Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG

F Chella; L Marzetti; V Pizzella; F Zappasodi; G Nolte

doi:10.1016/j.neuroimage.2013.12.064

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, Vol. 91, 2014, p. 146-61.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Chella, F, Marzetti, L, Pizzella, V, Zappasodi, F & Nolte, G 2014, 'Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG', NEUROIMAGE, vol. 91, pp. 146-61. https://doi.org/10.1016/j.neuroimage.2013.12.064

APA

Chella, F., Marzetti, L., Pizzella, V., Zappasodi, F., & Nolte, G. (2014). Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG. NEUROIMAGE, 91, 146-61. https://doi.org/10.1016/j.neuroimage.2013.12.064

Vancouver

Chella F, Marzetti L, Pizzella V, Zappasodi F, Nolte G. Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG. NEUROIMAGE. 2014;91:146-61. https://doi.org/10.1016/j.neuroimage.2013.12.064

Bibtex

@article{ebb5fbf8aec846cab9b9ec330f9ed71c,

title = "Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG",

abstract = "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.",

keywords = "Adult, Alpha Rhythm, Artifacts, Beta Rhythm, Brain Mapping, Electroencephalography, Female, Humans, Magnetoencephalography, Male, Nerve Net, Occipital Lobe, Parietal Lobe, Reference Values, Young Adult",

author = "F Chella and L Marzetti and V Pizzella and F Zappasodi and G Nolte",

year = "2014",

doi = "10.1016/j.neuroimage.2013.12.064",

language = "English",

volume = "91",

pages = "146--61",

journal = "NEUROIMAGE",

issn = "1053-8119",

publisher = "Academic Press",

}

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

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 -