说，需要多模式神经成像技术，既可以绘制大规模大脑网络的全脑图，也可以研究它们的因果作用。我们将使用最先进的多模式神经成像结合磁/脑电(MEG-EEG)和经颅磁刺激/脑电(TMS-EEG)来揭示网络相互作用在实现自下而上(视觉)信号的内部控制调节中的机制/因果作用。我们将确定注意额叶和顶叶大脑区域通过相互作用影响视觉区域神经元活动的兴奋性和时机，以根据预期和与当前行为目标的相关性选择信息。我们的总体目标是研究这些自上而下的相互作用是如何在人脑中实现的，并开发新的人脑网络完整性的探针。利用对自上而下控制任务中获得的脑磁图数据的尖端分析技术，我们将揭示与视觉加工的反馈控制相关的大规模网络相互作用的动态性质和皮层来源，以及它们对视觉皮质活动的局部影响。基于这些从脑磁图获得的个体动态网络，我们将测试单脉冲TMS非侵入性脑刺激诱导的活动如何作为被刺激区域及其主要频率的函数在大脑中传播。为此，我们将使用同步脑电记录(TMS-EEG)和相同的尖端分析框架来测量MEG-EEG中的网络交互，从而允许在同一参与者(MEG-EEG-TMS)中离线整合这些方法。最后，我们将在自然频率下使用有节奏的TM来模拟自上而下的效果。使用同步EEG并通过与MEG-EEG数据集成的方式，我们将测试TMS仿真的自上而下效应在多大程度上依赖于网络中目标大脑区域的重要性(其“中枢”)和状态依赖。总而言之，我们的项目将揭示全脑相关(MEG-EEG)和机械学(TMS-EEG)对大规模神经同步在实施前馈信号和视觉处理的自上而下控制中的功能意义的洞察。该项目还有望展示MEG/EEG引导的TMS在提高重复TMS疗效方面的效用，重复TMS广泛应用于认知和临床神经科学。揭示TMS疗效是否依赖于个体参与者的网络参数对其在实验和临床环境中的使用具有重要意义。总而言之，该项目将有助于确定大脑网络相互作用的基本电生理构件，并推动研究和调节这些过程的工具。

Healthy cognition depends on our capacity to influence the processing of external sensory events from our environment by expectations, previous experience and internal goals. In the brain, this is reflected in finely tuned interactions between higher-order frontal and parietal areas that take "top-down" cognitive control over sensory processing via influencing "bottom-up"-driven lower-level sensory regions. While brain regions that underlie top-down control are well defined anatomically, it is still an open question how the bidirectional communication between these brain regions and sensory areas is organized. In animal models, temporally correlated neuronal activity, i.e., neuronal synchronization, has been proposed to coordinate anatomically distributed processing and to regulate feedback control over the feedforward flow of sensory information in different frequency channels. However, there is only scarce evidence to support this hypothesis in the human brain and there is a need for multimodal neuroimaging techniques that would allow both whole brain mapping of large-scale brain networks and studying their causal role. We will use state-of-the art multimodal neuroimaging with combined magneto/ electroencephalography (MEG-EEG) and combined transcranial magnetic stimulation/ electroencephalography (TMS-EEG) to unravel the mechanistic / causal role of network interactions in achieving the internally controlled regulation of bottom-up (visual) signalling. We will identify interactions by which the attentional frontal and parietal brain areas influence the excitability and timing of neuronal activity in visual areas for the selection of information in accordance with expectations and relevance for current behavioural goals. Our overarching aim is to study how these top-down interactions are achieved in the human brain and to develop new probes of network integrity in the human brain.Exploiting cutting-edge analyses techniques for MEG-EEG data acquired during tasks engaging top-down control, we will uncover the dynamic nature and cortical sources of the large-scale network interactions that are correlated with feedback control of visual processing, and their local consequences on visual cortex activity. Based on these individual dynamic networks obtained from MEG-EEG, we will test how activity induced by non-invasive brain stimulation with single-pulse TMS propagates through the brain as a function of areas being stimulated and its dominant frequency. To this end, we will use simultaneous EEG recordings (TMS-EEG) and the same cutting-edge analysis framework for measuring network interactions in MEG-EEG, allowing to integrate these approaches offline in the same participants (MEG-EEG-TMS). Finally, we will use rhythmic TMS at natural frequencies to emulate top-down effects. Using simultaneous EEG and by means of integration with the MEG-EEG data, we will test to what extent the TMS-emulated top-down effects depend on the importance of the targeted brain region in the network (its "hubness") and are state-dependent. Collectively, our project will reveal both whole-brain correlative (MEG-EEG) and mechanistic (TMS-EEG) insight into the functional significance of large-scale neural synchronization in implementing top-down control of feedforward signalling and visual processing. The project is also expected to demonstrate the utility of MEG/EEG-guided TMS for improving the efficacy of repetitive TMS, which is widely used in cognitive and clinical neuroscience. Revealing whether TMS efficacy depends on network parameters in individual participants has important implications for its use in experimental and clinical settings. Altogether, this project will therefore help to identify the basic electrophysiological building blocks of brain network interactions, and to advance the tools for studying and modulating these processes.