Published papers

This paper presents a novel finding suggesting that tactile exploration behaviors, with a focus on finger scanning motion, are influenced by interoception. Initially, we investigated finger motion for surface scanning across various precisely controlled surface patterns using microfabrication techniques. Participants were then categorized into two distinct groups based on whether their finger scanning speed depended on surface patterns. Subsequently, we assessed participants' interoception through a heart rate counting task. The identified groups correlated with levels of interoceptive accuracy, indicating a significant relationship between interoception and tactile-related behaviors. This discovery could have a profound impact on tactile research, as it suggests the need to consider interoception properties alongside conventional participant demographics such as age and gender in experimental design.

Abstract This longitudinal study investigated the differential impacts of maternal speech on early socio-communicative development in infants at low likelihood (LL) and elevated likelihood (EL) of Autism Spectrum Disorder (ASD). Using functional near-infrared spectroscopy, we measured cortical responses and connectivity in 6-month-old infants while they listened to their mother’s voice and an unfamiliar female voice. LL infants exhibited extensive cortical activation and robust connectivity in temporal and frontal regions, particularly in areas associated with voice processing, reward, and language functions. In contrast, EL infants showed minimal activation and weaker connectivity in these regions. Specifically, LL infants demonstrated significant connectivity between the superior temporal gyrus and the inferior frontal gyrus on the left side, and between the orbitofrontal cortex and language areas, facilitating language processing and reward-related responses to maternal speech. These neural patterns were absent in EL infants, highlighting a neural basis for subsequent language delays. Furthermore, many of these reward-related or language-related networks predicted subsequent language development. Our findings underscore the importance of neural sensitivity to familiar human voices, regarding them as rewards that will eventually facilitate the acquisition of speech.

Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (∆[HbO]) or two deoxy-hemoglobin (∆[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE’s performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDEHbO (STDE based on ∆[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.

© 2017 The Authors. Developmental Psychobiology Published by Wiley Periodical, Inc. While the influence of the mother's voice on neonatal heart-rate response and its relevant activity on cerebral cortex and the autonomic nervous system (ANS) are well known, few studies have assessed its influence on respiratory activity. We investigated the relationship among the respiration rate, the delta wave amplitudes through electroencephalography, and the basal state of ANS through the respiratory variability index while 22 full-term neonates hear their mother's voice and an unknown voice. It was found that when respiratory variability was large, a transient (<5 s) change in respiration rates was observed in response to an unknown voice, while a greater increase in the delta wave amplitude was observed in the frontal lobe than the parietal one in response to the mother's voice. Conversely, when respiratory variability was small, a sustained increase (>10 s) in respiration rates was observed in response to the mother's voice, while a greater increase in the delta wave amplitude was found in both the frontal and parietal lobes. These results suggest that the basal state of ANS influences the latency of increases in respiration rates. Furthermore, induced by the mother's voice, transient increases in respiration rates are reduced in association with frontal lobe activity, and sustained increases in respiration rates are promoted in association with frontal and parietal lobe activities.

We demonstrate capture of event-related potentials (ERPs) using candle-like dry microneedle electrodes (CMEs). CMEs can record an electroencephalogram (EEG) even from hairy areas without any skin preparation, unlike conventional wet electrodes. In our previous research, we experimentally verified that CMEs can measure the spontaneous potential of EEG from the hairy occipital region without preparation with a signal-to-noise ratio as good as that of the conventional wet electrodes which require skin preparation. However, these results were based on frequency-based signals, which are relatively robust compared to noise contamination, and whether CMEs are sufficiently sensitive to capture finer signals remained unclear. Here, we first experimentally verified that CMEs can extract ERPs as good as conventional wet electrodes without preparation. In the auditory oddball tasks using pure tones, P300, which represent ERPs, was extracted with a signal-to-noise ratio as good as that of conventional wet electrodes. CMEs successfully captured perceptual activities. Then, we attempted to investigate cerebral cognitive activity using ERPs. In processing the vowel and prosody in auditory stimuli such as /itta/, /itte/, and /itta?/, laterality was observed that originated from the locations responsible for the process in near-infrared spectroscopy (NIRS) and magnetoencephalography experiments. We simultaneously measured ERPs with CMEs and NIRS in the oddball tasks using the three words. Laterality appeared in NIRS for six of 10 participants, although laterality was not clearly shown in the results, suggesting that EEGs have a limitation of poor spatial resolution. On the other hand, successful capturing of MMN and P300 using CMEs that do not require skin preparation may be readily applicable for real-time applications of human perceptual activities.

Functional near-infrared spectroscopy (fNIRS) is well-suited for hyperscanning in naturalistic situations, offering significant potential for assessing social brain function in everyday life. Previous studies have reported inter-brain synchrony during social interactions and sought to explore its mechanisms by correlating behavioral events with brain signals. However, commonly used regression analyses, such as Generalized Linear Models (GLM), rely on target events hypothesized as explanatory variables. This reliance introduces a dependency on the researcher's assumptions, which can compromise replicability in social neuroscience. While such dependency may be less problematic in strictly controlled experimental paradigms focused on specific hypotheses, it poses significant challenges in naturalistic experiments like social interactions, where numerous events and signals may serve as potential explanatory variables. To address this limitation, we introduced a new approach: signed-normalized mutual information for wavelet transform coherence (WTC-sNMI). This method enables a two-way analysis to evaluate relationships between event sequences and brain synchrony. Through simulations and real-data applications, we evaluated the performance of the proposed method. The results showed that WTC-sNMI analysis performed comparably to regression analysis in detecting both inter-brain synchrony and within-brain synchrony (i.e., brain connectivity). Moreover, applying WTC-sNMI to a tapping dataset revealed the expected patterns of synchrony between the lateral sides of the motor area. These findings validate the effectiveness of WTC-sNMI as a robust two-way analytical tool for studying brain-event relationships.

The past few years have seen an exponential growth of the robot-assisted rehabilitation field and new technological developments allowing the integration of the user's intention through detection of physiological information. The inclusion of motor intention is thought to be promising for motor rehabilitation and to facilitate neuroplasticity potentially by stimulating the cortical circuitry more than, or at least differently from, non-voluntary passive motion. Yet, contrasting results are reported in the literature. We aimed here to investigate the importance of the integration of motor intention on cortical activity using functional near-infrared spectroscopy (fNIRS) by comparing the active use of an assistive exoskeleton targeting the shoulder with passive use and unassisted motion. We recorded the activity of the bilateral frontal and parietal cortices of 20 healthy individuals during an arm raising task. Active robot assistance showed similar activity patterns to unassisted motion with the exception of a greater activation of the prefrontal region. Correlates of intention could be confirmed by an activation of the supplementary motor area in active-assisted and unassisted but not passive condition. Activation of the contralateral primary sensorimotor regions did not differ between passive and active conditions but activity of the ipsilateral hemisphere and secondary regions was reduced during passive motion. Our results provide arguments in favor of the integration of the user's intention through physiological signals for rehabilitation, in favor of the investigation of secondary and ipsilateral regions, and in favor of the use of fNIRS to investigate differences in cortical correlates of passive and active motion.

Quantitative monitoring and measurement of hand motion in children are crucial to support healthy development. Electrical impedance tomography-based tactile sensors, also known as tomographic tactile sensors, provide a promising approach for grasp classification. Our previous study in adults and children demonstrated the feasibility of pinch classification using a cylindrical device equipped with the tomographic tactile sensor. In this study, we developed a new sensing device to classify the power grip and precision grip in children. In order to address concerns that children might lick or swing the device, a cylindrical sensing device was integrated sensor and measurement circuit, incorporated a protective layer for enhanced safety. Seventeen children participated in an experiment to evaluate the feasibility of the grasp classification. The classification features were voltage vectors and reconstructed images obtained from the sensor, and two machine learning methods were used as the classifiers. The average classification accuracy exceeded 85% for both feature types, surpassing the chance level of 50%. These results demonstrate that the basic grasp patterns in children can be accurately classified using a tomographic tactile sensor. This study provides new insights into the future application of grasp motion classification in children.

Significance: The establishment of a light propagation analysis-based scalp-cortex correlation (SCC) between the scalp location of the source-detector (SD) pair and brain regions is essential for measuring functional brain development in the first 2 years of life using functional near-infrared spectroscopy (fNIRS). Aim: We aimed to reveal the optics-based SCC of 0-, 1-, and 2-year-olds (yo) and the suitable SD distance for this age period. Approach: Light propagation analyses using age-appropriate head models were conducted on SD pairs at 10-10 fiducial points on the scalp to obtain optics-based SCC and its metrics: the number of corresponding brain regions ( N C B R ), selectivity and sensitivity of the most likely corresponding brain region (MLCBR), and consistency of the MLCBR across developmental ages. Moreover, we assessed the suitable SD distances for 0-, 1-, and 2-yo by simultaneously considering the selectivity and sensitivity of the MLCBR. Results: Age-related changes in the SCC metrics were observed. For instance, the N C B R of 0-yo was larger than that of 1- and 2-yo. Conversely, the selectivity of 0-yo was lower than that of 1- and 2-yo. The sensitivity of 1-yo was higher than that of 0-yo at 15- to 30-mm SD distances and higher than that of 2-yo at 10-mm SD distance. Notably, the MLCBR of the fiducial points around the longitudinal fissure was inconsistent across age groups. An SD distance between 15 and 25 mm was found to be appropriate for satisfying both sensitivity and selectivity requirements. In addition, this work provides reference tables of optics-based SCC for 0-, 1-, and 2-yo. Conclusions: Optics-based SCC will be informative in designing and explaining child developmental studies using fNIRS. The suitable SD distances were between 15 and 25 mm for the first 2 years of life.

Abstract Background Among preterm infants, higher morbidities of neurological disturbances and developmental delays are critical issues. Resting-state networks (RSNs) in the brain are suitable measures for assessing higher-level neurocognition. Since investigating task-related brain activity is difficult in neonates, assessment of RSNs provides invaluable insight into their neurocognitive development. Methods The participants, 32 term and 71 preterm neonates, were divided into three groups based on gestational age (GA) at birth. Cerebral hemodynamic activity of RSNs was measured using functional near-infrared spectroscopy in the temporal, frontal, and parietal regions. Results High-GA preterm infants (GA ≥ 30 weeks) had a significantly stronger RSN than low-GA preterm infants and term infants. Regression analyses of RSNs as a function of postnatal age (PNA) revealed a steeper regression line in the high-GA preterm and term infants than in the low-GA infants, particularly for inter-area brain connectivity between the frontal and left temporal areas. Conclusions Slower PNA-dependent development of the frontal–temporal network found only in the low-GA group suggests that significant brain growth optimal in the intrauterine environment takes place before 30 weeks of gestation. The present study suggests a likely reason for the high incidence of neurodevelopmental impairment in early preterm infants. Impact Resting-state fNIRS measurements in three neonate groups differing in gestational age (GA) showed stronger networks in the high-GA preterm infants than in the term and low-GA infants, which was partly explained by postnatal age (PNA). Regression analyses revealed a similar PNA-dependence in the development of the inter-area networks in the frontal and temporal lobes in the high-GA and term infants, and significantly slower development in the low-GA infants. These results suggest that optimal intrauterine brain growth takes place before 30 weeks of gestation. This explains one of the reasons for the high incidence of neurodevelopmental impairment in early preterm infants.