Format

Send to

Choose Destination
Neuroimage. 2014 Feb 1;86:111-22. doi: 10.1016/j.neuroimage.2013.07.079. Epub 2013 Aug 15.

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

Author information

1
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany. Electronic address: sven.daehne@tu-berlin.de.
2
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany.
3
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany.
4
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr. 23, 10587 Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany; Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, 12203 Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea. Electronic address: klaus-robert.mueller@tu-berlin.de.
5
Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, 12203 Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany. Electronic address: vadim.nikulin@charite.de.

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.

KEYWORDS:

ASSEP; EEG; MEG; Oscillations; SPoC; Source power comodulation

[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center