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

量化和定位局部和大规模脑网络的交叉频率振荡动力学和连接性

• 批准号: RGPIN-2018-06692
• 负责人: Ribary, Urs
• 金额: $ 2.11万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683220
• 关键词: Quantifying; localizing; cross; frequency; oscillation

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.

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。******为了详细了解该框架的功能，现在有机会和需要推进数据分析策略，以量化和定位交叉频率振荡和连通性。挑战在于，当前的方法严重依赖于传统的M/EEG源空间解决方案。这些方法最适合于探索与刺激或任务相关的活动，但容易受到源泄漏效应的影响，并且可能不是网络和连接估计的最佳方法。因此，我们建议开发专门适合这些情况的新技术。特别是，我们提出(1)推进多源空间滤波方法，因为它们具有独特的泄漏抑制性能；(2)开发了一种新的概率算法，以提高超低信噪比条件下的信号源重建，该算法基于多个信号源的统计集合而不是单个信号源；(3)开发了一种基于源连通性测度的新型空间滤波器。这三个目标将通过计算机模拟和真实的脑电数据进行验证。对于每一种方法，我们都从初步研究中获得了积极的结果。******结果将是(i)在任务相关领域的网络中将更好地研究α、β、θ和γ频率的协调变化（ii）将开发用于网络和连接性分析的新颖先进算法，以及（iii）这些算法将以公开可用的软件工具的形式交付给整个研究界，以进行广泛的实际应用。