Recently, functional near-infrared spectroscopy (fNIRS) has been utilized to image the hemodynamic activities and connectivity in the human brain. With the advantage of economic efficiency, portability, and fewer physical constraints, fNIRS enables studying of the human brain at versatile environment and various body positions, including at bed side and during exercise, which complements the use of functional magnetic resonance imaging (fMRI). However, like fMRI, fNIRS imaging can be influenced by the presence of a strong global component. Yet, the nature of the global signal in fNIRS has not been established. In this study, we investigated the relationship between fNIRS global signal and electroencephalogram (EEG) vigilance using simultaneous recordings in resting healthy subjects in high-density and whole-head montage. In Experiment 1, data were acquired at supine, sitting, and standing positions. Results found that the factor of body positions significantly affected the amplitude of the resting-state fNIRS global signal, prominently in the frequency range of 0.05–0.1 Hz but not in the very low frequency range of less than 0.05 Hz. As a control, the task-induced fNIRS or EEG responses to auditory stimuli did not differ across body positions. However, EEG vigilance plays a modulatory role in the fNIRS signals in the frequency range of less than 0.05 Hz: resting-state sessions of low EEG vigilance measures are associated with high amplitudes of fNIRS global signals. Moreover, in Experiment 2, we further examined the epoch-to-epoch fluctuations in concurrent fNIRS and EEG data acquired from a separate group of subjects and found a negative temporal correlation between EEG vigilance measures and fNIRS global signal amplitudes. Our study for the first time revealed that vigilance as a neurophysiological factor modulates the resting-state dynamics of fNIRS, which have important implications for understanding and processing the noises in fNIRS signals.

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A suboptimal solution to constrained linear time varying quadratic regulation (CLTVQR) is proposed. In a neighborhood of the origin, the problem is formulated as a min-max LQR based on polytopic inclusion of the dynamics in this neighborhood. Outside this neighborhood, the control moves are obtained by solving a constrained finite horizon optimization problem. The main contribution is to obtain a cost value arbitrarily close (but not equal) to that of the optimal CLTVQR. The suboptimal CLTVQR preserves the feasibility of the optimal CLTVQR if and only if the min-max LQR exists feasible solution. By mild modification, this suboptimal method can be applied to nonlinear systems.

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Most of the prior studies of functional connectivity in both healthy and diseased brain utilized resting-state functional magnetic resonance imaging (fMRI) as a measure to represent the temporal synchrony in blood oxygenation level dependent (BOLD) signals across brain regions. To eliminate the impact of widely distributed global signal component across the brain, many studies have adopted global signal regression (GSR) as a pre-processing approach to regress the global signal component out of BOLD signals followed by computing hemodynamic connectivity. However, the procedure of global signal regression has been debated as physiologically relevant component may be present in global signal. In this study, we aimed to address the controversy of global signal using functional non-invasive neuroimaging technology, i.e. functional near-infrared spectroscopy (fNIRS), which measures hemodynamic signals by probing local changes in oxygen consumption, a common imaging contrast measured by BOLD fMRI. In the current study, we acquired simultaneous EEG and fNIRS signals, both in high-density configuration and whole-brain coverage, in healthy individuals at eyes-open and eyes-closed resting state and at three different body positions. We explored the underlying relationship between fNIRS global signal and EEG vigilance, and have identified negative correlation between fNIRS global signal and EEG vigilance across the physiological variations of measurements.