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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F18%3A00101792" target="_blank" >RIV/00216224:14740/18:00101792 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/18:PU129167

Result on the web

<a href="http://dx.doi.org/10.1088/1741-2552/aab66b" target="_blank" >http://dx.doi.org/10.1088/1741-2552/aab66b</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/aab66b" target="_blank" >10.1088/1741-2552/aab66b</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF NEURAL ENGINEERING

ISSN

1741-2560

e-ISSN

—

Volume of the periodical

15

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

036025

UT code for WoS article

000430324700001

EID of the result in the Scopus database

2-s2.0-85047432761