Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Mina Jamshidi Idaji; Klaus Robert Müller; Guido Nolte; Burkhard Maess; Arno Villringer; Vadim V. Nikulin

doi:10.1016/j.neuroimage.2020.116599

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Mina Jamshidi Idaji, Klaus Robert Müller, Guido Nolte, Burkhard Maess, Arno Villringer, Vadim V. Nikulin

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

Original language	English
Article number	116599
Journal	NeuroImage
Volume	211
DOIs	https://doi.org/10.1016/j.neuroimage.2020.116599
Publication status	Published - 2020 May 1

Bibliographical note

Funding Information:
VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project ‘5–100’. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451 ), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 , 01GQ0850 , 01IS18025A and 01IS18037A ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 . This research was partially funded by the German Research Foundation ( DFG, SFB936/Z3 and TRR169/C1/B4 ).

Funding Information:
VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project ?5?100?. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689.

Publisher Copyright:
© 2020 The Authors

Keywords

Cross-frequency coupling
EEG
ICA
Independent component analysis
MEG
NID
Nonlinear interaction decomposition
Nonlinear neuronal interactions

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2020.116599

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@article{449e48b2708346649f61ba004380fe7d,

title = "Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain",

abstract = "Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.",

keywords = "Cross-frequency coupling, EEG, ICA, Independent component analysis, MEG, NID, Nonlinear interaction decomposition, Nonlinear neuronal interactions",

author = "Idaji, {Mina Jamshidi} and M{\"u}ller, {Klaus Robert} and Guido Nolte and Burkhard Maess and Arno Villringer and Nikulin, {Vadim V.}",

note = "Funding Information: VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project {\textquoteleft}5–100{\textquoteright}. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451 ), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 , 01GQ0850 , 01IS18025A and 01IS18037A ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 . This research was partially funded by the German Research Foundation ( DFG, SFB936/Z3 and TRR169/C1/B4 ). Funding Information: VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project ?5?100?. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. Publisher Copyright: {\textcopyright} 2020 The Authors",

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doi = "10.1016/j.neuroimage.2020.116599",

language = "English",

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T1 - Nonlinear interaction decomposition (NID)

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AU - Idaji, Mina Jamshidi

AU - Müller, Klaus Robert

AU - Nolte, Guido

AU - Maess, Burkhard

AU - Villringer, Arno

AU - Nikulin, Vadim V.

N1 - Funding Information: VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project ‘5–100’. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451 ), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 , 01GQ0850 , 01IS18025A and 01IS18037A ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 . This research was partially funded by the German Research Foundation ( DFG, SFB936/Z3 and TRR169/C1/B4 ). Funding Information: VVN was supported in part by the HSE Basic Research Program and the Russian Academic Excellence Project ?5?100?. KRM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. Publisher Copyright: © 2020 The Authors

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N2 - Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

AB - Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

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KW - EEG

KW - ICA

KW - Independent component analysis

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KW - Nonlinear interaction decomposition

KW - Nonlinear neuronal interactions

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Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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