A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data.

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.

Variations in Cortical Oxygenation by Near-Infrared Spectroscopy According to Head Position after Acute Stroke: The Preliminary Findings of an Observational Study.

Background: After ischemic stroke, there is no general consensus on the optimal position for the head of patients in the acute phase. This observational study aimed to measure the variations in cortical oxygenation using noninvasive functional near-infrared spectroscopy (fNIRS) at different degrees of head positioning on a bed. Methods: Consecutive ischemic stroke patients aged 18 years or older with anterior circulation ischemic stroke within 48 h of symptom onset who could safely assume different positions on a bed were included. A 48-channel fNIRS system was placed in the bilateral sensorimotor cortex. Then, the bed of each patient was moved into four consecutive positions: (1) seated (90° angle between the head and bed surface); (2) lying at 30°; (3) seated again (90°); and (4) lying flat (0°). Each position was maintained for 90 s; the test was conducted 48 h after stroke onset and after 5 ± 1 days. The variations in oxygenated hemoglobin in the global brain surface and for each hemisphere were recorded and compared. Results: Twenty-one patients were included (males, n = 11; age, 79 ± 9 years; ASPECTS, 8 ± 2). When evaluating the affected side, the median oxygenation was significantly greater in the lying-flat (0°) and 30° positions than in the 90° position (p < 0.001 for both comparisons). No significant differences between the supine position and the 30° position were found, although oxygenation was slightly lower in the 30° position than in the supine position (p = 0.063). No differences were observed when comparing recanalized and nonrecanalized patients separately or according to stroke severity. The evaluation conducted 5 days after the stroke confirmed the previous data, with a significant difference in oxygenation at 0° and 30° compared to 90°. Conclusions: This preliminary study suggested that there are no substantial differences in brain oxygenation between the lying-flat head position and the 30° laying position.

Cross-modal plasticity in children with cochlear implant: converging evidence from EEG and functional near-infrared spectroscopy.

Over the first years of life, the brain undergoes substantial organization in response to environmental stimulation. In a silent world, it may promote vision by (i) recruiting resources from the auditory cortex and (ii) making the visual cortex more efficient. It is unclear when such changes occur and how adaptive they are, questions that children with cochlear implants can help address. Here, we examined 7-18 years old children: 50 had cochlear implants, with delayed or age-appropriate language abilities, and 25 had typical hearing and language. High-density electroencephalography and functional near-infrared spectroscopy were used to evaluate cortical responses to a low-level visual task. Evidence for a 'weaker visual cortex response' and 'less synchronized or less inhibitory activity of auditory association areas' in the implanted children with language delays suggests that cross-modal reorganization can be maladaptive and does not necessarily strengthen the dominant visual sense.

Brain responses to repetition-based rule-learning do not exhibit sex differences: an aggregated analysis of infant fNIRS studies.

Studies have repeatedly shown sex differences in some areas of language development, typically with an advantage for female over male children. However, the tested samples are typically small and the effects do not always replicate. Here, we used a meta-analytic approach to address this issue in a larger sample, combining seven fNIRS studies on the neural correlates of repetition- and non-repetition-based rule learning in newborns and 6-month-old infants. The ability to extract structural regularities from the speech input is fundamental for language development, it is therefore highly relevant to understand whether this ability shows sex differences. The meta-analysis tested the effect of Sex, as well as of other moderators on infants' hemodynamic responses to repetition-based (e.g. ABB: "mubaba") and non-repetition-based (e.g. ABC: "mubage") sequences in both anatomically and functionally defined regions of interests. Our analyses did not reveal any sex differences at birth or at 6 months, suggesting that the ability to encode these regularities is robust across sexes. Interestingly, the meta-analysis revealed other moderator effects. Thus in newborns, we found a greater involvement of the bilateral temporal areas compared to the frontal areas for both repetition and non-repetition sequences. Further, non-repetition sequences elicited greater responses in 6-month-olds than in newborns, especially in the bilateral frontal areas. When analyzing functional clusters of HbR timetraces, we found that a larger right-left asymmetry for newborn boys in brain responses compared to girls, which may be interpreted in terms of a larger right-left asymmetry in cerebral blood flow in boys than in girls early in life. We conclude that extracting repetition-based regularities from speech is a robust ability with a well-defined neural substrate present from birth and it does not exhibit sex differences.

Motor Processing in Children With Cochlear Implants as Assessed by Functional Near-Infrared Spectroscopy.

Auditory-motor and visual-motor networks are often coupled in daily activities, such as when listening to music and dancing; but these networks are known to be highly malleable as a function of sensory input. Thus, congenital deafness may modify neural activities within the connections between the motor, auditory, and visual cortices. Here, we investigated whether the cortical responses of children with cochlear implants (CI) to a simple and repetitive motor task would differ from that of children with typical hearing (TH) and we sought to understand whether this response related to their language development. Participants were 75 school-aged children, including 50 with CI (with varying language abilities) and 25 controls with TH. We used functional near-infrared spectroscopy (fNIRS) to record cortical responses over the whole brain, as children squeezed the back triggers of a joystick that vibrated or not with the squeeze. Motor cortex activity was reflected by an increase in oxygenated hemoglobin concentration (HbO) and a decrease in deoxygenated hemoglobin concentration (HbR) in all children, irrespective of their hearing status. Unexpectedly, the visual cortex (supposedly an irrelevant region) was deactivated in this task, particularly for children with CI who had good language skills when compared to those with CI who had language delays. Presence or absence of vibrotactile feedback made no difference in cortical activation. These findings support the potential of fNIRS to examine cognitive functions related to language in children with CI.

Erratum: Reproducibility of infant fNIRS studies: a meta-analytic approach (Erratum).

[This corrects the article DOI: 10.1117/1.NPh.10.2.023518.].

Reproducibility of infant fNIRS studies: a meta-analytic approach.

Significance: Concerns about the reproducibility of experimental findings have recently emerged in many disciplines, from psychology to medicine and neuroscience. As NIRS is a relatively recent brain imaging technique, the question of reproducibility has not yet been systematically addressed. Aim: The current study seeks to test the replicability of effects observed in NIRS experiments assessing young infants' rule-learning ability. Approach: We conducted meta-analyses and mixed-effects modeling-based inferential statistics to determine whether effect sizes were replicable and comparable in a sample of 23 NIRS studies investigating infants' abilities to process repetition- and diversity-based regularities in linguistic and nonlinguistic auditory and visual sequences. Additionally, we tested whether effect sizes were modulated by different factors such as the age of participants or the laboratory. We obtained NIRS data from 12 published and 11 unpublished studies. The 23 studies involved a total of 487 infants, aged between 0 and 9 months, tested in four different countries (Canada, France, Italy, and USA). Results: Our most important finding is that study and laboratory were never significant moderators of variation in effect sizes, indicating that results replicated reliably across the different studies and labs included in the sample. We observed small-to-moderate effect sizes, similar to effect sizes found with other neuroimaging and behavioral techniques in the developmental literature. In line with existing findings, effect sizes were modulated by the participants' age and differed across the different regularities tested, with repetition-based regularities giving rise to the strongest effects; in particular, the overall magnitude of this effect in the left temporal region was 0.27 when analyzing the entire dataset. Conclusions: Meta-analysis is a useful tool for assessing replicability and cross-study variability. Here, we have shown that infant NIRS studies in the language domain replicate robustly across various NIRS machines, testing sites, and developmental populations.

Vocal brain development in infants of mothers with serious mental illness (CAPRI-Voc): study protocol.

INTRODUCTION: Improving the lives of children and adolescents with parental mental illness (CAPRI) remains an urgent political and public health concern for the UK and European Union. Recurrent parental mental illness is believed to lead to fractures in the family, academic and social lives of these children, yet interventions are poorly targeted and non-specific. Part of an interdisciplinary programme of work (the CAPRI Programme; grant number: 682741), CAPRI-Voc aims to achieve two goals: first, to test the feasibility of our longitudinal imaging paradigm in mother-infant pairs where the mother has a diagnosis of severe mental illness. Second, to compare development of vocal processing in these infants with infants in the general population. METHODS AND ANALYSIS: Recruitment of 100 infants of mothers with mental illness, alongside 50 infants of healthy mothers. Both cohorts of infants will undergo functional near infrared spectroscopy (fNIRS) brain imaging at three time points: 9, 12 and 18 months to explore differences between cohorts in their neural responses to vocal stimuli in our language paradigm. Mothers will complete an interview and psychological questionnaires. We shall also complete an infant developmental battery and mother-child interaction play session. Data on recruitment, retention and dropout will be recorded. ETHICS AND DISSEMINATION: It will be made clear that fNIRS is a safe, non-invasive technology widely used in infant clinical and psychological research. We shall reassure mothers that no definitive causal link exists between maternal mental illness and language development in infants, and that individual data will only exist as part of the wider dataset. As the study includes both children and vulnerable adults, all research staff will complete National Health Service (NHS) Safeguarding level 3 training. Dissemination will be via direct feedback to stakeholders, patient and advisory groups, and through presentations at conferences, journal publications and university/NHS trust communications. The study was approved through North West-Greater Manchester West Research Ethics Committee (17/NW/0074) and Health Research Authority (212715).

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.

A practical guide for synthetic fNIRS data generation.

The use of a large and diversified ground-truth synthetic fNIRS dataset enables researchers to objectively validate and compare data analysis procedures. In this work, we describe each step of the synthetic data generation workflow and we provide tools to generate the dataset.

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.

Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks.

On [Formula: see text] March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understand the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in travelling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown, i.e. earlier government actions could have contained the growth to a degree that a widespread lockdown would have been avoided, or at least delayed. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.

Classifying the mental representation of word meaning in children with Multivariate Pattern Analysis of fNIRS.

This study presents the implementation of a within-subject neural decoder, based on Support Vector Machines, and its application for the classification of distributed patterns of hemodynamic activation, measured with Functional Near Infrared Spectroscopy (fNIRS) on children, in response to meaningful and meaningless auditory stimuli. Classification accuracy nominally exceeds chance level for the majority of the participants, but fails to reach statistical significance. Future work should investigate whether individual differences in classification accuracy may relate to other characteristics of the children, such as their cognitive, speech or reading abilities.

Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

OBJECTIVE: The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). APPROACH: This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. MAIN RESULTS: The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. SIGNIFICANCE: The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.

Stereotypic wheel running decreases cortical activity in mice.

Prolonged wakefulness is thought to gradually increase 'sleep need' and influence subsequent sleep duration and intensity, but the role of specific waking behaviours remains unclear. Here we report the effect of voluntary wheel running during wakefulness on neuronal activity in the motor and somatosensory cortex in mice. We find that stereotypic wheel running is associated with a substantial reduction in firing rates among a large subpopulation of cortical neurons, especially at high speeds. Wheel running also has longer-term effects on spiking activity across periods of wakefulness. Specifically, cortical firing rates are significantly higher towards the end of a spontaneous prolonged waking period. However, this increase is abolished when wakefulness is dominated by running wheel activity. These findings indicate that wake-related changes in firing rates are determined not only by wake duration, but also by specific waking behaviours.

Beyond astronaut's capabilities: The current state of the art.

Space agencies have developed extensive expertise with sustaining human presence in low earth orbits and microgravity. Prolonged human presence in space beyond EarthâAZs orbit presents additional, some still unsolved issues. These are linked to the distance to Earth (impossibility of effective tele-operation, psychological effects linked to remoteness from Earth, required autonomy, the handling of emergencies, long mission durations), and to the environments beyond the Earth magnetosphere (radiation levels, local environments including atmospheres, dust, gravity, day-night cycles). These issues have impacts on the spacecraft design, the mission operations, astronaut selection and preparation and required supporting/ enabling technologies. This paper builds upon previous work by Rossini et al. , in critically reviewing and updating the current state of scientific research on enhancing astronaut's capabilities to face some of these challenges. In particular, it discusses the pertinence and feasibility of two approaches aiming at enhancing the chances of success of human missions: induced hibernation state and brain-machine interfaces.

Pattern recognition with adaptive-thresholds for sleep spindle in high density EEG signals.

Medicine and Surgery, University of Pisa, via Savi 10, 56126, Pisa, Italy Sleep spindles are electroencephalographic oscillations peculiar of non-REM sleep, related to neuronal mechanisms underlying sleep restoration and learning consolidation. Based on their very singular morphology, sleep spindles can be visually recognized and detected, even though this approach can lead to significant mis-detections. For this reason, many efforts have been put in developing a reliable algorithm for spindle automatic detection, and a number of methods, based on different techniques, have been tested via visual validation. This work aims at improving current pattern recognition procedures for sleep spindles detection by taking into account their physiological sources of variability. We provide a method as a synthesis of the current state of art that, improving dynamic threshold adaptation, is able to follow modification of spindle characteristics as a function of sleep depth and inter-subjects variability. The algorithm has been applied to physiological data recorded by a high density EEG in order to perform a validation based on visual inspection and on evaluation of expected results from normal night sleep in healthy subjects.

Enabling variable-stiffness hand rehabilitation orthoses with dielectric elastomer transducers.

Patients affected by motor disorders of the hand and having residual voluntary movements of fingers or wrist can benefit from self-rehabilitation exercises performed with so-called dynamic hand splints. These systems consist of orthoses equipped with elastic cords or springs, which either provide a sustained stretch or resist voluntary movements of fingers or wrist. These simple systems are limited by the impossibility of modulating the mechanical stiffness. This limitation does not allow for customizations and real-time control of the training exercise, which would improve the rehabilitation efficacy. To overcome this limitation, 'active' orthoses equipped with devices that allow for electrical control of the mechanical stiffness are needed. Here, we report on a solution that relies on compact and light-weight electroactive elastic transducers that replace the passive elastic components. We developed a variable-stiffness transducer made of dielectric elastomers, as the most performing types of electromechanically active polymers. The transducer was manufactured with a silicone film and tested with a purposely-developed stiffness control strategy that allowed for electrical modulations of the force-elongation response. Results showed that the proposed new technology is a promising and viable solution to develop electrically controllable dynamic hand orthoses for hand rehabilitation.