Hemodynamic correlates of cognition in human infants

From behavioral synchrony to language and beyond

Decades of research on joint attention, coordinated joint engagement, and social contingency identify caregiver-child interaction in infancy as a foundation for language. These patterns of early behavioral synchrony contribute to the structure and connectivity of the brain in the temporoparietal regions typically associated with language skills. Thus, children attune to their communication partner and subsequently build cognitive skills directly relating to comprehension and production of language, literacy skills, and beyond. This has yielded marked interest in measuring this contingent, synchronous social behavior neurally. Neurological measures of early social interactions between caregiver and child have become a hotbed for research. In this paper, we review that research and suggest that these early neural couplings between adults and children lay the foundation for a broader cognitive system that includes attention, problem solving, and executive function skills. This review describes the role of behavioral synchrony in language development, asks what the relationship is between neural synchrony and language growth, and how neural synchrony may play a role in the development of a broader cognitive system founded in a socially-gated brain. We address the known neural correlates of these processes with an emphasis on work that examines the tight temporal contingency between communicative partners during these rich social interactions, with a focus on EEG and fNIRS and brief survey of MRI and MEG.

Functional near-infrared spectroscopy and language development: An integrative review

Functional near-infrared spectroscopy (fNIRS) stands poised to revolutionize our understanding of auditory detection, speech perception, and language development in infants. In this study, we conducted a meticulous integrative review across Medline, Scopus, and LILACS databases, targeting investigations utilizing fNIRS to explore language-related features and cortical activation during auditory stimuli in typical infants. We included studies that used the NIRS technique to study language and cortical activation in response to auditory stimuli in typical infants between 0 and 3 years old. We used the ROBINS-I tool to assess the quality and the risk of bias in the studies. Our analysis, encompassing 66 manuscripts, is presented in standardized tables for streamlined data extraction. We meticulously correlated findings with children's developmental stages, delineating crucial insights into brain development and its intricate interplay with language outcomes. Although most studies have a high risk for overall bias, especially due to the high loss of data in NIRS studies, the low risk in the other domains is predominant and homogeneous among the studies. Highlighted are the unique advantages of fNIRS for pediatric studies, underscored by its innate suitability for use in children. This review accentuates fNIRS' capacity to elucidate the neural correlates of language processing and the sequential steps of language acquisition. From birth, infants exhibit abilities that lay the foundation for language development. The progression from diffuse to specific neural activation patterns is extremely influenced by exposure to languages, social interaction, and prosodic features and, reflects the maturation of brain networks involved in language processing. In conclusion, fNIRS emerges as an indispensable functional imaging modality, providing insights into the temporal dynamics of language acquisition and associated developmental milestones. This synthesis presents the pivotal role of fNIRS in advancing our comprehension of early language development and paves the way for future research endeavors in this domain.

ninjaNIRS: an open hardware solution for wearable whole-head high-density functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) technology has been steadily advancing since the first measurements of human brain activity over 30 years ago. Initially, efforts were focused on increasing the channel count of fNIRS systems and then to moving from sparse to high density arrays of sources and detectors, enhancing spatial resolution through overlapping measurements. Over the last ten years, there have been rapid developments in wearable fNIRS systems that place the light sources and detectors on the head as opposed to the original approach of using fiber optics to deliver the light between the hardware and the head. The miniaturization of the electronics and increased computational power continues to permit impressive advances in wearable fNIRS systems. Here we detail our design for a wearable fNIRS system that covers the whole head of an adult human with a high-density array of 56 sources and up to 192 detectors. We provide characterization of the system showing that its performance is among the best in published systems. Additionally, we provide demonstrative images of brain activation during a ball squeezing task. We have released the hardware design to the public, with the hope that the community will build upon our foundational work and drive further advancements.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings

Significance

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge.

Aim

The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions.

Approach

The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking.

Results

Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3    pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.

Conclusions

To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18    g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings

Significance

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge.

Aim

The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions.

Approach

The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking.

Results

Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.

Conclusions

To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Detection of Astronaut's Stress Levels During 240-Day Confinement using EEG Signals and Machine Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-6. [PMID: 38082733 DOI: 10.1109/embc40787.2023.10340035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Early detection of mental stress is particularly important in prolonged space missions. In this study, we propose utilizing electroencephalography (EEG) with multiple machine learning models to detect elevated stress levels during a 240-day confinement. We quantified the levels of stress using alpha amylase levels, reaction time (RT) to stimuli, accuracy of target detection, and functional connectivity of EEG estimated by Phase Locking Value (PLV). Our results show that, alpha amylase level increased every 60-days (with 0.76 correlation) In-mission resulting in four elevated levels of stress. The RT and accuracy of target detection did not show any significant difference with time In-mission. The functional connectivity network showed different patterns between the frontal/occipital with other regions, and parietal to central region. The machine learning classifiers differentiate between four levels of stress with classification accuracy of 91.8%, 91.4%, 90.2%, 87.8, and 81% using linear discriminate analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (KNN), Naïve bayes (NB) and decision trees (DT). Our results suggest that EEG and machine learning can be used to detect elevated levels of mental stress in isolation and confined environments.

Associations between gait performance and pain intensity, psychosocial factors, executive functions as well as prefrontal cortex activity in chronic low back pain patients: A cross-sectional fNIRS study

Introduction

Activities of daily living, such as walking, are impaired in chronic low back pain (CLBP) patients compared to healthy individuals. Thereby, pain intensity, psychosocial factors, cognitive functioning and prefrontal cortex (PFC) activity during walking might be related to gait performance during single and dual task walking (STW, DTW). However, to the best of our knowledge, these associations have not yet been explored in a large sample of CLBP patients.

Method

Gait kinematics (inertial measurement units) and PFC activity (functional near-infrared spectroscopy) during STW and DTW were measured in 108 CLBP patients (79 females, 29 males). Additionally, pain intensity, kinesiophobia, pain coping strategies, depression and executive functioning were quantified and correlation coefficients were calculated to determine the associations between parameters.

Results

The gait parameters showed small correlations with acute pain intensity, pain coping strategies and depression. Stride length and velocity during STW and DTW were (slightly to moderately) positively correlated with executive function test performance. Specific small to moderate correlations were found between the gait parameters and dorsolateral PFC activity during STW and DTW.

Conclusion

Patients with higher acute pain intensity and better coping skills demonstrated slower and less variable gait, which might reflect a pain minimization strategy. Psychosocial factors seem to play no or only a minor role, while good executive functions might be a prerequisite for a better gait performance in CLBP patients. The specific associations between gait parameters and PFC activity during walking indicate that the availability and utilization of brain resources are crucial for a good gait performance.

Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges

Significance

Functional near-infrared spectroscopy (fNIRS) is a frequently used neuroimaging tool to explore the developing brain, particularly in infancy, with studies spanning from birth to toddlerhood (0 to 2 years). We provide an overview of the challenges and opportunities that the developmental fNIRS field faces, after almost 25 years of research.

Aim

We discuss the most recent advances in fNIRS brain imaging with infants and outlines the trends and perspectives that will likely influence progress in the field in the near future.

Approach

We discuss recent progress and future challenges in various areas and applications of developmental fNIRS from methodological and technological innovations to data processing and statistical approaches.

Results and Conclusions

The major trends identified include uses of fNIRS "in the wild," such as global health contexts, home and community testing, and hyperscanning; advances in hardware, such as wearable technology; assessment of individual variation and developmental trajectories particularly while embedded in studies examining other environmental, health, and context specific factors and longitudinal designs; statistical advances including resting-state network and connectivity, machine learning and reproducibility, and collaborative studies. Standardization and larger studies have been, and will likely continue to be, a major goal in the field, and new data analysis techniques, statistical methods, and collaborative cross-site projects are emerging.

Nested and robust modeling techniques for fNIRS data with demographics and experiment related factors in n-back task

Functional near-infrared spectroscopy (fNIRS) signals are used to measure relative changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Brain response studies constitute multilevel or nested datasets formed by different parts of the brain of individuals and multidimensional datasets. The changes in brain activities under specific stimuli are investigated with the help of statistical analysis. However, these studies ignore the dependence structure between the repeated measures of the same subject, which may cause inaccurate or incomplete findings. In this study, we adopt an advanced statistical method into HbO data controlling for variability within repeated measures of each subject while testing and measuring the degrees of the statistical significance between-subject factors and explanatory variables. The changes in HbO are investigated through a linear mixed model, taking experimental and demographic variables into account with open access neuroscience data. The channels nested within subjects are considered random to capture the differences among individuals. Our findings reveal that n-back conditions and mean response times of the subjects have statistically significant associations with mean HbO.

Neural mechanisms underlying rule selection based on response evaluation: a near-infrared spectroscopy study

The ability of humans to use rules for organizing action demands a high level of executive control. Situational complexity mediates rule selection, from the adoption of a given rule to the selection of complex rules to achieve an appropriate response. Several rules have been proposed to be superordinate to human behavior in a cognitive hierarchy and mediated by different brain regions. In the present study, using a novel rule-selection task based on pre-response evaluations that require several cognitive operations, we examined whether the task is mediated by a specific region of the prefrontal cortex using near-infrared spectroscopy. We showed that the selection of rules, including prior evaluation of a stimulus, activates broader areas of the prefrontal and premotor regions than response selection based on a given rule. The results are discussed in terms of hierarchical cognitive models, the functional specialization of multiple-cognitive operations in the prefrontal cortex, and their contribution to a novel cognitive task.

Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising

SIGNIFICANCE

Functional near-infrared spectroscopy (fNIRS) for resting-state neonatal brain function evaluation provides assistance for pediatricians in diagnosis and monitoring treatment outcomes. Artifact contamination is an important challenge in the application of fNIRS in the neonatal population.

AIM

Our study aims to develop a correction algorithm that can effectively remove different types of artifacts from neonatal data.

APPROACH

In the study, we estimate the recognition threshold based on the amplitude characteristics of the signal and artifacts. After artifact recognition, Spline and Gaussian replacements are used separately to correct the artifacts. Various correction method recovery effects on simulated artifact and actual neonatal data are compared using the Pearson correlation ( R ) and root mean square error (RMSE). Simulated data connectivity recovery is used to compare various method performances.

RESULTS

The neonatal resting-state data corrected by our method showed better agreement with results by visual recognition and correction, and significant improvements ( R = 0.732 ± 0.155 , RMSE = 0.536 ± 0.339 ; paired t -test, ** p < 0.01 ). Moreover, the method showed a higher degree of recovery of connectivity in simulated data.

CONCLUSIONS

The proposed algorithm corrects artifacts such as baseline shifts, spikes, and serial disturbances in neonatal fNIRS data quickly and more effectively. It can be used for preprocessing in clinical applications of neonatal fNIRS brain function detection.

Predictive brain signals mediate association between shared reading and expressive vocabulary in infants

The ability to predict upcoming information is crucial for efficient language processing and enables more rapid language learning. The present study explored how shared reading experience influenced predictive brain signals and expressive vocabulary of 12-month-old infants. The predictive brain signals were measured by fNIRS responses in the occipital lobe with an unexpected visual-omission task. The amount of shared reading experience was correlated with the strength of this predictive brain signal and with infants' expressive vocabulary. Importantly, the predictive brain signal explained unique variance of expressive vocabulary beyond shared reading experience and maternal education. A further mediation analysis showed that the effect of shared reading experience on expressive vocabulary was explained by the infants' predictive brain signal. This is the first evidence indicating that richer shared reading experience strengthens predictive signals in the infant brain and in turn facilitates expressive vocabulary acquisition.

Transcutaneous electrical acupoint stimulation for children with attention-deficit/hyperactivity disorder: a randomized clinical trial

Little is known about the effects of transcutaneous electrical acupoint stimulation (TEAS) for children with attention-deficit/hyperactivity disorder (ADHD). Here, we carried out a 4 week randomized clinical trial in which patients aged 6-12 years old with an ADHD diagnosis received TEAS or sham TEAS. The primary outcome measure was the investigator-rated Clinical Global Impression-Improvement (CGI-I) score at week 4. Secondary outcomes included changes from baseline to week 4 in the investigator-rated Clinical Global Impression-Severity of Illness (CGI-S) score, the Conners' Parent/Teacher Rating Scales-Revised: Short Form (CPRS-R: S/CTRS-R: S) score, go/no-go task performance, and functional near-infrared spectroscopy (fNIRS)-based oxygenated hemoglobin level within the prefrontal cortex. At week 4, the CGI-I score indicated improvement in 33.3% of the TEAS group compared with 7.7% of the sham group (P = 0.005). The TEAS group had a greater decrease in the mean CGI-S score (-0.87) than the sham TEAS group (-0.28) (P = 0.003). A greater enhancement in the mean cerebral oxygenated hemoglobin within the prefrontal cortex was found in the TEAS group (0.099 mM mm) compared with the sham TEAS group (0.005 mM mm) (P < 0.001). CPRS-R: S score, CTRS-R: S score, and go/no-go performance exhibited no significant improvement after TEAS treatment. The manipulation-associated adverse events were uncommon in both groups, and events were very mild. Our results show that noninvasive TEAS significantly improved general symptoms and increased prefrontal cortex blood flow within 4 weeks for children with ADHD. Further clinical trials are required to understand the long-term efficacy in a larger clinical sample. This trial was registered on ClinicalTrials.gov (NCT03917953).

Cortical activation and functional connectivity during the verbal fluency task for adolescent-onset depression: A multi-channel NIRS study

OBJECTIVE

Depression disorder is accompanied by cognitive impairments. However, there is limited research focused on cognitive impairments and their neurological mechanism in adolescents with depression. The purpose of the current study is to illustrate the differences in brain activity patterns between depressed adolescents and healthy controls (HCs).

METHOD

A total of 72 adolescents with depression, as well as 74 HCs, were recruited. We utilized functional near-infrared spectroscopy (fNIRS) to monitor the concentrations of oxyhemoglobin (Oxy-Hb) in the brains of participants while they performed the verbal fluency task (VFT) to examine cognitive impairment in adolescents with depression.

RESULTS

Our study demonstrated that adolescents with depression had significantly less cortical activation in the hemodynamic responses of Oxy-Hb at channels mainly located in the prefrontal cortex (PFC) than HCs during the 60-s task period (false discovery rate (FDR)-corrected p < 0.05). The mean channel-to-channel connectivity was 0.400 for HCs (SD = 0.149) and 0.303 (SD = 0.138) for adolescents with depression, and the HC group had a higher mean channel-to-channel connectivity strength than the depression group (t = -15.586, p < 0.001). For the patient group, we found significant negative correlations between HAMD scores and mean Oxy-Hb changes in Channel 38 (r = -0.33, p < 0.01), Channel 39 (r = -0.34, p < 0.01), Channel 41 (r = -0.25, p < 0.05), Channel 42 (r = -0.28, p < 0.05), and Channel 44 (r = -0.27, p < 0.05), and these channels were mainly located in areas with little difference between groups.

CONCLUSIONS

Our study provides neurological evidence about the executive function (EF) in depressed adolescents. Adolescents with depression exhibited an abnormal activation pattern and decreased task-related functional connectivity compared to HCs. The changed Oxy-Hb concentration of PFC during VFT was not sensitive to depression symptoms.

Towards a model of language neurobiology in early development

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes

Significance

The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe's performance a daunting task.

Aim

We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes.

Approach

Our software-modular optode configuration analyzer (MOCA)-implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities.

Results

Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts.

Conclusion

Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.

Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies

This study presents the implementation of a within-subject classification method, based on the use of Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), for the classification of hemodynamic responses. Using a synthetic dataset that closely resembles real experimental infant functional near-infrared spectroscopy (fNIRS) data, the impact of different levels of noise and different HRF amplitudes on the classification performances of the two classifiers are quantitively investigated.

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Comparing different pre-processing routines for infant fNIRS data

Functional Near Infrared Spectroscopy (fNIRS) is an important neuroimaging technique in cognitive developmental neuroscience. Nevertheless, there is no general consensus yet about best pre-processing practices. This issue is highly relevant, especially since the development and variability of the infant hemodynamic response (HRF) is not fully known. Systematic comparisons between analysis methods are thus necessary. We investigated the performance of five different pipelines, selected on the basis of a systematic search of the infant NIRS literature, in two experiments. In Experiment 1, we used synthetic data to compare the recovered HRFs with the true HRF and to assess the robustness of each method against increasing levels of noise. In Experiment 2, we analyzed experimental data from a published study, which assessed the neural correlates of artificial grammar processing in newborns. We found that with motion artifact correction (as opposed to rejection) a larger number of trials were retained, but HRF amplitude was often strongly reduced. By contrast, artifact rejection resulted in a high exclusion rate but preserved adequately the characteristics of the HRF. We also found that the performance of all pipelines declined as the noise increased, but significantly less so than if no pre-processing was applied. Finally, we found no difference between running the pre-processing on optical density or concentration change data. These results suggest that pre-processing should thus be optimized as a function of the specific quality issues a give dataset exhibits.

Correlating functional near-infrared spectroscopy with underlying cortical regions of 0-, 1-, and 2-year-olds using theoretical light propagation analysis

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.

Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.

Re-imagining fMRI for awake behaving infants

Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts to address these challenges, with the goal of creating methods for awake infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore and evaluate data quantity and quality, task-evoked activity, and preprocessing decisions. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and eye-gaze monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in awake and behaving human infants.

Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review

Purpose Functional brain imaging is playing an increasingly important role in the diagnosis and treatment of communication disorders, yet many populations and settings are incompatible with functional magnetic resonance imaging and other commonly used techniques. We conducted a systematic review of neuroimaging studies using functional near-infrared spectroscopy (fNIRS) with individuals with speech or language impairment across the life span. We aimed to answer the following question: To what extent has fNIRS been used to investigate the neural correlates of speech-language impairment? Method This systematic review was preregistered with PROSPERO, the international prospective register of systematic reviews (CRD42019136464). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for preferred reporting items for systematic reviews. The database searches were conducted between February and March of 2019 with the following search terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Results We found 34 fNIRS studies that involved individuals with speech or language impairment across nine categories: (a) autism spectrum disorders; (b) developmental speech and language disorders; (c) cochlear implantation and deafness; (d) dementia, dementia of the Alzheimer's type, and mild cognitive impairment; (e) locked-in syndrome; (f) neurologic speech disorders/dysarthria; (g) stroke/aphasia; (h) stuttering; and (i) traumatic brain injury. Conclusions Though it is not without inherent challenges, fNIRS may have advantages over other neuroimaging techniques in the areas of speech and language impairment. fNIRS has clinical applications that may lead to improved early and differential diagnosis, increase our understanding of response to treatment, improve neuroprosthetic functioning, and advance neurofeedback.

Oh, Behave!: PRESIDENTIAL ADDRESS, XXth International Conference on Infant Studies New Orleans, LA, US May 2016

Behavior is essential for understanding infant learning and development. Although behavior is transient and ephemeral, we have the technology to make it tangible and enduring. Video uniquely captures and preserves the details of behavior and the surrounding context. By sharing videos for documentation and data reuse, we can exploit the tremendous opportuni-ties provided by infancy research and overcome the important challenges in studying behavior. The Datavyu video coding software and Databrary digital video library provide tools and infrastructure for mining and sharing the richness of video. This article is based on my Presidential Address to the International Congress on Infant Studies in New Orleans, May 22, 2016 (Video 1 at https://www.databrary.org/volume/955/slot/39352/-?asset=190106. Given that the article de-scribes the power of video for understanding behavior, I use video clips rather than static images to illustrate most of my points, and the videos are shared on the Databrary library.

Video-based motion-resilient reconstruction of three-dimensional position for functional near-infrared spectroscopy and electroencephalography head mounted probes

Significance: We propose a video-based, motion-resilient, and fast method for estimating the position of optodes on the scalp. Aim: Measuring the exact placement of probes (e.g., electrodes and optodes) on a participant's head is a notoriously difficult step in acquiring neuroimaging data from methods that rely on scalp recordings (e.g., electroencephalography and functional near-infrared spectroscopy) and is particularly difficult for any clinical or developmental population. Existing methods of head measurements require the participant to remain still for a lengthy period of time, are laborious, and require extensive training. Therefore, a fast and motion-resilient method is required for estimating the scalp location of probes. Approach: We propose an innovative video-based method for estimating the probes' positions relative to the participant's head, which is fast, motion-resilient, and automatic. Our method builds on capitalizing the advantages and understanding the limitations of cutting-edge computer vision and machine learning tools. We validate our method on 10 adult subjects and provide proof of feasibility with infant subjects. Results: We show that our method is both reliable and valid compared to existing state-of-the-art methods by estimating probe positions in a single measurement and by tracking their translation and consistency across sessions. Finally, we show that our automatic method is able to estimate the position of probes on an infant head without lengthy offline procedures, a task that has been considered challenging until now. Conclusions: Our proposed method allows, for the first time, the use of automated spatial co-registration methods on developmental and clinical populations, where lengthy, motion-sensitive measurement methods routinely fail.

A Computational Role for Top-Down Modulation from Frontal Cortex in Infancy

Recent findings have shown that full-term infants engage in top-down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top-down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top-down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top-down signals in young infants when they are engaged in a probabilistic associative learning.

Neuroimaging the sleeping brain: Insight on memory functioning in infants and toddlers

Episodic memory, or the ability to remember past events with specific detail, is central to the human experience and is related to learning and adaptive functioning in a variety of domains. In typically developing children, episodic memory emerges during infancy and improves during early childhood and beyond. Developmental processes within the hippocampus are hypothesized to be primarily responsible for both the early emergence and persistence of episodic memory in late infancy and early childhood. However, these hypotheses are based on non-human models. In-vivo investigations in early human development of hippocampal processes have been significantly limited by methodological challenges in acquiring neuroimaging data, particularly task-related functional neuroimaging data, from infants and toddlers. Recent studies in adults have shown neural activity in the brain regions supporting episodic memory during slow-wave sleep using functional magnetic resonance imaging (fMRI), and fMRI has been increasingly utilized in infancy and early childhood to address other research questions. We review initial evidence and present preliminary data showing the promise of this approach for examining hippocampal contribution to how infants and toddlers remember individual events, and their association with information about the context in which the event occurred. Overall, our review, integrated with the presentation of some preliminary data provides insight on leveraging sleep to gain new perspectives on early memory functioning.

Language Experience Impacts Brain Activation for Spoken and Signed Language in Infancy: Insights From Unimodal and Bimodal Bilinguals

Recent neuroimaging studies suggest that monolingual infants activate a left-lateralized frontotemporal brain network in response to spoken language, which is similar to the network involved in processing spoken and signed language in adulthood. However, it is unclear how brain activation to language is influenced by early experience in infancy. To address this question, we present functional near-infrared spectroscopy (fNIRS) data from 60 hearing infants (4 to 8 months of age): 19 monolingual infants exposed to English, 20 unimodal bilingual infants exposed to two spoken languages, and 21 bimodal bilingual infants exposed to English and British Sign Language (BSL). Across all infants, spoken language elicited activation in a bilateral brain network including the inferior frontal and posterior temporal areas, whereas sign language elicited activation in the right temporoparietal area. A significant difference in brain lateralization was observed between groups. Activation in the posterior temporal region was not lateralized in monolinguals and bimodal bilinguals, but right lateralized in response to both language modalities in unimodal bilinguals. This suggests that the experience of two spoken languages influences brain activation for sign language when experienced for the first time. Multivariate pattern analyses (MVPAs) could classify distributed patterns of activation within the left hemisphere for spoken and signed language in monolinguals (proportion correct = 0.68; p = 0.039) but not in unimodal or bimodal bilinguals. These results suggest that bilingual experience in infancy influences brain activation for language and that unimodal bilingual experience has greater impact on early brain lateralization than bimodal bilingual experience.

Using a Dance Mat to Assess Inhibitory Control of Foot in Young Children

The development of motor response inhibition is critical during preschool years and has been associated with an improvement in gross motor coordination in this population. However, the assessment of inhibitory abilities in young children is challenging in terms of task selection and subject engagement, especially when investigating foot responses. Thus, the aim of this study was to describe a child-friendly Go/No-go paradigm to assess inhibitory control of foot based on a dance mat protocol. In this method, Go and No-go stimuli are modeled in the context of a fishing game, and behavioral responses are assessed by recording the latency to touch the mat and the accuracy of the touches. In this protocol article, we (1) describe the stages of the experimental set-up, (2) provide an illustrative data collection example in a sample of children aged 3–4 years, and (3) describe how to process the data generated. The utilization of the dance mat provides a feasible tool for researchers interested in studying the development of motor inhibitory control of foot in preschoolers. Potential applications of this protocol may include studies on developmental differences between hand and foot specialization, sports-related performance and neuroimaging.

Early adversity in rural India impacts the brain networks underlying visual working memory

There is a growing need to understand the global impact of poverty on early brain and behavioural development, particularly with regard to key cognitive processes that emerge in early development. Although the impact of adversity on brain development can trap children in an intergenerational cycle of poverty, the massive potential for brain plasticity is also a source of hope: reliable, accessible, culturally agnostic methods to assess early brain development in low resource settings might be used to measure the impact of early adversity, identify infants for timely intervention and guide the development and monitor the effectiveness of early interventions. Visual working memory (VWM) is an early marker of cognitive capacity that has been assessed reliably in early infancy and is predictive of later academic achievement in Western countries. Here, we localized the functional brain networks that underlie VWM in early development in rural India using a portable neuroimaging system, and we assessed the impact of adversity on these brain networks. We recorded functional brain activity as young children aged 4-48 months performed a VWM task. Brain imaging results revealed localized activation in the frontal cortex, replicating findings from a Midwestern US sample. Critically, children from families with low maternal education and income showed weaker brain activity and poorer distractor suppression in canonical working memory areas in the left frontal cortex. Implications of this work are far-reaching: it is now cost-effective to localize functional brain networks in early development in low-resource settings, paving the way for novel intervention and assessment methods.

A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

This mini-review is aimed at briefly summarizing the present status of functional near-infrared spectroscopy (fNIRS) and predicting where the technique should go in the next decade. This mini-review quotes 33 articles on the different fNIRS basics and technical developments and 44 reviews on the fNIRS applications published in the last eight years. The huge number of review articles about a wide spectrum of topics in the field of cognitive and social sciences, functional neuroimaging research, and medicine testifies to the maturity achieved by this non-invasive optical vascular-based functional neuroimaging technique. Today, fNIRS has started to be utilized on healthy subjects while moving freely in different naturalistic settings. Further instrumental developments are expected to be done in the near future to fully satisfy this latter important aspect. In addition, fNIRS procedures, including correction methods for the strong extracranial interferences, need to be standardized before using fNIRS as a clinical tool in individual patients. New research avenues such as interactive neurosciences, cortical activation modulated by different type of sport performance, and cortical activation during neurofeedback training are highlighted.

Newborns are sensitive to multiple cues for word segmentation in continuous speech

Before infants can learn words, they must identify those words in continuous speech. Yet, the speech signal lacks obvious boundary markers, which poses a potential problem for language acquisition (Swingley, Philos Trans R Soc Lond. Series B, Biol Sci 364(1536), 3617-3632, 2009). By the middle of the first year, infants seem to have solved this problem (Bergelson & Swingley, Proc Natl Acad Sci 109(9), 3253-3258, 2012; Jusczyk & Aslin, Cogn Psychol 29, 1-23, 1995), but it is unknown if segmentation abilities are present from birth, or if they only emerge after sufficient language exposure and/or brain maturation. Here, in two independent experiments, we looked at two cues known to be crucial for the segmentation of human speech: the computation of statistical co-occurrences between syllables and the use of the language's prosody. After a brief familiarization of about 3 min with continuous speech, using functional near-infrared spectroscopy, neonates showed differential brain responses on a recognition test to words that violated either the statistical (Experiment 1) or prosodic (Experiment 2) boundaries of the familiarization, compared to words that conformed to those boundaries. Importantly, word recognition in Experiment 2 occurred even in the absence of prosodic information at test, meaning that newborns encoded the phonological content independently of its prosody. These data indicate that humans are born with operational language processing and memory capacities and can use at least two types of cues to segment otherwise continuous speech, a key first step in language acquisition.

Distinct Methylphenidate-Evoked Response Measured Using Functional Near-Infrared Spectroscopy During Go/No-Go Task as a Supporting Differential Diagnostic Tool Between Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Comorbid Children

Attention deficit/hyperactivity disorder (ADHD) has been frequently reported as co-occurring with autism spectrum disorder (ASD). However, ASD-comorbid ADHD is difficult to diagnose since clinically significant symptoms are similar in both disorders. Therefore, we propose a classification method of differentially recognizing the ASD-comorbid condition in ADHD children. The classification method was investigated based on functional brain imaging measured by near-infrared spectroscopy (NIRS) during a go/no-go task. Optimization and cross-validation of the classification method was carried out in medicated-naïve and methylphenidate (MPH) administered ADHD and ASD-comorbid ADHD children (randomized, double-blind, placebo-controlled, and crossover design) to select robust parameters and cut-off thresholds. The parameters could be defined as either single or averaged multi-channel task-evoked activations under an administration condition (i.e., pre-medication, post-MPH, and post-placebo). The ADHD children were distinguished by significantly high MPH-evoked activation in the right hemisphere near the midline vertex. The ASD-comorbid ADHD children tended to have low activation responses in all regions. High specificity (86 ± 4.1%; mean ± SD), sensitivity (93 ± 7.3%), and accuracy (82 ± 1.6%) were obtained using the activation of oxygenated-hemoglobin concentration change in right middle frontal, angular, and precentral gyri under MPH medication. Therefore, the significantly differing MPH-evoked responses are potentially effective features and as supporting differential diagnostic tools.

A conceptual space for EEG-based brain-computer interfaces

Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.

Strengthening the Brain-Is Resistance Training with Blood Flow Restriction an Effective Strategy for Cognitive Improvement?

Aging is accompanied by a decrease in physical capabilities (e.g., strength loss) and cognitive decline. The observed bidirectional relationship between physical activity and brain health suggests that physical activities could be beneficial to maintain and improve brain functioning (e.g., cognitive performance). However, the exercise type (e.g., resistance training, endurance training) and their exercise variables (e.g., load, duration, frequency) for an effective physical activity that optimally enhance cognitive performance are still unknown. There is growing evidence that resistance training induces substantial brain changes which contribute to improved cognitive functions. A relative new method in the field of resistance training is blood flow restriction training (BFR). While resistance training with BFR is widely studied in the context of muscular performance, this training strategy also induces an activation of signaling pathways associated with neuroplasticity and cognitive functions. Based on this, it seems reasonable to hypothesize that resistance training with BFR is a promising new strategy to boost the effectiveness of resistance training interventions regarding cognitive performance. To support our hypothesis, we provide rationales of possible adaptation processes induced by resistance training with BFR. Furthermore, we outline recommendations for future studies planning to investigate the effects of resistance training with BFR on cognition.

Neonatal brain resting-state functional connectivity imaging modalities

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

The emergence of top-down, sensory prediction during learning in infancy: A comparison of full-term and preterm infants

Prematurity alters developmental trajectories in preterm infants even in the absence of medical complications. Here, we use fNIRS and learning tasks to probe the nature of the developmental differences between preterm and full-term born infants. Our recent work has found that prematurity disrupts the ability to engage in top-down sensory prediction after learning. We now examine the neural changes during the learning that precede prediction. In full-terms, we found modulation of all cortical regions examined during learning (temporal, frontal, and occipital). By contrast, preterm infants had no evidence of neural changes in the occipital lobe selectively. This is striking as the learning task leads to the emergence of visual prediction. Moreover, the shape of individual infants' occipital lobe trajectories (regardless of prematurity) predicts subsequent visual prediction abilities. These results suggest that modulation of sensory cortices during learning is closely related to the emergence of top-down signals and further indicates that developmental differences in premature infants may be associated with deficits in top-down processing.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) in Studying Cognitive Development: The Case of Mathematics and Language

In this review, we aim to highlight the application of functional near-infrared spectroscopy (fNIRS) as a useful neuroimaging technique for the investigation of cognitive development. We focus on brain activation changes during the development of mathematics and language skills in schoolchildren. We discuss how technical limitations of common neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have resulted in our limited understanding of neural changes during development, while fNIRS would be a suitable and child-friendly method to examine cognitive development. Moreover, this technique enables us to go to schools to collect large samples of data from children in ecologically valid settings. Furthermore, we report findings of fNIRS studies in the fields of mathematics and language, followed by a discussion of the outlook of fNIRS in these fields. We suggest fNIRS as an additional technique to track brain activation changes in the field of educational neuroscience.

Functional Organization of the Temporal-Parietal Junction for Theory of Mind in Preverbal Infants: A Near-Infrared Spectroscopy Study

Successful human social life requires imagining what others believe or think to understand and predict behavior. This ability, often referred to as theory of mind (ToM), reliably engages a specialized network of temporal and prefrontal brain regions in older children and adults, including selective recruitment of the temporal-parietal junction (TPJ). To date, how and when this specialized brain organization for ToM arises is unknown due to limitations in functional neuroimaging at younger ages. Here, we used the emerging technique of functional near-infrared spectroscopy to measure the functional brain response across parietal, temporal, and prefrontal regions in 7-month-old male and female infants as they viewed different video scenarios of a person searching for a hidden object. Over different conditions, we manipulated whether the person held an accurate (true) or inaccurate (false) belief about the location of the hidden object in the videos. In two separate experiments, we observed that activity from the TPJ, but not other temporal and prefrontal regions, spontaneously tracked with the beliefs of the other person, responding more during scenarios when the other person's belief regarding the location of the object was false compared with scenarios when her belief was true. These results mirror those obtained with adults to show that the TPJ already shows some functional organization relevant to high-level social cognition by around 7 months of age. Furthermore, these results suggest that infants may draw on similar core mechanisms to implicitly track beliefs, as adults do when reasoning explicitly about them.SIGNIFICANCE STATEMENT Humans selectively engage a network of brain regions, including the temporal-parietal junction (TPJ), to track what others think, an ability referred to as theory of mind. How and when this specialized brain organization for high-level social cognition arises is unknown. Using the emerging technique of near-infrared spectroscopy with 7-month-old infants, we observed that activity of the TPJ, but not other temporal and frontal regions, distinguished between scenarios when another person's belief about the location of the object was false compared with scenarios when the belief was true. These results suggest that a basic neural architecture to understand and predict the actions of others based on their beliefs may be present from the first year of life.

The infant brain in the social world: Moving toward interactive social neuroscience with functional near-infrared spectroscopy

Typically developing infants rapidly acquire a sophisticated array of social skills within the first year of life. These social skills are largely learned within the context of day-to-day interactions with caregivers. While social neuroscience has made great gains in our knowledge of the underlying neural circuitry of social cognition and behavior, much of this work has focused on experiments that sacrifice ecological validity for experimental control. Functional near-infrared spectroscopy (fNIRS) is a promising methodology for measuring brain activity in the context of naturalistic social interactions. Here, we review what we have learned from fNIRS studies that have used traditional experimental stimuli to study social development during infancy. We then discuss recent infant fNIRS studies that have utilized more naturalistic social stimuli, followed by a discussion of applications of this methodology to the study of atypical social development, with a focus on infants at risk for autism spectrum disorder. We end with recommendations for applying fNIRS to studies of typically developing and at-risk infants in naturalistic social situations.

Motion correction for infant functional near-infrared spectroscopy with an application to live interaction data

Correcting for motion is an important consideration in infant functional near-infrared spectroscopy studies. We tested the performance of conventional motion correction methods and compared probe motion and data quality metrics for data collected at different infant ages (5, 7, and 12 months) and during different methods of stimulus presentation (video versus live). While 5-month-olds had slower maximum head speed than 7- or 12-month-olds, data quality metrics and hemodynamic response recovery errors were similar across ages. Data quality was also similar between video and live stimulus presentation. Motion correction algorithms, such as wavelet filtering and targeted principal component analysis, performed well for infant data using infant-specific parameters, and parameters may be used without fine-tuning for infant age or method of stimulus presentation. We recommend using wavelet filtering with [Formula: see text]; however, a range of parameters seemed acceptable. We do not recommend using trial rejection alone, because it did not improve hemodynamic response recovery as compared to no correction at all. Data quality metrics calculated from uncorrected data were associated with hemodynamic response recovery error, indicating that full simulation studies may not be necessary to assess motion correction performance.

Using individual functional channels of interest to study cortical development with fNIRS

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that could be uniquely effective for investigating cortical function in human infants. However, prior efforts have been hampered by the difficulty of aligning arrays of fNIRS optodes placed on the scalp to anatomical or functional regions of underlying cortex. This challenge can be addressed by identifying channels of interest in individual participants, and then testing the reliability of those channels' response profiles in independent data. Using this approach, cortical regions with preferential responses to faces versus scenes, and to scenes versus faces, were observed reliably in both adults and infants. By contrast, standard analysis techniques did not reliably identify significant responses to both categories in either age group. These results reveal scene-responsive regions, and confirm face-responsive regions, in preverbal infants. More generally, the analysis approach will be a robust and sensitive tool for future characterization of the early functional development of the human brain.

Diffuse optical tomography to investigate the newborn brain

Over the past 15 years, functional near-infrared spectroscopy (fNIRS) has emerged as a powerful technology for studying the developing brain. Diffuse optical tomography (DOT) is an extension of fNIRS that combines hemodynamic information from dense optical sensor arrays over a wide field of view. Using image reconstruction techniques, DOT can provide images of the hemodynamic correlates to neural function that are comparable to those produced by functional magnetic resonance imaging. This review article explains the principles of DOT, and highlights the growing literature on the use of DOT in the study of healthy development of the infant brain, and the study of novel pathophysiology in infants with brain injury. Current challenges, particularly around instrumentation and image reconstruction, will be discussed, as will the future of this growing field, with particular focus on whole-brain, time-resolved DOT.

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

The specificity of the neural response to speech at birth

In this work we ask whether at birth, the human brain responds uniquely to speech, or if similar activation also occurs to a non-speech surrogate 'language'. We compare neural activation in newborn infants to the language heard in utero (English), to an unfamiliar language (Spanish), and to a whistled surrogate language (Silbo Gomero) that, while used by humans to communicate, is not speech. Anterior temporal areas of the neonate cortex are activated in response to both familiar and unfamiliar spoken language, but these classic language areas are not activated to the whistled surrogate form. These results suggest that at the time human infants emerge from the womb, the neural preparation for language is specialized to speech.

Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multivariate pattern analysis (MVPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.