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

人类的认知功能与与任务相关的皮层区域的振荡变化以及与神经元同步相关的区域之间连通性的改变有关。磁图和脑电图（M/EEG）显示，不同频率的区域内和区域之间的大脑动态常常有显著差异，而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是如何在不同频率之间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，即“ATG （Alpha-Theta-Gamma）开关”，它反映了一种基本机制，通常涉及从“静息状态”或“待机模式”切换到与感觉知觉和认知相关的局部和任务相关功能网络的激活，代表了丘脑皮质和皮质-皮质回路中网络特性产生的振荡动力学的基本属性。

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.