Spatial-temporal-spectral EEG patterns of BOLD functional
network connectivity dynamics

J 2018

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

LAMOŠ, Martin, Radek MAREČEK, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR et. al.

Basic information

Original name

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

Authors

LAMOŠ, Martin (203 Czech Republic, belonging to the institution), Radek MAREČEK (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, guarantor, belonging to the institution) and J. JAN (203 Czech Republic)

Edition

JOURNAL OF NEURAL ENGINEERING, BRISTOL, IOP PUBLISHING LTD, 2018, 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

Impact factor

Impact factor: 4.551

RIV identification code

RIV/00216224:14740/18:00101792

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1088/1741-2552/aab66b

UT WoS

000430324700001

Keywords in English

multimodal neuroimaging; dynamic functional connectivity; blind decomposition; large-scale brain networks; parallel factor analysis; independent component analysis

Abstract

V originále

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Links

EF16_013/0001775, research and development project

Name: Modernizace a podpora výzkumných aktivit národní infrastruktury pro biologické a medicínské zobrazování Czech-BioImaging

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

LM2015062, research and development project

Name: Národní infrastruktura pro biologické a medicínské zobrazování

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

LAMOŠ, Martin, Radek MAREČEK, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR and J. JAN. Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics. JOURNAL OF NEURAL ENGINEERING. BRISTOL: IOP PUBLISHING LTD, 2018, vol. 15, No 3, p. 036025-36036. ISSN 1741-2560. Available from: https://dx.doi.org/10.1088/1741-2552/aab66b.

@article{1507757,
   author = {Lamoš, Martin and Mareček, Radek and Slavíček, Tomáš and Mikl, Michal and Rektor, Ivan and Jan, J.},
   article_location = {BRISTOL},
   article_number = {3},
   doi = {http://dx.doi.org/10.1088/1741-2552/aab66b},
   keywords = {multimodal neuroimaging; dynamic functional connectivity; blind decomposition; large-scale brain networks; parallel factor analysis; independent component analysis},
   language = {eng},
   issn = {1741-2560},
   journal = {JOURNAL OF NEURAL ENGINEERING},
   title = {Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics},
   volume = {15},
   year = {2018}
}

TY  - JOUR
ID  - 1507757
AU  - Lamoš, Martin - Mareček, Radek - Slavíček, Tomáš - Mikl, Michal - Rektor, Ivan - Jan, J.
PY  - 2018
TI  - Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics
JF  - JOURNAL OF NEURAL ENGINEERING
VL  - 15
IS  - 3
SP  - 036025
EP  - 036025
PB  - IOP PUBLISHING LTD
SN  - 17412560
KW  - multimodal neuroimaging
KW  - dynamic functional connectivity
KW  - blind decomposition
KW  - large-scale brain networks
KW  - parallel factor analysis
KW  - independent component analysis
N2  - Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
ER  -

LAMOŠ, Martin, Radek MAREČEK, Tomáš SLAVÍČEK, Michal MIKL, Ivan REKTOR and J. JAN. Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics. \textit{JOURNAL OF NEURAL ENGINEERING}. BRISTOL: IOP PUBLISHING LTD, 2018, vol.~15, No~3, p.~036025-36036. ISSN~1741-2560. Available from: https://dx.doi.org/10.1088/1741-2552/aab66b.

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