Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation
The primary method for evaluating high-order brain interactions by information-theoretic measures such as (dual) total correlation and O-information involves the Gaussian approximation. Although this approximation is rather accurate for functional MRI signals, it is unclear how accurate it is for el...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Journal of Physics: Complexity |
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| Online Access: | https://doi.org/10.1088/2632-072X/add3a9 |
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| author | Rikkert Hindriks Frank van der Meulen Michel J A M van Putten Prejaas Tewarie |
| author_facet | Rikkert Hindriks Frank van der Meulen Michel J A M van Putten Prejaas Tewarie |
| author_sort | Rikkert Hindriks |
| collection | DOAJ |
| description | The primary method for evaluating high-order brain interactions by information-theoretic measures such as (dual) total correlation and O-information involves the Gaussian approximation. Although this approximation is rather accurate for functional MRI signals, it is unclear how accurate it is for electroencephalography (EEG) and magnetoencephalography (MEG) signals. Here, we introduce the elliptical approximation, which is accurate for Gaussian data and a large family of non-Gaussian data. To illustrate its use, we applied both approximations to EEG and MEG signals and found that the Gaussian approximation to the (dual) total correlation and O-information is quite accurate for physiological resting-state oscillations, but is highly inaccurate for EEG data recorded during an absence seizure. In particular, for interactions of high-order ( $\gt$ 10) the approximations do not always agree on whether the interactions are dominated by synergy or redundancy. Thus, our proposed method offers an opportunity to study high-order interactions in electrophysiological brain activity. |
| format | Article |
| id | doaj-art-0818e386ef9e4df9b62092c40558de37 |
| institution | OA Journals |
| issn | 2632-072X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj-art-0818e386ef9e4df9b62092c40558de372025-08-20T02:17:01ZengIOP PublishingJournal of Physics: Complexity2632-072X2025-01-016202501110.1088/2632-072X/add3a9Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximationRikkert Hindriks0https://orcid.org/0000-0003-3575-186XFrank van der Meulen1https://orcid.org/0000-0001-7246-8612Michel J A M van Putten2https://orcid.org/0000-0001-8319-3626Prejaas Tewarie3https://orcid.org/0000-0002-3311-4990Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam , Amsterdam, The NetherlandsDepartment of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam , Amsterdam, The NetherlandsClinical Neurophysiology Group, University of Twente & Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente , Enschede, The NetherlandsClinical Neurophysiology Group, University of Twente & Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente , Enschede, The Netherlands; Sir Peter Mansfield Imaging Center, School of Physics, University of Nottingham , Nottingham, United Kingdom; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, Laval University , Quebec, CanadaThe primary method for evaluating high-order brain interactions by information-theoretic measures such as (dual) total correlation and O-information involves the Gaussian approximation. Although this approximation is rather accurate for functional MRI signals, it is unclear how accurate it is for electroencephalography (EEG) and magnetoencephalography (MEG) signals. Here, we introduce the elliptical approximation, which is accurate for Gaussian data and a large family of non-Gaussian data. To illustrate its use, we applied both approximations to EEG and MEG signals and found that the Gaussian approximation to the (dual) total correlation and O-information is quite accurate for physiological resting-state oscillations, but is highly inaccurate for EEG data recorded during an absence seizure. In particular, for interactions of high-order ( $\gt$ 10) the approximations do not always agree on whether the interactions are dominated by synergy or redundancy. Thus, our proposed method offers an opportunity to study high-order interactions in electrophysiological brain activity.https://doi.org/10.1088/2632-072X/add3a9multivariate information theoryhigh-order interactionelectroencephalography (EEG)magnetoencephalography (MEG)elliptical distributionentropy |
| spellingShingle | Rikkert Hindriks Frank van der Meulen Michel J A M van Putten Prejaas Tewarie Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation Journal of Physics: Complexity multivariate information theory high-order interaction electroencephalography (EEG) magnetoencephalography (MEG) elliptical distribution entropy |
| title | Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation |
| title_full | Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation |
| title_fullStr | Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation |
| title_full_unstemmed | Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation |
| title_short | Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation |
| title_sort | unraveling high order interactions in electrophysiological brain signals using elliptical distributions moving beyond the gaussian approximation |
| topic | multivariate information theory high-order interaction electroencephalography (EEG) magnetoencephalography (MEG) elliptical distribution entropy |
| url | https://doi.org/10.1088/2632-072X/add3a9 |
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