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  Generalized cross-frequency decomposition: A method for the extraction of neuronal components coupled at different frequencies

Volk, D., Dubinin, I., Myasnikova, A., Gutkin, B., & Nikulin, V. V. (2018). Generalized cross-frequency decomposition: A method for the extraction of neuronal components coupled at different frequencies. Frontiers in Neuroinformatics, 12: 72. doi:10.3389/fninf.2018.00072.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-60E9-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9A0A-5
Genre: Journal Article

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 Creators:
Volk, Denis 1, Author
Dubinin, Igor 2, 3, Author
Myasnikova, Alexandra 2, Author
Gutkin, Boris 2, 4, Author
Nikulin, Vadim V.2, 5, 6, 7, Author              
Affiliations:
1Interdisciplinary Scientific Center J.-V. Poncelet (ISCP), Moscow, Russia, ou_persistent22              
2Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              
3Moscow Institute of Physics and Technology (MIPT), Moscow, Russia, ou_persistent22              
4Group for Neural Theory, Department d'etudes cognitives, École normale supérieure, Paris, France, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
6Neurophysics Group, Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
7Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              

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Free keywords: Cross-frequency coupling; EEG & MEG; Phase-to-phase coupling; Brain oscillations; Source localization
 Abstract: Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f1 and f2 whenever f1:f2 is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP).

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Language(s): eng - English
 Dates: 2018-07-192018-09-262018-10-18
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fninf.2018.00072
PMID: 30405385
PMC: PMC6200871
Other: eCollection 2018
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Project name : -
Grant ID : 14.641.31.0003
Funding program : RF Government Grant
Funding organization : Center for Bioelectric Interfaces, NRU Higher School of Economics

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Title: Frontiers in Neuroinformatics
  Abbreviation : Front Neuroinform
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 12 Sequence Number: 72 Start / End Page: - Identifier: ISSN: 1662-5196
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5196