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

健康的认知取决于我们通过期望、先前的经验和内部目标来影响外部感官事件的处理的能力。在大脑中，这反映在高级额叶和顶叶区域之间的精细调节的相互作用中，通过影响“自下而上”驱动的低级感觉区域，对感觉处理进行“自上而下”的认知控制。虽然在解剖学上，自上而下控制的大脑区域已经得到了很好的定义，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，神经元同步已经被提出来协调解剖学上的分布式处理，并调节对不同频率通道中的感觉信息的前馈流的反馈控制。然而，只有很少的证据来支持这一假设在人类大脑中，有一个多模态神经成像技术，将允许两个大规模的大脑网络的全脑映射和研究其因果关系的作用的需要。我们将使用最先进的多模态神经成像与组合磁/脑电图（MEG-EEG）和组合经颅磁刺激/脑电图（TMS-EEG）来解开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。我们将确定的相互作用，注意额叶和顶叶脑区影响的兴奋性和时间的视觉区域的神经元活动的信息选择，根据预期和相关性，为当前的行为目标。我们的首要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探针。我们将利用尖端的分析技术对自上而下控制任务中获得的MEG-EEG数据进行分析，揭示与视觉加工反馈控制相关的大规模网络相互作用的动态本质和皮层来源，以及它们对视觉皮层活动的局部影响。基于从MEG-EEG获得的这些个体动态网络，我们将测试使用单脉冲TMS的非侵入性脑刺激诱导的活动如何作为受刺激区域及其主频的函数在大脑中传播。为此，我们将使用同步EEG记录（TMS-EEG）和相同的尖端分析框架来测量MEG-EEG中的网络相互作用，允许在相同的参与者（MEG-EEG-TMS）中离线集成这些方法。最后，我们将在自然频率下使用有节奏的TMS来模拟自上而下的效果。使用同步EEG并通过与MEG-EEG数据的整合，我们将测试TMS模拟的自上而下效应在多大程度上取决于网络中目标大脑区域的重要性（其“中心”），并且是状态依赖的。总的来说，我们的项目将揭示全脑相关（MEG-EEG）和机械（TMS-EEG）洞察大规模神经同步在实现前馈信号和视觉处理的自上而下控制中的功能意义。该项目还有望证明MEG/EEG引导的TMS在提高重复TMS疗效方面的实用性，重复TMS广泛应用于认知和临床神经科学。揭示TMS疗效是否取决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。总之，该项目将有助于识别大脑网络相互作用的基本电生理构建模块，并推进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.