Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  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.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Volk_2018.pdf (Verlagsversion), 3MB
Name:
Volk_2018.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Volk, Denis 1, Autor
Dubinin, Igor 2, 3, Autor
Myasnikova, Alexandra 2, Autor
Gutkin, Boris 2, 4, Autor
Nikulin, Vadim V.2, 5, 6, 7, Autor           
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              

Inhalt

einblenden:
ausblenden:
Schlagwörter: Cross-frequency coupling; EEG & MEG; Phase-to-phase coupling; Brain oscillations; Source localization
 Zusammenfassung: 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).

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2018-07-192018-09-262018-10-18
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3389/fninf.2018.00072
PMID: 30405385
PMC: PMC6200871
Anderer: eCollection 2018
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden: ausblenden:
Projektname : -
Grant ID : 14.641.31.0003
Förderprogramm : RF Government Grant
Förderorganisation : Center for Bioelectric Interfaces, NRU Higher School of Economics

Quelle 1

einblenden:
ausblenden:
Titel: Frontiers in Neuroinformatics
  Kurztitel : Front Neuroinform
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Lausanne, Switzerland : Frontiers Research Foundation
Seiten: - Band / Heft: 12 Artikelnummer: 72 Start- / Endseite: - Identifikator: ISSN: 1662-5196
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5196