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Near-infrared spectroscopy (NIRS) is commonly used for studying human brain function. However, several studies have shown that superficial hemodynamic changes such as skin blood flow can affect the prefrontal NIRS hemoglobin (Hb) signals. To examine the criterion-related validity of prefrontal NIRS-Hb signals, we focused on the functional signals during a working memory (WM) task and investigated their similarity with blood-oxygen-level-dependent (BOLD) signals simultaneously measured by functional magnetic resonance imaging (fMRI). We also measured the skin blood flow with a laser Doppler flowmeter (LDF) at the same time to examine the effect of superficial hemodynamic changes on the NIRS-Hb signals. Correlation analysis demonstrated that temporal changes in the prefrontal NIRS-Hb signals in the activation area were significantly correlated with the BOLD signals in the gray matter rather than those in the soft tissue or the LDF signals. While care must be taken when comparing the NIRS-Hb signal with the extracranial BOLD or LDF signals, these results suggest that the NIRS-Hb signal mainly reflects hemodynamic changes in the gray matter. Moreover, the amplitudes of the task-related responses of the NIRS-Hb signals were significantly correlated with the BOLD signals in the gray matter across participants, which means participants with a stronger NIRS-Hb response showed a stronger BOLD response. These results thus provide supportive evidence that NIRS can be used to measure hemodynamic signals originating from prefrontal cortex activation.

Reproducibility of experimental results lies at the heart of scientific disciplines. Here we propose a signal processing method that extracts task-related components by maximizing the reproducibility during task periods from neuroimaging data. Unlike hypothesis-driven methods such as general linear models, no specific time courses are presumed, and unlike data-driven approaches such as independent component analysis, no arbitrary interpretation of components is needed. Task-related components are constructed by a linear, weighted sum of multiple time courses, and its weights are optimized so as to maximize inter-block correlations (CorrMax) or covariances (CovMax). Our analysis method is referred to as task-related component analysis (TRCA). The covariance maximization is formulated as a Rayleigh-Ritz eigenvalue problem, and corresponding eigenvectors give candidates of task-related components. In addition, a systematic statistical test based on eigenvalues is proposed, so task-related and -unrelated components are classified objectively and automatically. The proposed test of statistical significance is found to be independent of the degree of autocorrelation in data if the task duration is sufficiently longer than the temporal scale of autocorrelation, so TRCA can be applied to data with autocorrelation without any modification. We demonstrate that simple extensions of TRCA can provide most distinctive signals for two tasks and can integrate multiple modalities of information to remove task-unrelated artifacts. TRCA was successfully applied to synthetic data as well as near-infrared spectroscopy (NIRS) data of finger tapping. There were two statistically significant task-related components; one was a hemodynamic response, and another was a piece-wise linear time course. In summary, we conclude that TRCA has a wide range of applications in multi-channel biophysical and behavioral measurements.

Although functional near-infrared spectroscopy (fNIRS) has an advantage of simultaneously measuring changes in oxy- and deoxy-hemoglobin concentrations (Δ[HbO] and Δ[HbR]), only few analysis approaches exploit this advantage. As an extension of our recently proposed method (task-related component analysis, TRCA), this study proposes a new analysis method that extracts task-related oxygenation and cerebral blood volume (CBV) changes. In the original formulation of TRCA, task-relatedness of a signal is defined as consistent appearance of a same waveform in every task block, thereby constructing task-related components by maximizing inter-block covariance. The new method proposes that, in addition to maximizing inter-block covariance, the covariance between task-related Δ[HbO] and Δ[HbR] is maximized (TRCA(+)) or minimized (TRCA(-)) so that oxygenation and CBV changes are maximally contrasted. The proposed method (collectively called TRCA(±)) was formulated as a matrix eigenvalue problem, which can be solved efficiently with standard numerical methods, and was tested with a synthetic data generated by a balloon model, successfully recovering oxygenation and CBV components. fNIRS data from sensorimotor areas in a finger-tapping task and from prefrontal lobe in a working-memory (WM) task were then analyzed. For both tasks, the time courses and the spatial maps for oxygenation and CBV changes were found to differ consistently, providing certain constraints in the parameters of balloon models. In summary, TRCA can estimate task-related oxygenation and CBV changes simultaneously, thereby extending the applicability of fNIRS.

To examine the correlation between electroencephalography fluctuations (EEGF) and cerebral hemodynamics during sleep after eliminating influences from cardiovascular activity, we simultaneously measured EEGF, the cerebral hemoglobin concentration change, and mean arterial blood pressure (MAP) during the sleep of healthy human adults. The cerebral hemoglobin concentration change was measured at 88 positions covering the whole head, by optical topography. We extracted the intrinsic correlation between EEGF and the cerebral hemoglobin concentration change without MAP contributions through cross-correlation and partial correlation analyses considering time lags. We found that increases in the power of the alpha rhythm in EEGF were correlated with increases in oxygenated hemoglobin (oxy-Hb) and decreases in deoxygenated hemoglobin (deoxy-Hb) and that increases in the power of the sigma rhythm in EEGF were correlated with decreases in oxy-Hb and increases in deoxy-Hb. The former correlations tended to appear in the transition from sleep stage 2 to sleep stage 1, and the latter correlations tended to appear in the transition from sleep stage 1 to sleep stage 2. The former correlations were found in the inferior frontal and middle temporal gyri and the latter correlations were found in the superior frontal, middle frontal, and angular gyri.

We have developed a wavelet-based method of detecting body-movement artifacts in optical topography (OT) signals. Although OT, which is a noninvasive imaging technique for measuring hemodynamic response related to brain activation, is particularly useful for studying infants, the signals occasionally contain undesirable artifacts caused by body movements, so data corrupted by body-movement artifacts must be eliminated to obtain reliable results. For this purpose, we applied a wavelet transform to automatically detect body-movement artifacts in OT signals. We measured OT signals from nine healthy infants in response to speech stimuli. After the continuous signals had been divided into blocks (a block is a time series of OT signal in a 30-s period including a 10-s stimulation period), they were classified into two groups (movement blocks and non-movement blocks) according to whether the participants moved or not by video judgment. Using those data, we developed a wavelet-based algorithm for detecting body-movement artifacts at a high discrimination rate being consistent with the actual body-movement state. The wavelet method has two parameters (scale and threshold), and a Monte Carlo analysis gave the mean optimal parameters as 9 ± 1.9 (mean ± standard deviation) for the scale and as 42.7 ± 1.9 for the threshold. Our wavelet method with the mean optimal parameters (scale = 9, threshold = 43) achieved a higher discrimination rate (mean ± standard deviation: 86.3 ± 8.8%) for actual body movement than a previous method (mean ± standard deviation: 80.6 ± 8.7%) among different participants (paired t test: t(8) = 2.92, p

Functional magnetic resonance imaging (fMRI) is an important tool for noninvasively imaging the hemodynamic responses accompanying brain activity, but fMRI measurements are accompanied by loud acoustic noises resulting from Lorentz forces that cannot be completely excluded when the present technology is used. We used recorded fMRI acoustic noise and examined its effect on sensorimotor activation in optical topography measurement when subjects were instructed to tap the fingers of the right hand under a 23-dB non-noise condition and 46-, 56-, and 65-dB noise conditions. The results showed that the amplitude of the activation signal (relative change in concentration) for oxygenated hemoglobin in the sensorimotor cortex decreased with increasing noise. The activation signal for deoxygenated hemoglobin did not depend significantly on the noise level but did tend to decrease with increasing noise. These results suggest that fMRI acoustic noise affects the hemodynamics of cortical areas associated with the processing of information other than auditory information.

A common issue in blood-related brain-function measurements, such as optical topography, is that the observed signals are usually corrupted with strong noise that is primarily spontaneous low-frequency oscillations (LFOs) in cerebral hemodynamics, which are difficult to separate from the signals due to functional brain activity because of their common spectral range. We discuss the analysis of information transfer between LFOs around 0.1 Hz in the hemoglobin concentration change (HbCC) in the cerebral cortex, the heart rate (HR), and the mean arterial blood pressure (ABP) to understand the origin of spontaneous LFOs in cerebral hemodynamics. As measures of information transfer, we used transfer entropy (TE) for two-variable system analysis and introduced intrinsic transfer entropy for further analysis of three-variable systems by extending the original TE. Data for analysis were obtained from simultaneous measurements with optical topography and infrared finger plethysmography under rest conditions. The analysis revealed that the LFOs in oxy HbCC, a parameter of cerebral hemodynamics, mainly stem from HR, but its contribution is only about 20%. In addition, the intrinsic contribution of ABP is about 5% and the common contribution of HR and ABP is about 10%. From these, HR and ABP cannot account for more than the half the information carried with variable oxy HbCC, which suggests the origin of LFOs in cerebral hemodynamics may lie in the regulation of regional cerebral blood flow change and energetic metabolism rather than due to the systemic regulation of the cardiovascular system.