Literature DB >> 27300936

Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals.

Federico Chella1,2, Vittorio Pizzella1,2, Filippo Zappasodi1,2, Guido Nolte3, Laura Marzetti1,2.   

Abstract

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.

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Year:  2016        PMID: 27300936     DOI: 10.1103/PhysRevE.93.052420

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  5 in total

Review 1.  Nonlinear System Identification of Neural Systems from Neurophysiological Signals.

Authors:  Fei He; Yuan Yang
Journal:  Neuroscience       Date:  2020-12-11       Impact factor: 3.590

2.  Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice.

Authors:  Federico Chella; Antea D'Andrea; Alessio Basti; Vittorio Pizzella; Laura Marzetti
Journal:  Front Neurosci       Date:  2017-05-16       Impact factor: 4.677

3.  The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation.

Authors:  Ernesto Pereda; Miguel García-Torres; Belén Melián-Batista; Soledad Mañas; Leopoldo Méndez; Julián J González
Journal:  PLoS One       Date:  2018-08-16       Impact factor: 3.240

Review 4.  Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography.

Authors:  Laura Marzetti; Alessio Basti; Federico Chella; Antea D'Andrea; Jaakko Syrjälä; Vittorio Pizzella
Journal:  Front Neurosci       Date:  2019-09-12       Impact factor: 4.677

5.  Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases.

Authors:  Yvonne Höller; Kevin Butz; Aljoscha Thomschewski; Elisabeth Schmid; Andreas Uhl; Arne C Bathke; Georg Zimmermann; Santino O Tomasi; Raffaele Nardone; Wolfgang Staffen; Peter Höller; Markus Leitinger; Julia Höfler; Gudrun Kalss; Alexandra C Taylor; Giorgi Kuchukhidze; Eugen Trinka
Journal:  Front Hum Neurosci       Date:  2017-07-05       Impact factor: 3.169

  5 in total

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