5. DISCUSSION
5.1 T HE COMPONENT SPECTRAL FLUCTUATIONS RELATED TO PERFORMANCE
The theta and alpha power changes of several components from good performance to poor performance were similar between different brain regions. It is consistent with past studies (Lin et al., 2006; Huang et al., 2008). Hence, we compared the component fluctuations in time series between different components of intra-subject to confirm that the drowsiness related alpha and theta rhythm in these components may be modulated by the same nucleus or synchronized by cortical-cortical interaction.
5.2 The modulatory model of the neural system
The modulatory model underlying this analysis was illustrated schematically in Fig. 1-1.
Independent component analysis (ICA) was applied to EEG data to identifies temporally distinct (independent) signals generated by partial synchronization of local field potentials within cortical patches (b) and summing in different linear combinations at each electrode depending on the distance and orientation of each cortical patch from the scalp (a) and reference electrode (c). The spectra of resulting cortical independent components (ICs) monotonically decrease with frequency, on average, but exhibit large and frequent variations across time. These spectral modulations may be modeled as exponentially weighted influences of several near-independent modulator (IM) processes (d) that independently modulate 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 second linear ICA decomposition, applied to the IC log power spectra, to separate the effects of the individual IM processes (d) across EEG frequencies and IC sources (b).
investigate the modulation model. They have examined the correlation between occipital EEG alpha rhythm, selectively extracted using ICA, and fluctuations in the BOLD effect during an open versus closed eyes and an auditory stimulation versus silence condition. Occipital alpha amplitude is consistent with metabolic changes occurring simultaneously, sites in the medial thalamus and in the anterior midbrain, with about 2.5 sec lag. Goncalves et al.(2005) used simultaneous recording of electroencephalogram/functional magnetic resonance images (EEG/fMRI) to identify blood oxygenation level-dependent (BOLD) changes associated with spontaneous variations of the alpha rhythm, which is considered the hallmark of the brain resting state. They also found the BOLD signal was positively correlated with the alpha power in small thalamic areas, supporting the cortical and subcortical modulation of electroencephalographic alpha rhythm.
A combined PET/EEG study by Schreckenberger et al. also supported the hypothesis of a close functional relationship between thalamic activity and alpha rhythm in humans mediated by corticothalamic loops. (Schreckenberger et al., 2004) This functional corticothalamic loop supports the concept of cortical control of thalamic activity in humans for modulating cortical EEG activity.
Physiological processes that may produce these patterns include several brain systems regulating brain and behavioral arousal and/or valuation judgments of stimulus and other events via the release by midbrain or brainstem neurons of modulatory neurotransmitters – dopamine (DA), acetylcholine (ACh), norepinephrine (NE), serotonin, etc. – through their extensive cortical and thalamic projections (Robbins, 1997; Bardo, 1998).
5.3 The modulator fluctuations from alertness to drowsiness
In this study, we employed independent modulator decomposition to the spectral fluctuations of independent components, and two independent modulators related to performance changes were found in all subjects.
The alpha modulators were very sensitive to the performance changes, so the alpha modulator fluctuates very large during the low LDE periods. Therefore, the alpha modulator in this study might be partially contributed by cortical idling, decreased attentiveness and decline of movements. In the earlier finding (Lee et al., 1999), the frequency of the 8-14 Hz synchronizes in the thalamocortical system during quiet sleep. The EEG fluctuations from low error to high error were similarly during frontal, central-parietal and occipital lobe in the experiment. So that the alpha modulator maybe also modulated by thalamus during drowsiness.
The theta-beta modulators increase monotonically from low LDE to high LDE. Theta rhythm is the EEG characteristic of sleep stage 1 and microsleep (Bear et al., 2001; Gennaro et al., 2001; Thomas et al., 2003). Past studies also reported the fluctuations in the modulation of the beta-wave amplitude related to an indirect measurement of drowsiness (Poupard et al., 2001). Jung et al. (1997) also reported theta and beta band power were high correlated with performance changes.
The correlation coefficient between modulator power change and LDE is shown in Table 4-2. The standard deviation is large because the inter-subject behavior states are varied. Some subjects were involved more period of stage 1 sleep, others included more time of cortical idling, so that results of the independent modulator decomposition depend on drowsiness state.
6. Conclusions
In this study, we model spectral fluctuations of independent components from EEG activations as the actions of independently modulator processes, and report two main classes of spectral modulation patterns, alpha modulator and theta-beta modulator. The theta-beta modulator fluctuates very little during the low LDE periods, and increases monotonically from low LDE to high LDE. Different with the theta-beta band, the alpha modulator fluctuates very large during the low LDE periods. Therefore, the theta-beta modulator is high correlated with performance changes, and alpha modulator in this study might be partially contributed by cortical idling. In our modulation model, these modulators are modulated by the subcortical nucleus or synchronized by cortical-cortical interaction to influence on the rhythmic activations of cortical areas. The neuromodulatory systems can be explored more by the method we propose here.
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