Biology subjects

Leong, V.

Publications and source records attributed to Leong, V..

12 recordsLinked to original sources

bioRxiv2025

Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood

The development of functional brain connectivity during early life depends on social experience and is best understood in the context of interactions with a caregiver. It is still an open question to what extent distinct modes of functional connectivity dominate in different stages of human brain development, and whether cognitive tasks can differentially modulate them. Using electroencephalography (EEG), we investigated the development of linear and nonlinear functional brain connectivity in infants and adults while they socially interacted. Using simultaneous EEG recordings in parent-infant dyads performing two different experiments (N = 160; 80 adults and 80 infants), we computed functional connectivity capturing distinct dynamics: a linear measure capturing phase synchronization (weighted phase lag index; WPLI), and a nonlinear measure capturing information sharing (weighted symbolic mutual information; WSMI). In both tasks, adults showed higher WSMI than infants, whereas infants showed higher WPLI than adults. Moreover, infant age predicted only task-related connectivity values computed with the nonlinear measure and not with its linear counterpart, suggesting that the information-theoretic measure was more sensitive to developmental changes in task-relevant neural processing. These findings suggest that over development, a shift in dominance from linear to nonlinear modes of brain communication may be essential for supporting emerging higher cognitive abilities, such as precursors to executive function (here, attention shifting) and social decision-making. Further, this work highlights the importance of using nonlinear measures in addition to traditional linear ones, which collectively permit a more robust capture of maturational changes.

neuroscience↗

bioRxiv2023

A Pose-Informed De-Noising Diffusion Model for Adult Naturalistic EEG Signals

Artifact contamination in EEG (electroencephalogram) signals is a significant problem, especially in naturalistic settings where participants can move freely. This contamination stems from various sources like eye movements, muscle activity, sweat, and electrical interference, whose effects differ greatly from each other. Traditional denoising methods, such as Independent Component Analysis, are limited because they assume a linear relationship between the source of the artifacts and the EEG signals, and often require the dominance of one noise source over others. Moreover, these methods need expert knowledge in EEG analysis and lack an objective standard for evaluation. To overcome these challenges, we propose two innovations: Firstly, we introduce the use of "video-estimated" pose coordinates - the x and y positions of different body points (like wrists, eyes, and ankles) - to assist in the EEG denoising process. Secondly, we present a denoising diffusion model, EEG-DDM, that utilizes both the contaminated EEG signals and these pose coordinates to effectively denoise the EEG. Our findings show that incorporating keypoints (pose coordinates) improves denoising performance and helps maintain cross-spatial dependencies in the data. Additionally, we enhance human interpretability of the process by displaying saliency maps generated by our model, which explain the contributions of these keypoints in the denoising process.

neuroscience↗

bioRxiv2023

Principles for Optimal Window Size Selection for Infant and Adult EEG Connectivity Analysis

Neural connectivity analysis is often performed on continuous data that has been discretized into temporal windows of a fixed length. However, the selection of an optimal window length is non-trivial, and depends on the properties of the connectivity metric being used as well as the effects of interest within the data (e.g. developmental or inter-brain effects). A systematic investigation of these factors, and objective criteria for window size selection are currently missing in the literature, particularly in regard to pediatric datasets. Here, we provide a principled examination of the effect of window size on optimization of signal to noise ratio for linear and non-linear EEG connectivity, as applied to infant, adult and dyadic (infant-adult) datasets. We employed a linear weighted phase lag index (wPLI), and a nonlinear weighted symbolic mutual information (wSMI) metric to assess brain connectivity for each dataset. Our results showed a clear polar dissociation between linear and non-linear metrics, as well as between infant and adult datasets in optimal window size. Further, optimal dyadic (infant-adult) window size settings defaulted to one or the partner rather than reflecting an intermediate compromise. Given the specificity of these results (i.e. there was no single window size that was optimal for all contrasts), we conclude that a formal analysis of optimal window size may be useful prior to conducting any new connectivity analysis. Here, we recommend guiding principles, performance metrics and decision criteria for optimal and unbiased window size selection.

neuroscience↗

bioRxiv2021

Instructor-learner neural synchronization during elaborated feedback predicts learning transfer

The provision of feedback with complex information beyond the correct answer, i.e., elaborated feedback, can powerfully shape learning outcomes such as transfer, i.e., the ability to extend what has been learned in one context to new contexts. However, an understanding of neurocognitive processes of elaborated feedback during instructor-learner interactions remains elusive. Here, a two-person interactive design is used during simultaneous recording of functional near-infrared spectroscopy (fNIRS) signals from adult instructor-learner dyads. Instructors either provided elaborated feedback (i.e., correct answer and an example) or simple feedback (i.e., correct answer only) to learners during a concept learning task. Our results showed that elaborated feedback produced comparable levels of retention to simple feedback, however, transfer was significantly enhanced by elaboration. We also noted significant instructor-learner neural synchronization in frontoparietal regions during the provision of elaborated feedback, especially when examples were provided. Further, interpersonal neural synchronization in the parietal cortex successfully predicted transfer of knowledge to novel contexts. This prediction was retained for both learner-delayed and learner-preceding neural synchronization. These findings point toward transfer effects of elaborated feedback provided in a social context can be predictable through interpersonal neural synchronization, which may hold important implications for real-world learning and pedagogical efficacy. Educational Impact and Implications StatementFeedback provides learners with crucial information regarding the gap between what has currently been achieved and what remains to be achieved, and thus plays a critical role in any learning process. In real-world settings, feedback is typically provided and received through social interaction, and high-quality "elaborated feedback" contains complex information that goes beyond the correct answer. This study aims to elucidate the neurocognitive processes underpinning elaborated feedback during instructor-learner interactions. We detected significant instructor-learner neural synchronization in mutual frontoparietal brain regions during elaborated feedback, particularly during the provision of specific elaborated information (i.e., concrete examples). Moreover, this synchronization (including learner-delayed and learner-preceded synchronization) in the parietal region predicted whether the learners transferred learning to novel examples of learned psychology concepts. This study advances current understanding on the neural mechanisms for elaborated feedback and the role of social interaction in feedback effects. These results may have important implications for successful real-world learning and communication, and related pedagogical applications in educational settings.

scientific communication and education↗

bioRxiv2021

Synchrony of mind and body are distinct in mother-child dyads

Hyperscanning studies have begun to unravel the brain mechanisms underlying social interaction, indicating a functional role for interpersonal neural synchronization (INS), yet the mechanisms that drive INS are poorly understood. While interpersonal synchrony is considered a multimodal phenomenon, it is not clear how different biological and behavioral synchrony markers are related to each other. The current study, thus, addresses whether INS is functionally-distinct from synchrony in other systems - specifically the autonomic nervous system (ANS) and motor behavior. To test this, we used a novel methodological approach, based on concurrent functional near-infrared spectroscopy-electrocardiography, recorded while N = 34 mother-child and stranger-child dyads (child mean age 14 years) engaged in cooperative and competitive tasks. Results showed a marked differentiation between neural, ANS and behavioral synchrony. Importantly, only in the neural domain was higher synchrony for mother-child compared to stranger-child dyads observed. Further, ANS and neural synchrony were positively related during competition but not during cooperation. These results suggest that synchrony in different behavioral and biological systems may reflect distinct processes. Mother-child INS may arise due to neural processes related to social affiliation, which go beyond shared arousal and similarities in behavior.

neuroscience↗

bioRxiv2021

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among adult EEG researchers. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n=44, n=25). Additionally, we examined both classifiers ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset, compared to manual ICA data cleaning. Here the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal, operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

neuroscience↗

bioRxiv2019

Emotional valence modulates the topology of the parent-infant inter-brain network

Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual-brain connectivity approach to assess how emotional valence modulates the parent-infant neural network. Fifteen mothers modelled positive and negative emotions toward pairs of objects during social interaction with their infants (aged 10.3 months) whilst their neural activity was concurrently measured using dual-EEG. Intra-brain and inter-brain network connectivity in the 6-9 Hz (infant Alpha) range was computed during maternal expression of positive and negative emotions using directed (partial directed coherence) and non-directed (phase-locking value) connectivity metrics. Graph theoretical metrics were used to quantify differences in network topology as a function of emotional valence. Inter-brain network indices (Density, Strength and Divisibility) consistently revealed that the integration of parents and childrens neural processes was significantly stronger during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics indicated that mother-to-infant directional influences were stronger during the expression of positive than negative emotions. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in interpersonal network topology.

neuroscience↗

bioRxiv2018

Parental neural responsivity to infants visual attention: how mature brains scaffold immature brains during social interaction.

Almost all attention and learning - in particular, most early learning - takes place in social settings. But little is known of how our brains support dynamic social interactions. We recorded dual-EEG from infants and parents during solo play and joint play. During solo play, fluctuations in infants Theta power significantly forward-predicted their subsequent attentional behaviours. But this forwards-predictiveness was lower during joint play than solo play, suggesting that infants endogenous neural control over attention is greater during solo play. Overall, however, infants were more attentive to the objects during joint play. To understand why, we examined how adult brain activity related to infant attention. We found that parents Theta power closely tracked and responded to changes in their infants attention. Further, instances in which parents showed greater neural responsivity were associated with longer sustained attention by infants. Our results offer new insights into how one partner influences another during social interaction.

neuroscience↗

bioRxiv2017

Topographical and spectral signatures of infant and adult movement artifacts in naturalistic EEG

Electroencephalography (EEG) is perhaps the most widely used brain-imaging technique for paediatric populations. However, EEG signals are prone to distortion by motion. Compared to adults, infants motion is both more frequent and less stereotypical yet motion effects on the infant EEG signal are largely undocumented. Here, we present a systematic assessment of naturalistic motion effects on the infant and adult EEG signal. In Study 1, we documented the prevalence of 27 naturally occurring facial and body motions by video-coding five mother-infant pairs during naturalistic play. In Study 2, we elicited a subset of the most common facial, limb and postural motions from one adult and one infant actor while their EEG was recorded. In Study 3, we recorded EEG signals from a larger group of 12 infants whilst they produced the same motions spontaneously. Our findings from Study 2 suggested that adult movements mainly generated increases in spectral power relative to resting state, primarily at peripheral sites and in delta and high-beta frequency bands. In infants, both elicited motions (N=1, Study 2) and spontaneously-occurring motions (N=12, Study 3) produced decreases in theta, alpha and beta power over central regions and increased beta/delta power at peripheral sites. We also observed that infants jaw and upper limb movements generated more pronounced EEG artifacts than lower limb movements. It is intended that this work will inform future development of methods for addressing EEG motion-related artifacts and support wider use of naturalistic paradigms in social and developmental neuroscience.

neuroscience↗

bioRxiv2017

Towards a neuroscientific understanding of play: A neuropsychological coding framework for analysing infant-adult play patterns

During early life, play is a ubiquitous activity, and an individuals propensity for play is positively related to cognitive development and emotional well-being. Play behaviour is diverse and multi-faceted. A challenge for current research is to converge on a common definition and measurement system for play - whether examined at a behavioural, cognitive or neurological level. Combining these different approaches in a multi-level analysis could yield significant advances in understanding the neurocognitive mechanisms of play, and provide the basis for developing biologically-grounded play models. However, there is currently no integrated framework for conducting a multi-level analysis of play that spans brain, cognition and behaviour. The proposed neuropsychological coding framework uses grounded and observable behaviours along three neuropsychological dimensions (sensorimotor, cognitive and socio-emotional), to compute inferences about playful behaviour and related social interactional states. Here, we illustrate the sensitivity and utility of the proposed coding framework using two contrasting dyadic corpora (N=5) of mother-infant object-oriented interactions during experimental conditions that were either conducive (Condition 1) or non-conducive (Condition 2) to the emergence of playful behaviour. We find that the framework accurately identifies the modal form of social interaction as being either playful (Condition 1) or non-playful (Condition 2), and further provides useful insights about differences in the quality of social interaction and temporal synchronicity within the dyad. In conclusion, here, we present a novel neuropsychological framework for analysing the continuous time-evolution of adult-infant play patterns, underpinned by biologically informed state coding along sensorimotor, cognitive and socio-emotional dimensions. We expect that the proposed framework will have wide utility amongst researchers wishing to employ an integrated, multi-level approach to the study of play, and lead towards a greater understanding of the neuroscientific basis of play and may yield insights into a new biologically-grounded taxonomy of play interactions.

neuroscience↗

bioRxiv2017

Infants’ neural oscillatory processing of theta-rate speech patterns exceeds adults’

During their early years, infants use the temporal statistics of the speech signal to boot-strap language learning, but the neural mechanisms that facilitate this temporal analysis are poorly understood. In adults, neural oscillatory entrainment to the speech amplitude envelope has been proposed to be a mechanism for multi-time resolution analysis of adult-directed speech, with a focus on Theta (syllable) and low Gamma (phoneme) rates. However, it is not known whether developing infants perform multi-time oscillatory analysis of infant-directed speech with the same temporal focus. Here, we examined infants processing of the temporal structure of sung nursery rhymes, and compared their neural entrainment across multiple timescales with that of well-matched adults (their mothers). Typical infants and their mothers (N=58, median age 8.3 months) viewed videos of sung nursery rhymes while their neural activity at C3 and C4 was concurrently monitored using dual-electroencephalography (dual-EEG). The accuracy of infants and adults neural oscillatory entrainment to speech was compared by calculating their phase-locking values (PLVs) across the EEG-speech frequency spectrum. Infants showed better phase-locking than adults at Theta (~4.5 Hz)and Alpha (~9.3 Hz) rates, corresponding to rhyme and phoneme patterns in our stimuli. Infant entrainment levels matched adults for syllables and prosodic stress patterns (Delta,~1-2 Hz). By contrast, infants were less accurate than adults at tracking slow (~0.5 Hz) phrasal patterns. Therefore, compared to adults, language-learning infants temporal parsing of the speech signal shows highest relative acuity at Theta-Alpha rates. This temporal focus could support the accurate encoding of syllable and rhyme patterns during infants sensitive period for phonetic and phonotactic learning. Therefore, oscillatory entrainment could be one neural mechanism that supports early bootstrapping of language learning from infant-directed speech (such as nursery rhymes).

neuroscience↗

bioRxiv2017

Speaker gaze changes information coupling between infant and adult brains

When infants and adults communicate, they exchange social signals of availability and communicative intention such as eye gaze. Previous research indicates that when communication is successful, close temporal dependencies arise between adult speakers and listeners neural activity. However, it is not known whether similar neural contingencies exist within adult-infant dyads. Here, we used dual-electroencephalography to assess whether direct gaze increases neural coupling between adults and infants during screen-based and live interactions. In Experiment 1 (N=17), infants viewed videos of an adult who was singing nursery rhymes with (a) Direct gaze (looking forward); (b) Indirect gaze (head and eyes averted by 20{degrees}); or (c) Direct-Oblique gaze (head averted but eyes orientated forward). In Experiment 2 (N=19), infants viewed the same adult in a live context, singing with Direct or Indirect gaze. Gaze-related changes in adult-infant neural network connectivity were measured using Partial Directed Coherence. Across both experiments, the adult had a significant (Granger)-causal influence on infants neural activity, which was stronger during Direct and Direct-Oblique gaze relative to Indirect gaze. During live interactions, infants also influenced the adult more during Direct than Indirect gaze. Further, infants vocalised more frequently during live Direct gaze, and individual infants who vocalized longer also elicited stronger synchronisation from the adult. These results demonstrate that direct gaze strengthens bi-directional adult-infant neural connectivity during communication. Thus, ostensive social signals could act to bring brains into mutual temporal alignment, creating a joint-networked state that is structured to facilitate information transfer during early communication and learning.

neuroscience↗