Multiway Array Decomposition of EEG Spectrum: Implications of
Its Stability for the Exploration of Large-Scale Brain Networks

J 2017

Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks

MAREČEK, Radek, Martin LAMOŠ, René LABOUNEK, Marek BARTOŇ, Tomáš SLAVÍČEK et. al.

Basic information

Original name

Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks

Authors

MAREČEK, Radek (203 Czech Republic, guarantor, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution), René LABOUNEK (203 Czech Republic), Marek BARTOŇ (203 Czech Republic, belonging to the institution), Tomáš SLAVÍČEK (203 Czech Republic, belonging to the institution), Michal MIKL (203 Czech Republic, belonging to the institution), Ivan REKTOR (203 Czech Republic, belonging to the institution) and Milan BRÁZDIL (203 Czech Republic, belonging to the institution)

Edition

Neural Computation, MIT Press, 2017, 0899-7667

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 1.651

RIV identification code

RIV/00216224:14740/17:00095530

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1162/NECO_a_00933

UT WoS

000399678100005

Keywords in English

multimodal neuroimaging; brain rhythms; blind decomposition; large scale brain networks

Abstract

V originále

The multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method called PARAFAC. We focused on patterns’ stability over time and in population and divided the complete dataset containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, i.e. the common way of dealing with EEG data. Altogether our results suggest that the PARAFAC is a suitable method for research in the field of large scale brain networks and their manifestation in EEG signal.

Links

ED1.1.00/02.0068, research and development project

Name: CEITEC - central european institute of technology

GA14-33143S, research and development project

Name: Vliv fyziologických procesů na reliabilitu a časovou proměnlivost konektivity v lidském mozku měřené pomocí fMRI

Investor: Czech Science Foundation

Citovat

MAREČEK, Radek, Martin LAMOŠ, René LABOUNEK, Marek BARTOŇ, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR and Milan BRÁZDIL. Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks. Neural Computation. MIT Press, 2017, vol. 29, No 4, p. 968-989. ISSN 0899-7667. Available from: https://dx.doi.org/10.1162/NECO_a_00933.

@article{1409563,
   author = {Mareček, Radek and Lamoš, Martin and Labounek, René and Bartoň, Marek and Slavíček, Tomáš and Mikl, Michal and Rektor, Ivan and Brázdil, Milan},
   article_number = {4},
   doi = {http://dx.doi.org/10.1162/NECO_a_00933},
   keywords = {multimodal neuroimaging; brain rhythms; blind decomposition; large scale brain networks},
   language = {eng},
   issn = {0899-7667},
   journal = {Neural Computation},
   title = {Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks},
   url = {https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00933},
   volume = {29},
   year = {2017}
}

TY  - JOUR
ID  - 1409563
AU  - Mareček, Radek - Lamoš, Martin - Labounek, René - Bartoň, Marek - Slavíček, Tomáš - Mikl, Michal - Rektor, Ivan - Brázdil, Milan
PY  - 2017
TI  - Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks
JF  - Neural Computation
VL  - 29
IS  - 4
SP  - 968-989
EP  - 968-989
PB  - MIT Press
SN  - 08997667
KW  - multimodal neuroimaging
KW  - brain rhythms
KW  - blind decomposition
KW  - large scale brain networks
UR  - https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00933
L2  - https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00933
N2  - The multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method called PARAFAC. We focused on patterns’ stability over time and in population and divided the complete dataset containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, i.e. the common way of dealing with EEG data. Altogether our results suggest that the PARAFAC is a suitable method for research in the field of large scale brain networks and their manifestation in EEG signal.
ER  -

MAREČEK, Radek, Martin LAMOŠ, René LABOUNEK, Marek BARTOŇ, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR and Milan BRÁZDIL. Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks. \textit{Neural Computation}. MIT Press, 2017, vol.~29, No~4, p.~968-989. ISSN~0899-7667. Available from: https://dx.doi.org/10.1162/NECO\_{}a\_{}00933.

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