arXiv subjects

Yunting Zhang

Publications and source records attributed to Yunting Zhang.

5 recordsLinked to original sources

arXiv2026

Who Is in Mind Matters: Attachment Representations in Early Childhood Synchronize Child-Adult Interacting Brains

Human attachment is distinguished by enduring internalized representations that shapes neurodevelopment and social-emotional functioning. However, as unobservable inner processes mixed with social cues and partner-specific factors, the neurocognitive mechanisms of these representations during real-time interaction remain unclear. Using a novel Remote Partner-Belief Manipulation paradigm in 40 child-mother-stranger trios, we experimentally isolated attachment representations in 3-4-year-olds by manipulating children's partner-belief during remote cooperation. The inner processes were captured from synchrony between partners' EEG, showing that children's mother-partner belief, regardless of the actual partner, significantly enhanced interbrain synchrony. This partner-belief modulation concentrated on children's P4 channel (overlaying the attachment-designated right temporoparietal junction), where synchrony strength correlated to attachment security and children's response acceleration due to mother-partner belief. These findings established attachment representations as an independent, endogenous driver of interbrain synchrony, potentially via children's heightened attention towards their attachment figure, implying the role of symbolic attachment activation when separation.

q-bio.NC↗

arXiv2025

Theta and/or alpha? Neural oscillational substrates for dynamic inter-brain synchrony during mother-child cooperation

Mother-child interaction is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at θ and/or α rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. Through dynamic analysis of dual-EEG from 40 mother-child dyads during turn-taking cooperation, we uncover that θ-IBS and α-IBS alternated with interactive behaviors, with EEG frequency-shift as a prerequisite for IBS transitions. When mothers attempt to track their children's attention and/or predict their intentions, they will adjust their EEG frequencies to align with their children's θ oscillations, leading to a higher occurrence of the θ-IBS state. Conversely, the α-IBS state, accompanied by the EEG frequency-shift to the α range, is more prominent during mother-led interactions. Further exploratory analysis reveals greater presence and stability of the θ-IBS state during cooperative than non-cooperative conditions, particularly in dyads with stronger emotional attachments and more frequent interactions in their daily lives. Our findings shed light on the neural oscillational substrates underlying the IBS dynamics during mother-child interactions.

q-bio.NC↗

arXiv2024

An Efficient Two-Dimensional Functional Mixed-Effect Model Framework for Repeatedly Measured Functional Data

With the rapid development of wearable device technologies, accelerometers can record minute-by-minute physical activity for consecutive days, which provides important insight into a dynamic association between the intensity of physical activity and mental health outcomes for large-scale population studies. Using Shanghai school adolescent cohort we estimate the effect of health assessment results on physical activity profiles recorded by accelerometers throughout a week, which is recognized as repeatedly measured functional data. To achieve this goal, we propose an innovative two-dimensional functional mixed-effect model (2dFMM) for the specialized data, which smoothly varies over longitudinal day observations with covariate-dependent mean and covariance functions. The modeling framework characterizes the longitudinal and functional structures while incorporating two-dimensional fixed effects for covariates of interest. We also develop a fast three-stage estimation procedure to provide accurate fixed-effect inference for model interpretability and improve computational efficiency when encountering large datasets. We find strong evidence of intraday and interday varying significant associations between physical activity and mental health assessments among our cohort population, which shed light on possible intervention strategies targeting daily physical activity patterns to improve school adolescent mental health. Our method is also used in environmental data to illustrate the wide applicability. Supplementary materials for this article are available online.

stat.ME↗

arXiv2023

Inter-brain substrates of role switching during mother-child interaction

Mother-child interaction is highly dynamic and reciprocal. Switching roles in these back-and-forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well-studied. Here, we designed a role-controlled cooperative task with dual EEG recording to study how differently two brains interact when mothers and children hold different roles. When children were actors and mothers were observers, mother-child inter-brain synchrony emerged within the theta oscillations and the frontal lobe, which highly correlated with children's attachment to their mothers. When their roles were reversed, this synchrony was shifted to the alpha oscillations and the central area and associated with mothers' perception of their relationship with their children. The results suggested an observer-actor neural alignment within the actor's oscillations, which was modulated by the actor-toward-observer emotional bonding. Our findings contribute to the understanding of how inter-brain synchrony is established and dynamically changed during mother-child reciprocal interaction.

q-bio.NC↗

arXiv2020

A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy

Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. In this study, we proposed a Hidden Markov Model (HMM) based unsupervised algorithm that can automatically and effectively infer sleep/wake states. It is an individualized data-driven approach that analyzes actigraphy from each individual respectively to learn activity characteristics and further separate sleep and wake states. We used Actiwatch and polysomnography (PSG) data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis to evaluate the performance of our method. Epoch-by-epoch comparisons were made between our HMM algorithm and that embedded in the Actiwatch software (AS). The percent agreement between HMM and PSG was 85.7%, and that between AS and PSG was 84.7%. Positive predictive values for sleep epochs were 85.6% and 84.6% for HMM and AS, respectively, and 95.5% and 85.6% for wake epochs. Both methods have similar performance and tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is able to quantify the variability in activity counts that allow us to differentiate relatively active and sedentary individuals: individuals with higher estimated variabilities tend to show more frequent sedentary behaviors. In conclusion, our unsupervised data-driven HMM algorithm achieves slightly better performance compared to the commonly used algorithm in the Actiwatch software. HMM can help expand the application of actigraphy in large-scale studies and in cases where intrusive PSG is hard to acquire or unavailable. In addition, the estimated HMM parameters can characterize individual activity patterns that can be utilized for further analysis.

stat.AP↗