Publication detail

Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG

LABOUNEK, R. LAMOŠ, M. MAREČEK, R. BRÁZDIL, M. JAN, J.

Original Title

Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG

English Title

Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG

Type

journal article in Web of Science

Language

en

Original Abstract

Background: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). New method: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. Results: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. Comparison with existing methods: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. Conclusions: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.

English abstract

Background: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). New method: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. Results: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. Comparison with existing methods: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. Conclusions: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.

Keywords

Simultaneous EEG-fMRI, Visual oddball paradigm, Absolute and relative power, Regressor, General linear model (GLM), Task-related variability, EEG Regressor Builder

RIV year

2015

Released

30.04.2015

Publisher

Elsevier B. V.

Location

Netherlands

Pages from

125

Pages to

136

Pages count

12

URL

BibTex


@article{BUT113412,
  author="René {Labounek} and Martin {Lamoš} and Radek {Mareček} and Milan {Brázdil} and Jiří {Jan}",
  title="Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG",
  annote="Background: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM).

New method: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM.

Results: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived.

Comparison with existing methods: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task.

Conclusions: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.",
  address="Elsevier B. V.",
  chapter="113412",
  doi="10.1016/j.jneumeth.2015.02.016",
  institution="Elsevier B. V.",
  number="1",
  volume="245",
  year="2015",
  month="april",
  pages="125--136",
  publisher="Elsevier B. V.",
  type="journal article in Web of Science"
}