期刊
NEUROIMAGE
卷 91, 期 -, 页码 146-161出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2013.12.064
关键词
Functional connectivity; Cross-frequency interactions; Higher order spectral analysis; Bispectrum; Brain networks; Volume conduction
资金
- Italian Ministry of Education, University and Research (PRIN) [2010SH7H3F_006]
- EU [ERC-2010-AdG-269716]
- DFG [SFB 936/A3]
- BMBF [031A130]
- Mapping the Human Connectome Structure, Function, and Heritability from the 16 National Institutes of Health Institutes and Centers [1U54MH091657-01]
- NIH Blueprint for Neuroscience Research
We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electro-encephalographic (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 the 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. (C) 2014 Elsevier Inc. All rights reserved.
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