TY - JOUR
T1 - Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG
AU - Labounek, René
AU - Lamoš, Martin
AU - Mareček, Radek
AU - Brázdil, Milan
AU - Jan, Jiří
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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.
AB - 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.
KW - Absolute and relative power
KW - EEG Regressor Builder
KW - General linear model (GLM)
KW - Regressor
KW - Simultaneous EEG-fMRI
KW - Task-related variability
KW - Visual oddball paradigm
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U2 - 10.1016/j.jneumeth.2015.02.016
DO - 10.1016/j.jneumeth.2015.02.016
M3 - Article
C2 - 25724321
AN - SCOPUS:84925012809
SN - 0165-0270
VL - 245
SP - 125
EP - 136
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -