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•First investigation of validity of longitudinal infant channel-space fNIRS analysis.•Novel image reconstruction analysis conducted.•Variability in array position is dominant factor driving different inferences.•Channel-space fNIRS analyses robust to implicit assumptions at group-level.•Hope to encourage more widespread use of image reconstruction in infant analyses. The first 1000 days from conception to two-years of age are a critical period in brain development, and there is an increasing drive for developing technologies to help advance our understanding of neurodevelopmental processes during this time. Functional near-infrared spectroscopy (fNIRS) has enabled longitudinal infant brain function to be studied in a multitude of settings. Conventional fNIRS analyses tend to occur in the channel-space, where data from equivalent channels across individuals are combined, which implicitly assumes that head size and source-detector positions (i.e. array position) on the scalp are constant across individuals. The validity of such assumptions in longitudinal infant fNIRS analyses, where head growth is most rapid, has not previously been investigated. We employed an image reconstruction approach to analyse fNIRS data collected from a longitudinal cohort of infants in The Gambia aged 5- to 12-months. This enabled us to investigate the effect of variability in both head size and array position on the anatomical and statistical inferences drawn from the data at both the group- and the individual-level. We also sought to investigate the impact of group size on inferences drawn from the data. We found that variability in array position was the driving factor between differing inferences drawn from the data at both the individual- and group-level, but its effect was weakened as group size increased towards the full cohort size (N = 53 at 5-months, N = 40 at 8-months and N = 45 at 12-months). We conclude that, at the group sizes in our dataset, group-level channel-space analysis of longitudinal infant fNIRS data is robust to assumptions about head size and array position given the variability in these parameters in our dataset. These findings support a more widespread use of image reconstruction techniques in longitudinal infant fNIRS studies.

Background Empathy‐understanding and sharing someone else's feelings‐is crucial for social bonds. Studies on empathy development are limited and mainly performed with behavioural assessments. This is in contrast to the extensive literature on cognitive and affective empathy in adults. However, understanding the mechanisms behind empathy development is critical to developing early interventions to support children with limited empathy. This is particularly key in toddlerhood, as children transition from highly scaffolded interactions with their parents and towards interactions with their peers. However, we know little about toddlers' empathy, in part due to the methodological constraints of testing this population in traditional lab settings. Methods Here, we combine naturalistic observations with a targeted review of the literature to provide an assessment of our current understanding of the development of empathy in toddlerhood as it is expressed in real‐world settings. We went into toddlers' typical habitat, a nursery, and we performed 21 h of naturalistic observations of 2‐to‐4‐year‐olds. We then reviewed the literature to evaluate our current understanding of the mechanisms that underpin observed behaviours. Results We observed that (i) emotional contagion, possibly a primitive form of empathy, was observed at the nursery, but rarely; (ii) older toddlers often stared when someone cried, but there was no clear evidence of shared feelings; (iii) teacher and parent scaffolding might be paramount for empathy development; (iv) as some atypical empathic reactions can be observed from toddlerhood, early interventions could be developed. Several competing theoretical frameworks could account for current findings. Conclusions Targeted studies of toddlers and their interaction partners in both controlled and naturalistic contexts are required to distinguish different mechanistic explanations for empathic behaviour in toddlerhood. We recommend the use of new cutting‐edge methodologies to embed neurocognitively‐informed frameworks into toddlers' natural social world.

There is a scarcity of prospective longitudinal research targeted at early postnatal life which maps developmental pathways of early-stage processing and brain specialisation in the context of early adversity. Follow up from infancy into the one-five year age range is key, as it constitutes a critical gap between infant and early childhood studies. Availability of portable neuroimaging (functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG)) has enabled access to rural settings increasing the diversity of our sampling and broadening developmental research to include previously underrepresented ethnic-racial and geographical groups in low- and middle- income countries (LMICs). The primary objective of the Brain Imaging for Global Health (BRIGHT) project was to establish brain function - using longitudinal data from mother - for-age reference curves infant dyads living in the UK and rural Gambia and investigate the association between context-associated moderators and developmental trajectories across the first two years of life in The Gambia. In total, 265 participating families were seen during pregnancy, at 7–14 days, 1-, 5-, 8-, 12-, 18- and 24-months post-partum. An additional visit is now underway at 3–5 years to assess pre-school outcomes. The majority of our Gambian cohort live in poverty, but while resource-poor in many factors they commonly experience a rich and beneficial family and caregiving context with multigenerational care and a close-knit supportive community. Understanding the impact of different factors at play in such an environment ( i.e. , detrimental undernutrition versus beneficial multigenerational family support) will (i) improve the representativeness of models of general cognitive developmental pathways from birth, (ii) identify causal pathways of altered trajectories associated with early adversity at both individual and group level, and (iii) identify the context-associated moderators ( i.e. social context) that protect development despite the presence of poverty-associated challenges. This will in turn contribute to the development of targeted interventions.

Despite motion artifacts are a major source of noise in fNIRS infant data, how to approach motion correction in this population has only recently started to be investigated. Homer2 offers a wide range of motion correction methods and previous work on simulated and adult data suggested the use of Spline interpolation and Wavelet filtering as optimal methods for the recovery of trials affected by motion. However, motion artifacts in infant data differ from those in adults’ both in amplitude and frequency of occurrence. Therefore, artifact correction recommendations derived from adult data might not be optimal for infant data. We hypothesized that the combined use of Spline and Wavelet would outperform their individual use on data with complex profiles of motion artifacts. To demonstrate this, we first compared, on infant semi-simulated data, the performance of several motion correction techniques on their own and of the novel combined approach; then, we investigated the performance of Spline and Wavelet alone and in combination on real cognitive data from three datasets collected with infants of different ages (5, 7 and 10 months), with different tasks (auditory, visual and tactile) and with different NIRS systems. To quantitatively estimate and compare the efficacy of these techniques, we adopted four metrics: hemodynamic response recovery error, within-subject standard deviation, between-subjects standard deviation and number of trials that survived each correction method. Our results demonstrated that (i) it is always better correcting for motion artifacts than rejecting the corrupted trials; (ii) Wavelet filtering on its own and in combination with Spline interpolation seems to be the most effective approach in reducing the between- and the within-subject standard deviations. Importantly, the combination of Spline and Wavelet was the approach providing the best performance in semi-simulation both at low and high levels of noise, also recovering most of the trials affected by motion artifacts across all datasets, a crucial result when working with infant data. •Comparison of motion correction techniques on semi-simulated and real fNIRS infant data.•Spline and wavelet combined outperform the individual use of these techniques.•Spline and wavelet combined better recovered the true HRF in simulated data.•Spline and wavelet combined had the best performance in motion artifact correction.•Spline and wavelet combined saved nearly all corrupted trials across all datasets.

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data. •Connectivity studies give important insights into infant brain development.•fNIRS is a valuable method for infancy studies, but can we analyse connectivity?•On fMRI-fNIRS acquired simultaneously, we estimate effective connectivity with DCM.•We showed high correspondence of DCM values between fMRI and fNIRS data.•We validated DCM on fNIRS infant data, providing guidance for future projects.

A child’s social world is complex and rich, but has traditionally been assessed with conventional experiments where children are presented with repeated stimuli on a screen. These assessments are impoverished relative to the dynamics of social interactions in real life, and can be challenging to implement with preschoolers, who struggle to comply with strict lab rules. The current work meets the need to develop new platforms to assess preschoolers’ social development, by presenting a unique virtual-reality set-up combined with wearable functional near-infrared spectroscopy (fNIRS). As a proof-of-principle, we validated this platform by measuring brain activity during self-guided social interaction in 3-to-5-year-olds, which is under-investigated, yet crucial to understand the basis of social interactions in preschoolers. 37 preschoolers chose an interaction partner from one of 4 human-like avatars of different gender and age. We recorded spontaneous brain fluctuations from the frontal and temporoparietal regions (notably engaged in social-categorization and preference) while children played a bubble-popping game with a preferred and an assigned avatar. 60% of the participants chose to play with the same-gender and same-age avatar. However, this result was driven by females (>80% vs. 50% in males). Different fronto-temporoparietal connectivity patterns when playing with the two avatars were observed, especially in females. We showed the feasibility of using a novel set-up to naturalistically assess social preference in preschoolers, which was assessed at the behavioural and functional connectivity level. This work provides a first proof-of-principle for using cutting-edge technologies and naturalistic experiments to study social development, opening new avenues of research.

Functional near-infrared spectroscopy (fNIRS) is a unique neuroimaging methodology with high portability and tolerance of motion, making it well-suited to research in dynamic real-world environments. However, it is crucial to carefully design fNIRS paradigms to suit the unique requirements of real-world research settings. We outline key design principles and considerations for fNIRS studies. In this paper, we address the lack of guidance on experimental design for fNIRS, which in our growing field can help to educate new fNIRS researchers and improve the quality and applicability of fNIRS research across various settings. Here, we provide a primer for how to design fNIRS studies and overcome challenges in fNIRS experimental design, with a focus on naturalistic real-world research, which has gathered increased research interest in recent years. We conclude by outlining seven key design principles researchers can use to guide experimental design for fNIRS research.

Infant functional near-infrared spectroscopy (fNIRS) data are particularly vulnerable to noise; participant behavior can result in motion artifacts, and reduced set-up times can cause poor optode coupling. Accurate channel pruning is therefore essential, but approaches vary and often use adult-derived thresholds, risking unnecessary data loss. We systematically compared pruning approaches and parameter choices to evaluate their effects on data quality and retention in infant fNIRS. Data from 5 to 24-month-old infants were collected across two cohorts, using two paradigms. Channel pruning was performed using the coefficient of variation (CV) and the quality testing of near-infrared scans (QT-NIRS) tool, varying key thresholds. Multilevel models assessed the effects of pruning method, parameter choice, age, motion, and testing site on signal-to-noise ratio (SNR) and channels retained. QT-NIRS produced significantly higher SNR than CV pruning across nearly all age, task, and cohort combinations when matched for data retention. Higher QT-NIRS thresholds improved quality but reduced retention. Motion prevalence strongly reduced both SNR and retention; testing site and age had smaller but notable effects. QT-NIRS offers a better balance of data quality and retention than CV pruning. Lower QT-NIRS thresholds than adult defaults are recommended for infant data. These findings provide practical guidance for preprocessing pipelines in developmental fNIRS research.

Neonates undergo rapid development, yet the examination of emerging brain markers across paradigms, cognitive domains, and diverse global populations remains limited. We investigated whether brain responses at 1 month of age could be interrogated across paradigms to offer deeper context-specific insights into neurodevelopment. Functional near-infrared spectroscopy was used to assess frontal and temporal brain responses during natural sleep in 181 infants from a low-income setting (rural Gambia) and 58 infants from a higher-income setting (Cambridge, United Kingdom) during three auditory paradigms: social selectivity, habituation and novelty detection, and functional connectivity. Paradigm-level brain responses were analyzed using threshold-free cluster enhancement and cross-paradigm comparisons of individual responses. Both Gambian and UK infants showed habituation but not novelty responses, higher inter- versus intra-hemispheric connectivity, stronger inter-hemispheric connectivity in temporal relative to frontal regions, stronger inter-regional connectivity between right temporal and left frontal regions, and nonvocal > vocal selectivity (UK infants only). Cross-cohort differences in the cross-paradigm analyses suggest that context-specific developmental markers are evident within the first month of life and show high individual variability. Cross-paradigm analyses revealed that greater vocal selectivity (UK) is associated with higher inter-hemispheric connectivity, potentially allowing us to identify biomarkers of more mature neurodevelopment within the first weeks of postnatal life.

Up until now, self-recognition in the mirror, achieved at around 18 months, has been used to assess self-awareness in infancy. Even though the significance of this test is not universally accepted, this field has progressed very little over the last decades, in contrast to a broad volume of literature on the self in adults. However, a relationship between self-other differentiation and social cognitive abilities has been recently hypothesized, renewing the interest in mechanisms underlying emerging self-awareness. Adult studies have highlighted that brain networks, instead of isolated brain areas, support self-processing. Therefore, the first two studies of this thesis validated the use of advanced connectivity analyses on infant fNIRS data. Making use of these methods, one study demonstrated that functional connectivity between regions belonging to a network that has been related to abstract self-processing in adults gradually increases over the first two years of life. The same network was found to characterise infants who recognise themselves in the mirror. In another study, crucial regions of this network were shown to be engaged during self-recognition in 18-month-olds. As social interactions have been suggested to be fundamental for the construction of the self, the last two studies of this thesis investigated the relationship between emerging self-awareness and social interactions. To test this, I focused on mimicry, known to play an important role in affiliation and in mediating relationships. One study demonstrated that emerging selfawareness may affect infants' tendency to selectively mimic in-group members, which may indicate a possible role of self-comparison and identification processes. The last study did not find evidence for a relationship between mothers' tendency to imitate their infants at 4 months and emerging selfawareness. Taken together, these studies enrich our understanding of the mechanisms underlying emerging self-awareness and they represent a pioneering starting point for further investigations into this topic.