Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG.
Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG.
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DOI：
10.1016/j.neuroimage.2013.12.064
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发表时间：
2014-05-01
期刊：
影响因子：
5.7
通讯作者：
Nolte G
中科院分区：
文献类型：
--
作者：
Chella F;Marzetti L;Pizzella V;Zappasodi F;Nolte G
我们提出了一种新的方法，三阶谱分析，通常被称为双谱分析，脑电图（EEG）和脑磁图（MEG）的数据，研究跨频率的功能性大脑连接。从EEG和MEG测量中估计功能连接的主要障碍在于信号是潜在脑源活动的大部分未知混合物。这通常构成严重的混杂因素，并严重影响脑源相互作用的检测。为了克服这个问题，我们以前开发的度量的基础上的属性的虚部的一致性。在这里，我们推广这些性质从线性到非线性的情况。具体来说，我们提出了一个度量的基础上反对称组合的交叉双谱，我们证明是强大的混合文物。此外，我们的度量提供了复值的数量，使有机会研究大脑源之间的相位关系。该方法的有效性首先证明了对模拟EEG数据。与传统的双谱度量相比，所提出的方法显示出对混合伪影的灵敏度降低。它也表现出更好的性能，在提取源之间的相位关系比互谱的虚部延迟相互作用。然后将该方法应用于静息状态下记录的真实的EEG数据。在10 Hz和20 Hz的脑源之间观察到交叉频率相互作用，即，α和β节律。这种相互作用，然后通过使用一个适合为基础的程序，从信号到源的水平。这种方法突出了10-20 Hz的优势相互作用定位在枕顶中枢网络。
We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electroencephalographic (EEG) and magnetoencephalographic (MEG) data for studying cross-frequency functional brain connectivity. The main obstacle in estimating functional connectivity from EEG and MEG measurements lies in the signals being a largely unknown mixture of the activities of the underlying brain sources. This often constitutes a severe confounder and heavily affects the detection of brain source interactions. To overcome this problem, we previously developed metrics based on the properties of the imaginary part of coherency. Here, we generalize these properties from the linear to the nonlinear case. Specifically, we propose a metric based on an antisymmetric combination of cross-bispectra, which we demonstrate to be robust to mixing artifacts. Moreover, our metric provides complex-valued quantities that give the opportunity to study phase relationships between brain sources. The effectiveness of the method is first demonstrated on simulated EEG data. The proposed approach shows a reduced sensitivity to mixing artifacts when compared with a traditional bispectral metric. It also exhibits a better performance in extracting phase relationships between sources than the imaginary part of cross-spectrum for delayed interactions. The method is then applied to real EEG data recorded during resting state. A cross-frequency interaction is observed between brain sources at 10 Hz and 20 Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal to source level by using a fit-based procedure. This approach highlights a 10–20 Hz dominant interaction localized in an occipito-parieto-central network.
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DOI：
10.1016/0013-4694(71)90183-0
发表时间：
1971-01-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
DUMERMUTH, G;HUBER, PJ;GASSER, T
通讯作者：
GASSER, T
影响因子：
3
作者：
Helbig, Marko;Schwab, Karin;Witte, Herbert
通讯作者：
Witte, Herbert
DOI：
10.1177/1073858409354384
发表时间：
2011-02
期刊：
The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry
影响因子：
--
作者：
Deco G;Corbetta M
通讯作者：
Corbetta M
影响因子：
5.7
作者：
Marzetti, Laura;Del Gratta, Cosimo;Nolte, Guido
通讯作者：
Nolte, Guido
影响因子：
16.2
作者：
de Pasquale F;Della Penna S;Snyder AZ;Marzetti L;Pizzella V;Romani GL;Corbetta M
通讯作者：
Corbetta M