Publication detail

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

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

Original Title

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

English Title

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

Type

journal article

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

Released

23.03.2017

Publisher

MIT Press

Location

Cambrige

Pages from

968

Pages to

989

Pages count

22

URL

BibTex


@article{BUT129444,
  author="Radek {Mareček} and Martin {Lamoš} and René {Labounek} and Marek {Bartoň} and Tomáš {Slavíček} and Michal {Mikl} and Ivan {Rektor} and Milan {Brázdil}",
  title="Multiway array decomposition of EEG spectrum: Implications of its stability for the exploration of large-scale brain networks",
  annote="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.",
  address="MIT Press",
  chapter="129444",
  doi="10.1162/NECO_a_00933",
  howpublished="print",
  institution="MIT Press",
  number="4",
  volume="29",
  year="2017",
  month="march",
  pages="968--989",
  publisher="MIT Press",
  type="journal article"
}