Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Doctoral Programme Brain & Mind, University of Helsinki, Finland.
Neuroimage. 2018 Jun;173:632-643. doi: 10.1016/j.neuroimage.2018.02.032. Epub 2018 Feb 22.
When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.
当与源建模相结合时,磁共振(MEG)和脑电图(EEG)可用于无创地研究皮质过程之间的远程相互作用。然而,这种区域间连通性的估计受到瞬时场扩散和容积传导的阻碍,这会人为地引入线性相关,并损害皮质电流估计中的源可分离性。为了克服标准互相关测量中固有的线性源混合的放大效应,已经提出了基于相位和幅度相关的替代连通性测量,如虚相干和正交幅度相关。由于这些技术从定义上对零延迟相关不敏感,因此在识别不能归因于场扩散或容积传导的相关性方面越来越受欢迎。然而,我们在这里表明,尽管这些措施不受线性混合的直接影响,但它们仍然可能通过真相互作用附近的场扩散揭示大量虚假的阳性连接。这个基本问题影响了基于感兴趣区域的分析和全连接图映射。最重要的是,除了定义和说明虚假或“幽灵”相互作用的问题外,我们还通过广泛的模拟对这种影响进行了严格的量化。此外,我们还进一步表明,信号混合也显著限制了神经元相位和幅度相关性的可分离性。我们得出结论,即使使用对零延迟相关免疫的测量方法,在 MEG/EEG 源空间中的连通性分析中也必须仔细考虑虚假相关性。