Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space
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DOI:
10.1016/j.neuroimage.2011.11.084
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发表时间:
2012-03-01
期刊:
影响因子:
5.7
通讯作者:
Nolte, Guido
Nolte, Guido
中科院分区:
医学1区
文献类型:
--
作者:
Ewald, Arne;Marzetti, Laura;Nolte, Guido

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相干性的虚部是在EEG/MEG传感器水平上研究脑源同步的度量,对体积传导的伪影具有鲁棒性,这意味着独立源不能产生显著的结果。然而,这并不意味着当存在真实的相互作用时,体积传导是不相关的。在这里,我们详细分析了构建真正的大脑相互作用的措施,这是严格不变的传感器数据的线性空间变换的可能性。具体地,这样的测量可以从虚拟通道中的虚相干性的最大化、作为虚相干性的校正变量的双变量测量、以及指示包含在空间内或两个空间之间的总交互的全局测量来构造。二阶统计矩为这一问题提供了一个完整的理论框架。现有的线性和非线性方法的关系。我们将该方法应用于静息状态EEG数据,在所有波段显示出清晰的相互作用,并在静息状态和手指敲击任务期间组合测量EEG和MEG。我们发现MEG能够观察到在EEG数据中无法观察到的大脑相互作用(C)2011 Elsevier Inc. All rights reserved.
The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data (C) 2011 Elsevier Inc. All rights reserved.