What can be found in scalp EEG spectrum beyond common frequency
bands. EEG-fMRI study

J 2016

What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study

MAREČEK, Radek, Martin LAMOŠ, Michal MIKL, Marek BARTOŇ, Jiří FAJKUS et. al.

Basic information

Original name

What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study

Authors

MAREČEK, Radek (203 Czech Republic, guarantor, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution), Michal MIKL (203 Czech Republic, belonging to the institution), Marek BARTOŇ (203 Czech Republic, belonging to the institution), Jiří FAJKUS (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

JOURNAL OF NEURAL ENGINEERING, BRISTOL, IOP PUBLISHING LTD, 2016, 1741-2560

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.465

RIV identification code

RIV/00216224:14740/16:00094577

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1088/1741-2560/13/4/046026

UT WoS

000380668900029

Keywords in English

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

Abstract

V originále

Objective. The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. Approach. We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. Main results. Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. Significance. These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.

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, Michal MIKL, Marek BARTOŇ, Jiří FAJKUS, Ivan REKTOR and Milan BRÁZDIL. What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study. JOURNAL OF NEURAL ENGINEERING. BRISTOL: IOP PUBLISHING LTD, 2016, vol. 13, No 4, p. nestránkováno, 13 pp. ISSN 1741-2560. Available from: https://dx.doi.org/10.1088/1741-2560/13/4/046026.

@article{1364744,
   author = {Mareček, Radek and Lamoš, Martin and Mikl, Michal and Bartoň, Marek and Fajkus, Jiří and Rektor, Ivan and Brázdil, Milan},
   article_location = {BRISTOL},
   article_number = {4},
   doi = {http://dx.doi.org/10.1088/1741-2560/13/4/046026},
   keywords = {multimodal neuroimaging; brain rhythms; blind decomposition; large scale brain networks},
   language = {eng},
   issn = {1741-2560},
   journal = {JOURNAL OF NEURAL ENGINEERING},
   title = {What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study},
   url = {http://iopscience.iop.org/article/10.1088/1741-2560/13/4/046026/pdf},
   volume = {13},
   year = {2016}
}

TY  - JOUR
ID  - 1364744
AU  - Mareček, Radek - Lamoš, Martin - Mikl, Michal - Bartoň, Marek - Fajkus, Jiří - Rektor, Ivan - Brázdil, Milan
PY  - 2016
TI  - What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study
JF  - JOURNAL OF NEURAL ENGINEERING
VL  - 13
IS  - 4
SP  - nestránkováno
EP  - nestránkováno
PB  - IOP PUBLISHING LTD
SN  - 17412560
KW  - multimodal neuroimaging
KW  - brain rhythms
KW  - blind decomposition
KW  - large scale brain networks
UR  - http://iopscience.iop.org/article/10.1088/1741-2560/13/4/046026/pdf
L2  - http://iopscience.iop.org/article/10.1088/1741-2560/13/4/046026/pdf
N2  - Objective. The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. Approach. We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. Main results. Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. Significance. These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.
ER  -

MAREČEK, Radek, Martin LAMOŠ, Michal MIKL, Marek BARTOŇ, Jiří FAJKUS, Ivan REKTOR and Milan BRÁZDIL. What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study. \textit{JOURNAL OF NEURAL ENGINEERING}. BRISTOL: IOP PUBLISHING LTD, 2016, vol.~13, No~4, p.~nestránkováno, 13 pp. ISSN~1741-2560. Available from: https://dx.doi.org/10.1088/1741-2560/13/4/046026.

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