Quantifying and localizing cross-frequency oscillation dynamics and connectivity across local and large-scale brain networks

人类的认知功能与任务相关的皮质区域内的振荡变化以及与神经元同步相关的这些区域之间的连接性变化有关。脑磁图和脑电图仪(M/EEG)表明，不同频率的区域内和不同区域之间的大脑动力学往往有显著差异，并且大脑节律在相距很远的频率范围内相互作用。然而，目前尚不清楚活动变化是如何跨频率协调以支持认知激活的。我们最近引入了一个新的神经生理学框架，“ATG(Alpha-Theta-Gamma)Switch”，反映了一种基本的机制，它通常意味着从“静息状态”或“待机模式”切换到激活与感觉和认知相关的局部和任务相关功能网络，这代表了丘脑和皮质环路中的网络特性所产生的振荡动力学的基本属性。*要详细了解这个框架的功能，现在有机会也需要推进数据分析策略，以量化和定位跨频率振荡和连接。挑战在于，目前的方法在很大程度上是基于传统的M/EEG源空间解决方案。这些方法最适合探索与刺激或任务相关的活动，但容易产生源泄漏效应，对于网络和连通性估计可能不是最佳的。因此，我们建议开发专门适合这些情况的新技术。具体而言，我们提出了(1)改进多源空间滤波方法，因为它们具有独特的泄漏抑制特性；(2)开发了一种新的概率算法，用于改善超低信噪比条件下的源重构，基于多个源而不是单个源的统计集成；(3)开发了一种基于源连通性度量的新型空间滤波器。所有这三个目标都将通过计算机模拟和真实的M/EEG数据进行验证。对于这些方法中的每一种，我们都从初步研究中获得了积极的结果。*结果将是：(1)将在任务相关领域的网络中更好地研究阿尔法、贝塔、西塔和伽马频率的协调变化；(2)将开发用于网络和连通性分析的新的先进算法；以及(3)这些算法将以公开可用的软件工具的形式提供给广大研究界，以供广泛的实际应用。

Human cognitive functions are associated with oscillatory changes within task-relevant cortical regions, as well as alterations in connectivity between those regions associated with neuronal synchronization. Magneto- and Electroencephalography (M/EEG) have shown that brain dynamics within and between regions often differ markedly across frequencies, and that brain rhythms interact across widely separated frequency ranges. It remains unclear, however, how activity changes are coordinated across frequencies to support cognitive activation. We recently introduced a new neurophysiological framework, the "ATG (Alpha-Theta-Gamma) switch", reflecting a basic mechanism that generally implicates a switch from “resting state" or "stand-by mode” to the activation of local and task-related functional networks relating to sensory perception and cognition, representing a fundamental attribute of oscillatory dynamics that arises from network properties in thalamocortical and cortico-cortical circuits.******For detailed understanding of functioning of this framework, there is now an opportunity and need to advance data analysis strategies for quantifying and localizing cross-frequency oscillations and connectivity. The challenge is that current methods are heavily based on traditional M/EEG source-space solutions. Those work best for exploring stimuli- or task-related activity, but are prone to source leakage effects and may be not optimal for network and connectivity estimation. Therefore, we propose developing new techniques specifically suited for these situations. In particular, we propose (1) advancing multi-source spatial filtering methods because of their unique leakage suppression properties; (2) developing a new probabilistic algorithm for improving source reconstruction in ultra-low signal-to-noise ratio conditions, based on operating statistical ensembles of multiple sources rather than individual ones; (3) developing a new type of spatial filters which are based on source connectivity measures. All three objectives will be verified by computer simulations and real M/EEG data. For each of these approaches we have positive results from preliminary studies.******The outcome will be (i) that coordinated changes in alpha, beta, theta and gamma frequencies will be better investigated in networks of task-relevant areas (ii) novel advanced algorithms for network and connectivity analyses will be developed, and (iii) these algorithms will be delivered to the research community at large in the form of publicly available software tools for wide practical application.