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: $ 4.23万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764123
• 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质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通性变化有关。磁性和脑电图（M/EEG）表明，在频率之间和区域之间和区域之间的大脑动力学通常不同，并且大脑节奏在广泛分离的频率范围内相互作用。然而，尚不清楚如何在频率之间协调活动变化以支持认知激活。 We recently introduced a new neurophysiological framework, the "ATG (Alpha-Theta-Gamma) switch", reflecting a basic mechanism that generally implements 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。对于该框架功能的详细理解，现在有机会，需要提高数据分析策略，以量化和本地化跨频振荡和连接性。面临的挑战是，当前的方法基于传统的M/EEG源空间解决方案。这些功能最适合探索刺激或任务相关的活动，但很容易源泄漏效应，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合这些情况的新技术。特别是，我们建议（1）由于其独特的泄漏抑制属性，推进多源空间滤波方法； （2）基于多个来源的操作统计集合而不是单个概率，开发了一种用于改善超低信噪比条件中源重建的新概率算法； （3）开发一种基于源连接度量的新型空间过滤器。这三个目标将通过计算机模拟和实际M/EEG数据来验证。对于每种方法，我们都会从初步研究中获得积极的结果。结果将是（i）将在与任务相关的领域的网络（II）进行网络和连接分析的新型高级算法中更好地研究Alpha，beta，Theta和Gamma频率的协调变化，并且（III）将在公共可用的软件工具中提供大型研究社区的网络和连接分析算法，并将这些算法提供给这些算法。