Probing the association between maternal anxious attachment style and mother-child brain-to-brain coupling during passive co-viewing of visual stimuli

Investigating mother-child inter-brain synchrony in a naturalistic paradigm: A functional near infrared spectroscopy (fNIRS) hyperscanning study

Successful social interactions between mothers and children are hypothesised to play a significant role in a child's social, cognitive and language development. Earlier research has confirmed, through structured experimental paradigms, that these interactions could be underpinned by coordinated neural activity. Nevertheless, the extent of neural synchrony during real-life, ecologically valid interactions between mothers and their children remains largely unexplored. In this study, we investigated mother-child inter-brain synchrony using a naturalistic free-play paradigm. We also examined the relationship between neural synchrony, verbal communication patterns and personality traits to further understand the underpinnings of brain synchrony. Twelve children aged between 3 and 5 years old and their mothers participated in this study. Neural synchrony in mother-child dyads were measured bilaterally over frontal and temporal areas using functional Near Infra-red Spectroscopy (fNIRS) whilst the dyads were asked to play with child-friendly toys together (interactive condition) and separately (independent condition). Communication patterns were captured via video recordings and conversational turns were coded. Compared to the independent condition, mother-child dyads showed increased neural synchrony in the interactive condition across the prefrontal cortex and temporo-parietal junction. There was no significant relationship found between neural synchrony and turn-taking and between neural synchrony and the personality traits of each member of the dyad. Overall, we demonstrate the feasibility of measuring inter-brain synchrony between mothers and children in a naturalistic environment. These findings can inform future study designs to assess inter-brain synchrony between parents and pre-lingual children and/or children with communication needs.

Interpersonal neural synchronization during social interactions in close relationships: A systematic review and meta-analysis of fNIRS hyperscanning studies

In recent years, researchers have used hyperscanning techniques to explore how brains interact during various human activities. These studies have revealed a phenomenon called interpersonal neural synchronization (INS), but little research has focused on the overall effect of INS in close relationships. To address this gap, this study aims to synthesize and analyze the existing literature on INS during social interactions in close relationships. We conducted a meta-analysis of 17 functional near-infrared spectroscopy (fNIRS) hyperscanning studies involving 1149 dyads participants, including romantic couples and parent-child dyads. The results revealed robust and consistent INS in the frontal, temporal, and parietal regions of the brain and found similar INS patterns in couples and parent-child studies, providing solid empirical evidence for the attachment theory. Moreover, the age of children and brain areas were significant predictors of the effect size in parent-child research. The developmental stage of children and the mismatched development of brain structures might be the crucial factors for the difference in neural performance in social and cognitive behaviors in parent-child dyads.

Mothers and fathers show different neural synchrony with their children during shared experiences

Parent-child shared experiences has an important influence on social development in children although contributions of mothers and fathers may differ. Neural synchronicity occurs between mothers and fathers and their children during social interactions but it is unclear whether they differ in this respect. We used data from simultaneous fNIRS hyperscanning in mothers (n = 33) and fathers (n = 29) and their children (3-4 years) to determine different patterns and strengths of neural synchronization in the frontal cortex during co-viewing of videos or free-play. Mothers showed greater synchrony with child than fathers during passive viewing of videos and the synchronization was positively associated with video complexity and negatively associated with parental stress. During play interactions, mothers showed more controlling behaviors over their child and greater evidence for joint gaze and joint imitation play with child whereas fathers spent more time gazing at other things. In addition, different aspects of child communication promoted neural synchrony between mothers and fathers and child during active play interactions. Overall, our findings indicate greater neural and behavioral synchrony between mothers than fathers and young children during passive or active shared experiences, although for both it was weakened by parental distress and child difficulty.

Coregulation: A Multilevel Approach via Biology and Behavior

In this article, we explore the concept of coregulation, which encompasses the mutual adaptation between partners in response to one another's biology and behavior. Coregulation operates at both biological (hormonal and nervous system) and behavioral (affective and cognitive) levels and plays a crucial role in the development of self-regulation. Coregulation extends beyond the actions of individuals in a dyad and involves interactive contributions of both partners. We use as an example parent-child coregulation, which is pervasive and expected, as it emerges from shared genetic relatedness, cohabitation, continuous interaction, and the influence of common factors like culture, which facilitate interpersonal coregulation. We also highlight the emerging field of neural attunement, which investigates the coordination of brain-based neural activities between individuals, particularly in social interactions. Understanding the mechanisms and significance of neural attunement adds a new dimension to our understanding of coregulation and its implications for parent-child relationships and child development.

Charting the social neuroscience of human attachment (SoNeAt)

This introduction aims to set out the potential as well as some of the pitfalls of the newly emerging area of the Social Neuroscience of Human Attachment (SoNeAt). To organize and interconnect the burgeoning empirical studies in this line of research, including those in this special issue, we outline a programmatic framework including an extension of our conceptual proposals NAMA and NAMDA to guide future research. We hope that this special issue will act as a stimulus for redoubling our efforts advancing the newly emerging SoNeAt area bridging attachment theory and social neuroscience.

The specificity, situational modulations, and behavioral correlates of parent-child neural synchrony

In recent years, aiming to uncover the neural mechanism of parent-child interaction and link it to the children's social development, a newly developed index, namely, parent-child inter-brain neural synchronization (INS) has attracted growing interest. Existing studies have mainly focused on three aspects of the INS; these are the specificity of the INS (i.e., stronger INS for parent-child dyads than stranger-child dyads), the situational modulations of the INS (i.e., how the valence of the situation or the types of interaction modulate INS), and the associations between the INS and the state-like behavioral tendencies or trait-like individual features of the parents and children. This review summarizes the existing findings in line with these three topics and provides preliminary suggestions to promote parent-child INS. In the meanwhile, the inconsistent findings and unstudied questions were discussed, opening new avenues for future studies.

Habilitation of sleep problems among mothers and their children with autism spectrum disorder: Insights from multi-level exploratory dyadic analyses

Few habilitation strategies for children with autism spectrum disorder (ASD) consider their sleep-related problems. Together with the fact that caregivers of children with ASD also face issues with sleep, there may be yet-to-be uncovered relationships between caregiver-child sleep patterns and sleep quality, offering a key opportunity for clinicians to consider the needs of both child and caregiver in terms of sleep. 29 dyads of mothers and their children with ASD were recruited for this cohort study and both subjective (self-report questionnaires and sleep diaries) and objective (cortisol samples and actigraphy) measures of sleep were collected to investigate significant predictors of sleep quality. Comparative, correlational, and hierarchical analyses were conducted. Findings indicated that both mother and child experience sleep deprivation in terms of shorter sleep duration and poor sleep quality in terms of longer sleep onset latencies and a higher frequency of wake bouts. Exploratory hierarchical analyses also found that child-related sleep difficulties such as sleep disordered breathing and night waking significantly predict mothers' sleep quality, which may point to the bi-directional influence of mother-child sleep. Based on these findings, it is recommended that clinicians adopt a family systems perspective and consider the sleep environment of the household, particularly that of the caregiver and child, when designing interventions for sleep-related problems in ASD. Finally, there is a need for additional support to promote good quality sleep among caregivers of children with ASD to bolster out-of-clinic care.

Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

Asymmetric Prefrontal Cortex Activation Associated with Mutual Gaze of Mothers and Children during Shared Play

Mother–child shared play provides rich opportunities for mutual symmetrical interactions that serve to foster bond formation in dyads. Mutual gaze, a symmetrical behaviour that occurs during direct eye contact between two partners, conveys important cues of social engagement, affect and attention. However, it is not known whether the prefrontal cortical areas responsible for higher-order social cognition of mothers and children likewise exhibit neural symmetry; that is, similarity in direction of neural activation in mothers and children. This study used functional Near-infrared Spectroscopy (fNIRS) hyperscanning on 22 pairs of mothers and their preschool-aged children as they engaged in a 10-min free-play session together. The play interaction was video recorded and instances of mutual gaze were coded for after the experiment. Multivariate linear regression analyses revealed that neural asymmetry occurred during mother–child mutual gaze, where mothers showed a deactivation of prefrontal activity whereas children showed an activation instead. Findings suggest that mothers and children may employ divergent prefrontal mechanisms when engaged in symmetrical behaviours such as mutual gaze. Future studies could ascertain whether the asymmetric nature of a parent–child relationship, or potential neurodevelopmental differences in social processing between adults and children, significantly contribute to this observation.

Assessing Computational Methods to Quantify Mother-Child Brain Synchrony in Naturalistic Settings Based on fNIRS Signals

Mother-child brain-to-brain synchrony captures the temporal similarities in brain signals between dyadic partners, and has been shown to emerge during the display of joint behaviours. Despite the rise in the number of studies that investigate synchrony in naturalistic contexts, the use of varying methodological approaches to compute synchrony remains a central problem. When dyads engage in unstructured social interactions, the wide range of behavioural cues they display contribute to the use of varying lengths of signals to compute synchrony. The present functional Near-infrared Spectroscopy (fNIRS) study investigates how different methods to quantify brain signals during joint and non-joint portions of dyadic play affect the outcome of brain-to-brain synchrony. Three strategies to cope with unstructured data are tested and different signal lengths of 15, 20, 25, 30, 35, 40, 45s were used to determine the optimal method to sensitively capture synchrony. Results showed that using all available portions of the signals generated a greater number of less conservative results compared to the other two strategies, which were to compute the average synchrony for the joint and non-joint signals portions and to compute the difference between the average synchrony of joint and non-joint portions. From the different signal durations, only length portions of 25s to 35s generated significant results. These findings demonstrate that differences in computational approaches and signal lengths affect synchrony measurements and should be considered in naturalistic synchrony studies.

Interpersonal Synchrony in the Context of Caregiver-Child Interactions: A Document Co-citation Analysis

Social interactions accompany individuals throughout their whole lives. When examining the underlying mechanisms of social processes, dynamics of synchrony, coordination or attunement emerge between individuals at multiple levels. To identify the impactful publications that studied such mechanisms and establishing the trends that dynamically originated the available literature, the current study adopted a scientometric approach. A sample of 543 documents dated from 1971 to 2021 was derived from Scopus. Subsequently, a document co-citation analysis was conducted on 29,183 cited references to examine the patterns of co-citation among the documents. The resulting network consisted of 1,759 documents connected to each other by 5,011 links. Within the network, five major clusters were identified. The analysis of the content of the three major clusters-namely, "Behavioral synchrony," "Towards bio-behavioral synchrony," and "Neural attunement"-suggests an interest in studying attunement in social interactions at multiple levels of analysis, from behavioral to neural, by passing through the level of physiological coordination. Furthermore, although initial studies on synchrony focused mostly on parent-child interactions, new hyperscanning paradigms are allowing researchers to explore the role of biobehavioral synchrony in all social processes in a real-time and ecological fashion. Future potential pathways of research were also discussed.

Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.