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High-frequency Broadband Modulations of Electroencephalographic Spectra.

Onton J, Makeig S - Front Hum Neurosci (2009)

Bottom Line: High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities.Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions.Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities.

View Article: PubMed Central - PubMed

Affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.

ABSTRACT
High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities. EEG data from an eyes-closed emotion imagination task were linearly decomposed using independent component analysis (ICA) into maximally independent component (IC) processes. Joint decomposition of IC log spectrograms into source- and frequency-independent modulator (IM) processes revealed three distinct classes of IMs that separately modulated broadband high-frequency ( approximately 15-200 Hz) power of brain, scalp muscle, and likely ocular motor IC processes. Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions. Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities.

No MeSH data available.


Independent spectral modulators of scalp EEG signals. ICA, applied to EEG data recorded at a large number of scalp electrodes, identifies (A) temporally distinct (independent) signals generated by partial synchronization of local field potentials within cortical patches (B), the resulting far-field potentials summed (Σ), in differing linear combinations, at each electrode depending on the distance and orientation of each cortical patch generator relative to the (A) recording and (C) reference electrodes. On average, power in the cortical IC signals decrease monotonically with frequency, but also exhibit continual, marked, and complex variations across time. Rather than viewing these variations as occurring independently at each frequency, spectral modulations may be modeled as exponentially weighted influences of several distinct but possibly overlapping modulator (IM) processes (D) that independently modulate via multiplicatively scaling (Π) the activity spectra of one or more independent component (IC) signals. On converting the IC spectra to log power, combined IM influences on IC spectra are converted to log-linear weighted sums of IM influences, allowing a linear ICA decomposition of the IC log-power spectra to separate the effects of the individual IM processes (D) on power at selected frequencies of IC sources (B).
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Figure 1: Independent spectral modulators of scalp EEG signals. ICA, applied to EEG data recorded at a large number of scalp electrodes, identifies (A) temporally distinct (independent) signals generated by partial synchronization of local field potentials within cortical patches (B), the resulting far-field potentials summed (Σ), in differing linear combinations, at each electrode depending on the distance and orientation of each cortical patch generator relative to the (A) recording and (C) reference electrodes. On average, power in the cortical IC signals decrease monotonically with frequency, but also exhibit continual, marked, and complex variations across time. Rather than viewing these variations as occurring independently at each frequency, spectral modulations may be modeled as exponentially weighted influences of several distinct but possibly overlapping modulator (IM) processes (D) that independently modulate via multiplicatively scaling (Π) the activity spectra of one or more independent component (IC) signals. On converting the IC spectra to log power, combined IM influences on IC spectra are converted to log-linear weighted sums of IM influences, allowing a linear ICA decomposition of the IC log-power spectra to separate the effects of the individual IM processes (D) on power at selected frequencies of IC sources (B).

Mentions: Many studies of EEG spectral dynamics consider separate, narrow and/or pre-defined frequency bins. However, to better understand the functional roles of local field dynamics in the brain, more flexible data-driven models of spectral dynamics are desirable. Here, we attempted to characterize the distinct, frequency modulations occurring in continuously recorded EEG data, irrespective of their physiological bases, and to determine the inter- relationship of observed modulations in brain and muscle source EEG activities. Previously, we have shown that spectral activity of ICs projecting to the frontal midline scalp exhibit multiple modes of event-related power modulation during a working memory task (Onton et al., 2005). The method we applied here is a related but novel method for decomposing log-spectral fluctuations of multiple ICs into a product of distinct spectral modulator processes (Figure 1 and Materials and Methods). Such processes might derive from coordinated actions of modulatory factors, for example the brainstem-based neuromodulatory systems releasing dopamine, acetylcholine, norepinephrine, etc., that are linked to arousal and event evaluation (Robbins, 1997; Bardo, 1998) or other cognitive/emotional processes. There may appear to be a contradiction between the concepts of ‘independent’ component processes (ICs) and ‘amplitude co-modulated’ processes (co- modulated by one or more IMs), since by strict definition co-modulated processes are not truly independent. Mathematical investigation shows, however, that infomax ICA should correctly separate and identify co-modulated but otherwise independent processes when their probability density functions resemble those of typical brain source ICs (Palmer and Makeig, 2010).


High-frequency Broadband Modulations of Electroencephalographic Spectra.

Onton J, Makeig S - Front Hum Neurosci (2009)

Independent spectral modulators of scalp EEG signals. ICA, applied to EEG data recorded at a large number of scalp electrodes, identifies (A) temporally distinct (independent) signals generated by partial synchronization of local field potentials within cortical patches (B), the resulting far-field potentials summed (Σ), in differing linear combinations, at each electrode depending on the distance and orientation of each cortical patch generator relative to the (A) recording and (C) reference electrodes. On average, power in the cortical IC signals decrease monotonically with frequency, but also exhibit continual, marked, and complex variations across time. Rather than viewing these variations as occurring independently at each frequency, spectral modulations may be modeled as exponentially weighted influences of several distinct but possibly overlapping modulator (IM) processes (D) that independently modulate via multiplicatively scaling (Π) the activity spectra of one or more independent component (IC) signals. On converting the IC spectra to log power, combined IM influences on IC spectra are converted to log-linear weighted sums of IM influences, allowing a linear ICA decomposition of the IC log-power spectra to separate the effects of the individual IM processes (D) on power at selected frequencies of IC sources (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2806183&req=5

Figure 1: Independent spectral modulators of scalp EEG signals. ICA, applied to EEG data recorded at a large number of scalp electrodes, identifies (A) temporally distinct (independent) signals generated by partial synchronization of local field potentials within cortical patches (B), the resulting far-field potentials summed (Σ), in differing linear combinations, at each electrode depending on the distance and orientation of each cortical patch generator relative to the (A) recording and (C) reference electrodes. On average, power in the cortical IC signals decrease monotonically with frequency, but also exhibit continual, marked, and complex variations across time. Rather than viewing these variations as occurring independently at each frequency, spectral modulations may be modeled as exponentially weighted influences of several distinct but possibly overlapping modulator (IM) processes (D) that independently modulate via multiplicatively scaling (Π) the activity spectra of one or more independent component (IC) signals. On converting the IC spectra to log power, combined IM influences on IC spectra are converted to log-linear weighted sums of IM influences, allowing a linear ICA decomposition of the IC log-power spectra to separate the effects of the individual IM processes (D) on power at selected frequencies of IC sources (B).
Mentions: Many studies of EEG spectral dynamics consider separate, narrow and/or pre-defined frequency bins. However, to better understand the functional roles of local field dynamics in the brain, more flexible data-driven models of spectral dynamics are desirable. Here, we attempted to characterize the distinct, frequency modulations occurring in continuously recorded EEG data, irrespective of their physiological bases, and to determine the inter- relationship of observed modulations in brain and muscle source EEG activities. Previously, we have shown that spectral activity of ICs projecting to the frontal midline scalp exhibit multiple modes of event-related power modulation during a working memory task (Onton et al., 2005). The method we applied here is a related but novel method for decomposing log-spectral fluctuations of multiple ICs into a product of distinct spectral modulator processes (Figure 1 and Materials and Methods). Such processes might derive from coordinated actions of modulatory factors, for example the brainstem-based neuromodulatory systems releasing dopamine, acetylcholine, norepinephrine, etc., that are linked to arousal and event evaluation (Robbins, 1997; Bardo, 1998) or other cognitive/emotional processes. There may appear to be a contradiction between the concepts of ‘independent’ component processes (ICs) and ‘amplitude co-modulated’ processes (co- modulated by one or more IMs), since by strict definition co-modulated processes are not truly independent. Mathematical investigation shows, however, that infomax ICA should correctly separate and identify co-modulated but otherwise independent processes when their probability density functions resemble those of typical brain source ICs (Palmer and Makeig, 2010).

Bottom Line: High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities.Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions.Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities.

View Article: PubMed Central - PubMed

Affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.

ABSTRACT
High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities. EEG data from an eyes-closed emotion imagination task were linearly decomposed using independent component analysis (ICA) into maximally independent component (IC) processes. Joint decomposition of IC log spectrograms into source- and frequency-independent modulator (IM) processes revealed three distinct classes of IMs that separately modulated broadband high-frequency ( approximately 15-200 Hz) power of brain, scalp muscle, and likely ocular motor IC processes. Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions. Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities.

No MeSH data available.