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@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 a J. JAN. Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics. \textit{JOURNAL OF NEURAL ENGINEERING}. BRISTOL: IOP PUBLISHING LTD, 2018, roč.~15, č.~3, s.~036025-36036. ISSN~1741-2560. Dostupné z: https://dx.doi.org/10.1088/1741-2552/aab66b.
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