Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG
Standard
Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG. / Chella, F; Marzetti, L; Pizzella, V; Zappasodi, F; Nolte, G.
in: NEUROIMAGE, Jahrgang 91, 2014, S. 146-61.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG
AU - Chella, F
AU - Marzetti, L
AU - Pizzella, V
AU - Zappasodi, F
AU - Nolte, G
N1 - Copyright © 2014 Elsevier Inc. All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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 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 10Hz and 20Hz, 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 10-20Hz dominant interaction localized in an occipito-parieto-central network.
AB - 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 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 10Hz and 20Hz, 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 10-20Hz dominant interaction localized in an occipito-parieto-central network.
KW - Adult
KW - Alpha Rhythm
KW - Artifacts
KW - Beta Rhythm
KW - Brain Mapping
KW - Electroencephalography
KW - Female
KW - Humans
KW - Magnetoencephalography
KW - Male
KW - Nerve Net
KW - Occipital Lobe
KW - Parietal Lobe
KW - Reference Values
KW - Young Adult
U2 - 10.1016/j.neuroimage.2013.12.064
DO - 10.1016/j.neuroimage.2013.12.064
M3 - SCORING: Journal article
C2 - 24418509
VL - 91
SP - 146
EP - 161
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
ER -