Effects of sex and proficiency in second language processing as revealed by a large-scale fNIRS study of school-aged children.

Previous neuroimaging studies in adults have revealed that first and second languages (L1/L2) share similar neural substrates, and that proficiency is a major determinant of the neural organization of L2 in the lexical-semantic and syntactic domains. However, little is known about neural substrates of children in the phonological domain, or about sex differences. Here, we conducted a large-scale study (n = 484) of school-aged children using functional near-infrared spectroscopy and a word repetition task, which requires a great extent of phonological processing. We investigated cortical activation during word processing, emphasizing sex differences, to clarify similarities and differences between L1 and L2, and proficiency-related differences during early L2 learning. L1 and L2 shared similar neural substrates with decreased activation in L2 compared to L1 in the posterior superior/middle temporal and angular/supramarginal gyri for both sexes. Significant sex differences were found in cortical activation within language areas during high-frequency word but not during low-frequency word processing. During high-frequency word processing, widely distributed areas including the angular/supramarginal gyri were activated in boys, while more restricted areas, excluding the angular/supramarginal gyri were activated in girls. Significant sex differences were also found in L2 proficiency-related activation: activation significantly increased with proficiency in boys, whereas no proficiency-related differences were found in girls. Importantly, cortical sex differences emerged with proficiency. Based on previous research, the present results indicate that sex differences are acquired or enlarged during language development through different cognitive strategies between sexes, possibly reflecting their different memory functions.

Task-related oxygenation and cerebral blood volume changes estimated from NIRS signals in motor and cognitive tasks.

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.

Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis.

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S-D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S-D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals.

Direct cortical hemodynamic mapping of somatotopy of pig nostril sensation by functional near-infrared cortical imaging (fNCI).

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for the noninvasive monitoring of human brain activation states utilizing the coupling between neural activity and regional cerebral hemodynamics. Illuminators and detectors, together constituting optodes, are placed on the scalp, but due to the presence of head tissues, an inter-optode distance of more than 2.5cm is necessary to detect cortical signals. Although direct cortical monitoring with fNIRS has been pursued, a high-resolution visualization of hemodynamic changes associated with sensory, motor and cognitive neural responses directly from the cortical surface has yet to be realized. To acquire robust information on the hemodynamics of the cortex, devoid of signal complications in transcranial measurement, we devised a functional near-infrared cortical imaging (fNCI) technique. Here we demonstrate the first direct functional measurement of temporal and spatial patterns of cortical hemodynamics using the fNCI technique. For fNCI, inter-optode distance was set at 5mm, and light leakage from illuminators was prevented by a special optode holder made of a light-shielding rubber sheet. fNCI successfully detected the somatotopy of pig nostril sensation, as assessed in comparison with concurrent and sequential somatosensory-evoked potential (SEP) measurements on the same stimulation sites. Accordingly, the fNCI system realized a direct cortical hemodynamic measurement with a spatial resolution comparable to that of SEP mapping on the rostral region of the pig brain. This study provides an important initial step toward realizing functional cortical hemodynamic monitoring during neurosurgery of human brains.

Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data.

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.

A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task.

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.

Sound to language: different cortical processing for first and second languages in elementary school children as revealed by a large-scale study using fNIRS.

A large-scale study of 484 elementary school children (6-10 years) performing word repetition tasks in their native language (L1-Japanese) and a second language (L2-English) was conducted using functional near-infrared spectroscopy. Three factors presumably associated with cortical activation, language (L1/L2), word frequency (high/low), and hemisphere (left/right), were investigated. L1 words elicited significantly greater brain activation than L2 words, regardless of semantic knowledge, particularly in the superior/middle temporal and inferior parietal regions (angular/supramarginal gyri). The greater L1-elicited activation in these regions suggests that they are phonological loci, reflecting processes tuned to the phonology of the native language, while phonologically unfamiliar L2 words were processed like nonword auditory stimuli. The activation was bilateral in the auditory and superior/middle temporal regions. Hemispheric asymmetry was observed in the inferior frontal region (right dominant), and in the inferior parietal region with interactions: low-frequency words elicited more right-hemispheric activation (particularly in the supramarginal gyrus), while high-frequency words elicited more left-hemispheric activation (particularly in the angular gyrus). The present results reveal the strong involvement of a bilateral language network in children's brains depending more on right-hemispheric processing while acquiring unfamiliar/low-frequency words. A right-to-left shift in laterality should occur in the inferior parietal region, as lexical knowledge increases irrespective of language.

Quantification of systemic interference in optical topography data during frontal lobe and motor cortex activation: an independent component analysis.

Functional near-infrared optical topography (OT) is used to non-invasively measure the changes in oxygenated and deoxygenated haemoglobin (Δ[HbO2], Δ[HHb]) and hence investigate the brain haemodynamic changes, which occur in response to functional activation at specific regions of the cerebral cortex. However, when analysing functional OT data the task-related systemic changes should be taken into account.Here we used an independent component analysis (ICA) method on the OT [HbO2] signal, to determine the task-related independent components and then compared them with the systemic measurements (blood pressure, heart rate, scalp blood flow) to assess whether the components are due to systemic noise or neuronal activation. This analysis can therefore extract the true OT haemodynamic neuronal response and hence discriminate between regional activated cortical areas and global haemodynamic changes.

Anisotropic spatial coherence of ongoing and spontaneous activities in auditory cortex.

We carried out voltage-sensitive dye imaging of the guinea pig auditory cortex to determine whether the ongoing and spontaneous activities of the cortex exhibit spatial coherence reflecting the tonotopic organization of the cortex. We used independent component analysis and a signal-plus-noise model to extract ongoing activities from the observed signals including physiological noise and stimulus-evoked activities. We analyzed the cross-correlations of background activities between all pairs of recording channels and found that ongoing and spontaneous activities in the auditory cortex exhibited anisotropic spatial coherence extending along the isofrequency bands.

High cognitive function of an ALS patient in the totally locked-in state.

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease characterized by progressive degeneration of upper and lower motor neurons. Patients with ALS progressively lose their ability to control voluntary movements and occasionally enter the totally locked-in state (TLS), in which they cannot move any part of their bodies including the eyes. In this study, we clarified the preserved abilities and reorganization of the motor system of a 73-year-old patient with ALS in the TLS using optical topography, a recently developed extension of near-infrared spectroscopy. The patient performed four cognitive tasks: dichotic listening, covert singing, word fluency, and motor imagery. The bilateral prefrontal and bilateral sensorimotor areas were activated during the two language-related tasks (dichotic listening task and the word fluency), the right prefrontal and sensorimotor areas during the covert singing task, and the right prefrontal and dorsal sensorimotor areas during the motor imagery task. Contralateral sensorimotor activation was not observed in the motor imagery task. These results suggest that cognitive functions can be preserved in ALS in the TLS, with sensorimotor areas playing an important role.

Extracting task-related activation components from optical topography measurement using independent components analysis.

Optical topography (OT) signals measured during an experiment that used activation tasks for certain brain functions contain neuronal-activation induced blood oxygenation changes and also physiological changes. We used independent component analysis to separate the signals and extracted components related to brain activation without using any hemodynamic models. The analysis procedure had three stages: first, OT signals were separated into independent components (ICs) by using a time-delayed decorrelation algorithm; second, task-related ICs (TR-ICs) were selected from the separated ICs based on their mean intertrial cross-correlations; and third, the TR-ICs were categorized by k-means clustering into TR activation-related ICs (TR-AICs) and TR noise ICs (TR-NICs). We applied this analysis procedure to the OT signals obtained from experiments using one-handed finger-tapping tasks. In the averaged waveform of the TR-AICs, a small overshoot can be seen for a few seconds after the onset of each task and a few seconds after it ends, and the averaged waveforms of the TR-NICs have an N-shaped pattern.

Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback.

Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.

Adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children.

Functional near-infrared spectroscopy (fNIRS) signals are prone to problems caused by motion artifacts and physiological noises. These noises unfortunately reduce the fNIRS sensitivity in detecting the evoked brain activation while increasing the risk of statistical error. In fNIRS measurements, the repetitive resting-stimulus cycle (so-called block-design analysis) is commonly adapted to increase the sample number. However, these blocks are often affected by noises. Therefore, we developed an adaptive algorithm to identify, reject, and select the noise-free and/or least noisy blocks in accordance with the preset acceptance rate. The main features of this algorithm are personalized evaluation for individual data and controlled rejection to maintain the sample number. Three typical noise criteria (sudden amplitude change, shifted baseline, and minimum intertrial correlation) were adopted. Depending on the quality of the dataset used, the algorithm may require some or all noise criteria with distinct parameters. Aiming for real applications in a pediatric study, we applied this algorithm to fNIRS datasets obtained from attention deficit/hyperactivity disorder (ADHD) children as had been studied previously. These datasets were divided for training and validation purposes. A validation process was done to examine the feasibility of the algorithm regardless of the types of datasets, including those obtained under sample population (ADHD or typical developing children), intervention (nonmedication and drug/placebo administration), and measurement (task paradigm) conditions. The algorithm was optimized so as to enhance reproducibility of previous inferences. The optimum algorithm design involved all criteria ordered sequentially (0.047 mM mm of amplitude change, 0.029 mM mm / s of baseline slope, and 0.6 × interquartile range of outlier threshold for each criterion, respectively) and presented complete reproducibility in both training and validation datasets. Compared to the visual-based rejection as done in the previous studies, the algorithm achieved 71.8% rejection accuracy. This suggests that the algorithm has robustness and potential to substitute for visual artifact-detection.

Erratum: Adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children.

[This corrects the article DOI: 10.1117/1.NPh.5.4.045001.].

Tutorial on platform for optical topography analysis tools.

Optical topography/functional near-infrared spectroscopy (OT/fNIRS) is a functional imaging technique that noninvasively measures cerebral hemoglobin concentration changes caused by neural activities. The fNIRS method has been extensively implemented to understand the brain activity in many applications, such as neurodisorder diagnosis and treatment, cognitive psychology, and psychiatric status evaluation. To assist users in analyzing fNIRS data with various application purposes, we developed a software called platform for optical topography analysis tools (POTATo). We explain how to handle and analyze fNIRS data in the POTATo package and systematically describe domain preparation, temporal preprocessing, functional signal extraction, statistical analysis, and data/result visualization for a practical example of working memory tasks. This example is expected to give clear insight in analyzing data using POTATo. The results specifically show the activated dorsolateral prefrontal cortex is consistent with previous studies. This emphasizes analysis robustness, which is required for validating decent preprocessing and functional signal interpretation. POTATo also provides a self-developed plug-in feature allowing users to create their own functions and incorporate them with established POTATo functions. With this feature, we continuously encourage users to improve fNIRS analysis methods. We also address the complications and resolving opportunities in signal analysis.

Intersubject variability of near-infrared spectroscopy signals during sensorimotor cortex activation.

We investigate the intersubject signal variability of near-infrared spectroscopy (NIRS), which is commonly used for noninvasive measurement of the product of the optical path length and the concentration change in oxygenated hemoglobin (DeltaC'oxy) and deoxygenated hemoglobin (DeltaC'deoxy) and their sum (DeltaC'total) related to human cortical activation. We do this by measuring sensorimotor cortex activation in 31 healthy adults using 24-measurement-position near-infrared (NIR) topography. A finger-tapping task is used to activate the sensorimotor cortex, and significant changes in the hemisphere contralateral to the tapping hand are assessed as being due to the activation. Of the possible patterns of signal changes, 90% include a positive DeltaC'oxy, 76% included a negative DeltaC'deoxy, and 73% included a positive DeltaC'total. The DeltaC'deoxy and DeltaC'total are less consistent because of a large intersubject variability in DeltaC'deoxy; in some cases there is a positive DeltaC'deoxy. In the cases with no positive DeltaC'oxy in the contralateral hemisphere, there are cases of other possible changes for either or both hemispheres and no cases of no change in any hemoglobin species in either hemisphere. These results suggest that NIR topography is useful for observing brain activity in most cases, although intersubject signal variability still needs to be resolved.

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes.

It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level-dependent signals was analyzed. A three-way analysis of variance ([Formula: see text]) indicated that the main effect of fNIRS signal depth on the correlation is significant [[Formula: see text], [Formula: see text]]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method.

Biphasic changes in tissue partial pressure of oxygen closely related to localized neural activity in guinea pig auditory cortex.

An understanding of the local changes in cerebral oxygen content accompanying functional brain activation is critical for making a valid signal interpretation of hemodynamic-based functional brain imaging. However, spatiotemporal relations between changes in tissue partial pressure of oxygen (Po2) and induced neural activity remain incompletely understood. To characterize the local Po2 response to the given neural activity, the authors simultaneously measured tissue Po2 and neural activity in the identical region of guinea pig auditory cortex with an oxygen microelectrode (tip < 10 microm) and optical recording with voltage-sensitive dye (RH 795). In addition, a laser displacement gauge and a laser-Doppler flowmeter were used to monitor the spatial displacement and regional cerebral blood flow, respectively, in the Po2 measurement region. In the activated region, tissue Po2 initially decreased during the approximately 3-seconds after the onset of acoustic stimuli, and then increased during the next approximately 5 seconds. Such biphasic changes are consistently found in cortical layers I to IV. In addition, amplitude of the biphasic change was closely related to detected peak height of the optical signal changes. The results suggest that the initial decrease in tissue Po2 is coupled to the induced neural activity and depends on response time of local increase in cerebral blood flow.

Isolation of neural activities from respiratory and heartbeat noises for in vivo optical recording in guinea pigs using independent component analysis.

Optical recording in vivo is severely interfered by heartbeat and respiratory noises. Here we tested if these noises can be removed from in vivo optical recordings from the primary auditory cortex of the guinea pig, using independent component analysis (ICA). We applied a fast ICA algorithm combined with principal component analysis to optical recordings of long durations (9-40 s). Our results show that ICA can be successfully used to separate sound-evoked neural activities from heartbeat and respiratory noises.

Correlation between prefrontal cortex activity during working memory tasks and natural mood independent of personality effects: an optical topography study.

Interactions between mood and cognition have drawn much attention in the fields of psychology and neuroscience. Recent neuroimaging studies have examined a neural basis of the mood-cognition interaction that which emphasize the role of the prefrontal cortex (PFC). Although these studies have shown that natural mood variations among participants are correlated with PFC activity during cognitive tasks, they did not control for personality differences. Our aim in this study was to clarify the relationship between natural mood and PFC activity by partialling out the effects of personality. Forty healthy adults completed self-report questionnaires assessing natural mood (the Profile of Mood States) and personality (the NEO Five-Factor Inventory and the Behavioral Inhibition/Activation Systems scales). They performed verbal and spatial working memory (WM) tasks while their PFC activity was measured using optical topography, a non-invasive, low-constraint neuroimaging tool. Correlation analysis showed that the level of negative mood was inversely associated with PFC activity during the verbal WM task, which replicated our previous findings. Furthermore, the negative correlation between negative mood and PFC activity remained significant after controlling for participants' personality traits, suggesting that natural mood is an independent contributing factor of PFC activity during verbal WM tasks.