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

人类认知功能与任务相关皮层区域内的振荡变化以及与神经元同步相关的那些区域之间的连接改变相关。磁图和脑电图（M/EEG）已经表明，区域内和区域之间的大脑动力学通常在频率上明显不同，并且大脑节律在广泛分离的频率范围内相互作用。然而，目前尚不清楚活动变化如何在频率之间协调以支持认知激活。我们最近引入了一个新的神经生理学框架，“ATG（α-θ-γ）开关”，反映了一种基本机制，通常涉及从“静止状态”或“待机模式”到与感官知觉和认知有关的局部和任务相关功能网络的激活的转换，代表由丘脑皮层和皮层-皮层回路中的网络特性引起的振荡动力学的基本属性。***为了详细了解这一框架的运作，现在有机会和需要推进数据分析战略，以量化和定位跨频率振荡和连通性。挑战在于，当前的方法在很大程度上基于传统的M/EEG源空间解决方案。这些最适合探索刺激或任务相关的活动，但容易产生源泄漏效应，并且可能不是网络和连接估计的最佳选择。因此，我们建议开发专门适用于这些情况的新技术。特别是，我们提出（1）推进多源空间滤波方法，因为它们独特的泄漏抑制性能;（2）开发一种新的概率算法，用于改善在超低信噪比条件下的源重建，基于多个源的操作统计集合，而不是单个源;（3）开发一种新型的空间滤波器，它是基于源连接措施。所有这三个目标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。其结果将是（i）协调变化的α，β，θ和γ频率将更好地研究网络的任务相关领域（ii）网络和连通性分析的新的先进算法将被开发，和（iii）这些算法将被交付给研究界在广大的形式公开可用的软件工具，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.