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.

Základní údaje

Originální název

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

Autoři

MAREČEK, Radek (203 Česká republika, garant, domácí), Martin LAMOŠ (203 Česká republika, domácí), René LABOUNEK (203 Česká republika), Marek BARTOŇ (203 Česká republika, domácí), Tomáš SLAVÍČEK (203 Česká republika, domácí), Michal MIKL (203 Česká republika, domácí), Ivan REKTOR (203 Česká republika, domácí) a Milan BRÁZDIL (203 Česká republika, domácí)

Vydání

Neural Computation, MIT Press, 2017, 0899-7667

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 1.651

Kód RIV

RIV/00216224:14740/17:00095530

Organizační jednotka

Středoevropský technologický institut

DOI

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

UT WoS

000399678100005

Klíčová slova anglicky

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

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 3. 2018 16:37, Mgr. Pavla Foltynová, Ph.D.

Anotace

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.

Návaznosti

ED1.1.00/02.0068, projekt VaV

Název: CEITEC - central european institute of technology

GA14-33143S, projekt VaV

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

Investor: Grantová agentura ČR, Vliv fyziologických procesů na reliabilitu a časovou proměnlivost konektivity v lidském mozku měřené pomocí fMRI

Citovat

MAREČEK, Radek, Martin LAMOŠ, René LABOUNEK, Marek BARTOŇ, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR a 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, roč. 29, č. 4, s. 968-989. ISSN 0899-7667. Dostupné z: 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 a 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, roč.~29, č.~4, s.~968-989. ISSN~0899-7667. Dostupné z: https://dx.doi.org/10.1162/NECO\_{}a\_{}00933.

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