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SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Authors
Daehne, SvenMeinecke, Frank C.Haufe, StefanHoehne, JohannesTangermann, MichaelMueller, Klaus-RobertNikulin, Vadim V.
Issue Date
1-2월-2014
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Keywords
EEG; MEG; Oscillations; SPoC; Source power comodulation; ASSEP
Citation
NEUROIMAGE, v.86, pp.111 - 122
Indexed
SCIE
SCOPUS
Journal Title
NEUROIMAGE
Volume
86
Start Page
111
End Page
122
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/99317
DOI
10.1016/j.neuroimage.2013.07.079
ISSN
1053-8119
Abstract
Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters. (C) 2013 The Authors. Published by Elsevier Inc All rights reserved.
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