Multiway array decomposition of EEG spectrum: Implications of its stability for the exploration of large-scale brain networks

Multiway array decomposition of EEG spectrum: Implications of its stability for the exploration of large-scale brain networks

Článek WoS

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.

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.

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

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

MAREČEK, R.; LAMOŠ, M.; LABOUNEK, R.; BARTOŇ, M.; SLAVÍČEK, T.; MIKL, M.; REKTOR, I.; BRÁZDIL, M.

2018

23.03.2017

MIT Press

Cambrige

0899-7667

NEURAL COMPUTATION

29

4

Spojené státy americké

968

989

22

http://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00933