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

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.

The Development of Cortical Responses to the Integration of Audiovisual Speech in Infancy

In adults, the integration of audiovisual speech elicits specific higher (super-additive) or lower (sub-additive) cortical responses when compared to the responses to unisensory stimuli. Although there is evidence that the fronto-temporal network is active during perception of audiovisual speech in infancy, the development of fronto-temporal responses to audiovisual integration remains unknown. In the current study, 5-month-olds and 10-month-olds watched bimodal (audiovisual) and alternating unimodal (auditory + visual) syllables. In this context we use alternating unimodal to denote alternating auditory and visual syllables that are perceived as separate syllables by adults. Using fNIRS we measured responses over large cortical areas including the inferior frontal and superior temporal regions. We identified channels showing different responses to bimodal than alternating unimodal condition and used multivariate pattern analysis (MVPA) to decode patterns of cortical responses to bimodal (audiovisual) and alternating unimodal (auditory + visual) speech. Results showed that in both age groups integration elicits cortical responses consistent with both super- and sub-additive responses in the fronto-temporal cortex. The univariate analyses revealed that between 5 and 10 months spatial distribution of these responses becomes increasingly focal. MVPA correctly classified responses at 5 months, with key input from channels located in the inferior frontal and superior temporal channels of the right hemisphere. However, MVPA classification was not successful at 10 months, suggesting a potential cortical re-organisation of audiovisual speech perception at this age. These results show the complex and non-gradual development of the cortical responses to integration of congruent audiovisual speech in infancy.

Functional near-infrared spectroscopy in pediatric clinical research: Different pathophysiologies and promising clinical applications

Over its 30 years of existence, functional near-infrared spectroscopy (fNIRS) has matured into a highly versatile tool to study brain function in infants and young children. Its advantages, amongst others, include its ease of application and portability, the option to combine it with electrophysiology, and its relatively good tolerance to movement. As shown by the impressive body of fNIRS literature in the field of cognitive developmental neuroscience, the method's strengths become even more relevant for (very) young individuals who suffer from neurological, behavioral, and/or cognitive impairment. Although a number of studies have been conducted with a clinical perspective, fNIRS cannot yet be considered as a truly clinical tool. The first step has been taken in this direction by studies exploring options in populations with well-defined clinical profiles. To foster further progress, here, we review several of these clinical approaches to identify the challenges and perspectives of fNIRS in the field of developmental disorders. We first outline the contributions of fNIRS in selected areas of pediatric clinical research: epilepsy, communicative and language disorders, and attention-deficit/hyperactivity disorder. We provide a scoping review as a framework to allow the highlighting of specific and general challenges of using fNIRS in pediatric research. We also discuss potential solutions and perspectives on the broader use of fNIRS in the clinical setting. This may be of use to future research, targeting clinical applications of fNIRS in children and adolescents.

Speakers aren't blank slates (with respect to sign-language phonology)!

A large literature has gauged the linguistic knowledge of signers by comparing sign-processing by signers and non-signers. Underlying this approach is the assumption that non-signers are devoid of any relevant linguistic knowledge, and as such, they present appropriate non-linguistic controls-a recent paper by Meade et al. (2022) articulates this view explicitly. Our commentary revisits this position. Informed by recent findings from adults and infants, we argue that the phonological system is partly amodal. We show that hearing infants use a shared brain network to extract phonological rules from speech and sign. Moreover, adult speakers who are sign-naïve demonstrably project knowledge of their spoken L1 to signs. So, when it comes to sign-language phonology, speakers are not linguistic blank slates. Disregarding this possibility could systematically underestimate the linguistic knowledge of signers and obscure the nature of the language faculty.

Level of Attention to Motherese Speech as an Early Marker of Autism Spectrum Disorder

Importance

Caregivers have long captured the attention of their infants by speaking in motherese, a playful speech style characterized by heightened affect. Reduced attention to motherese in toddlers with autism spectrum disorder (ASD) may be a contributor to downstream language and social challenges and could be diagnostically revealing.

Objective

To investigate whether attention toward motherese speech can be used as a diagnostic classifier of ASD and is associated with language and social ability.

Design, Setting, and Participants

This diagnostic study included toddlers aged 12 to 48 months, spanning ASD and non-ASD diagnostic groups, at a research center. Data were collected from February 2018 to April 2021 and analyzed from April 2021 to March 2022.

Exposures

Gaze-contingent eye-tracking test.

Main Outcomes and Measures

Using gaze-contingent eye tracking wherein the location of a toddler's fixation triggered a specific movie file, toddlers participated in 1 or more 1-minute eye-tracking tests designed to quantify attention to motherese speech, including motherese vs traffic (ie, noisy vehicles on a highway) and motherese vs techno (ie, abstract shapes with music). Toddlers were also diagnostically and psychometrically evaluated by psychologists. Levels of fixation within motherese and nonmotherese movies and mean number of saccades per second were calculated. Receiver operating characteristic (ROC) curves were used to evaluate optimal fixation cutoff values and associated sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Within the ASD group, toddlers were stratified based on low, middle, or high levels of interest in motherese speech, and associations with social and language abilities were examined.

Results

A total of 653 toddlers were included (mean [SD] age, 26.45 [8.37] months; 480 males [73.51%]). Unlike toddlers without ASD, who almost uniformly attended to motherese speech with a median level of 82.25% and 80.75% across the 2 tests, among toddlers with ASD, there was a wide range, spanning 0% to 100%. Both the traffic and techno paradigms were effective diagnostic classifiers, with large between-group effect sizes (eg, ASD vs typical development: Cohen d, 1.0 in the techno paradigm). Across both paradigms, a cutoff value of 30% or less fixation on motherese resulted in an area under the ROC curve (AUC) of 0.733 (95% CI, 0.693-0.773) and 0.761 (95% CI, 0.717-0.804), respectively; specificity of 98% (95% CI, 95%-99%) and 96% (95% CI, 92%-98%), respectively; and PPV of 94% (95% CI, 86%-98%). Reflective of heterogeneity and expected subtypes in ASD, sensitivity was lower at 18% (95% CI, 14%-22%) and 29% (95% CI, 24%-34%), respectively. Combining metrics increased the AUC to 0.841 (95% CI, 0.805-0.877). Toddlers with ASD who showed the lowest levels of attention to motherese speech had weaker social and language abilities.

Conclusions and Relevance

In this diagnostic study, a subset of toddlers showed low levels of attention toward motherese speech. When a cutoff level of 30% or less fixation on motherese speech was used, toddlers in this range were diagnostically classified as having ASD with high accuracy. Insight into which toddlers show unusually low levels of attention to motherese may be beneficial not only for early ASD diagnosis and prognosis but also as a possible therapeutic target.

Open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep using fNIRS

Spontaneous, task-free, hemodynamic activity of the brain provides useful information about its functional organization, as it can describe how different brain regions communicate to each other. Neuroimaging studies measuring the spontaneous activity of the brain are conducted while the participants are not engaged in a particular task or receiving any external stimulation. This approach is particularly useful in developmental populations as brain activity can be measured without the need for infant compliance and the risks of data contamination due to motion artifacts. In this project we sought to i) characterize the intrinsic functional organization of the brain in 4-month-old infants and ii) investigate whether bilingualism, as a specific environmental factor, could lead to adaptations on functional brain network development at this early age. Measures of spontaneous hemodynamic activity were acquired in 4-month-old infants (n = 104) during natural sleep using functional near-infrared spectroscopy (fNIRS). Emphasis was placed on acquiring high-quality data that could lead to reproducible results and serve as a valuable resource for researchers investigating the developing functional connectome.

Measurement(s)	spontaneous hemodynamic brain activity
Technology Type(s)	functional near infrared spectroscopy

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Gender difference in functional activity of 4-months-old infants during sleep: A functional near-infrared spectroscopy study

Sex differences emerge early in infancy. A number of earlier studies have investigated the resting-state network of infant sleep states, and there have been many studies using functional near-infrared spectroscopy (fNIRS) to examine the effects of infant language learning on changes in oxyhemoglobin and deoxyhemoglobin levels. However, studies examining sex differences from the resting-state network of infant sleep states are scarce. This study uses an open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep by fNIRS, to identify some difference between male and female infants. We used Power Spectral Density showing at which frequencies the data variation/variance is high. We have also analyzed some gender differences by analyzing the relationship between individual channels, the degree of activation, etc. The results of this study showed that female and male infants had different Power Spectral Density for oxyhemoglobin and deoxyhemoglobin at rest, showing stronger differences at frontoparietal network, somatomotor network, visual network and dorsal network. This may be due to the differences in the timing or extent of development of those networks. These differences will provide some assistance in future studies of the early education of male and female infants.

Infants differentially extract rules from language

Infants readily extract linguistic rules from speech. Here, we ask whether this advantage extends to linguistic stimuli that do not rely on the spoken modality. To address this question, we first examine whether infants can differentially learn rules from linguistic signs. We show that, despite having no previous experience with a sign language, six-month-old infants can extract the reduplicative rule (AA) from dynamic linguistic signs, and the neural response to reduplicative linguistic signs differs from reduplicative visual controls, matched for the dynamic spatiotemporal properties of signs. We next demonstrate that the brain response for reduplicative signs is similar to the response to reduplicative speech stimuli. Rule learning, then, apparently depends on the linguistic status of the stimulus, not its sensory modality. These results suggest that infants are language-ready. They possess a powerful rule system that is differentially engaged by all linguistic stimuli, speech or sign.

Infant social interactions and brain development: A systematic review

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Associations between caregiver-infant behaviours during social interactions and brain development outcomes were investigated.

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Caregivers' and infants' behaviours in interactions related to children’s structural, functional and connectivity measures.

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Concurrent associations between behavioural and brain measures were apparent as early as three months postnatally.

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Long-term associations between behaviours in early interactions and brain development outcomes were observed decades later.

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Individual differences in early interactions and associated brain development is an important avenue for further research.

From birth, interactions with others are an integral part of a person’s daily life. In infancy, social exchanges are thought to be critical for optimal brain development. This systematic review explores this association by drawing together infant studies that relate adult-infant behaviours – coded from their social interactions - to children’s brain measures collected during a neuroimaging session in infancy, childhood, adolescence or adulthood. In total, we identified 55 studies that explored associations between infants’ social interactions and neural measures. These studies show that several aspects of caregiver-infant behaviours are associated with, or predict, a variety of neural responses in infants, children and adolescents. The presence of both concurrent and long-term associations - some of which are first observed just a few months postnatally and extend into adulthood - open an important research avenue and motivate further longitudinal studies.

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.

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain's organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( N = 99 ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

Inferior parietal lobule is sensitive to different semantic similarity relations for concrete and abstract words

Similarity measures, the extent to which two concepts have similar meanings, are the key to understand how concepts are represented, with different theoretical perspectives relying on very different sources of data from which similarity can be calculated. While there is some commonality in similarity measures, the extent of their correlation is limited. Previous studies also suggested that the relative performance of different similarity measures may also vary depending on concept concreteness and that the inferior parietal lobule (IPL) may be involved in the integration of conceptual features in a multimodal system for the semantic categorization. Here, we tested for the first time whether theory-based similarity measures predict the pattern of brain activity in the IPL differently for abstract and concrete concepts. English speakers performed a semantic decision task, while we recorded their brain activity in IPL through fNIRS. Using representational similarity analysis, results indicated that the neural representational similarity in IPL conformed to the lexical co-occurrence among concrete concepts (regardless of the hemisphere) and to the affective similarity among abstract concepts in the left hemisphere only, implying that semantic representations of abstract and concrete concepts are characterized along different organizational principles in the IPL. We observed null results for the decoding accuracy. Our study suggests that the use of the representational similarity analysis as a complementary analysis to the decoding accuracy is a promising tool to reveal similarity patterns between theoretical models and brain activity recorded through fNIRS.