arXiv subjects

Shanbao Tong

Publications and source records attributed to Shanbao Tong.

11 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↗

arXiv2026

The immediate effect of kangaroo mother care on Mother-infant inter-brain synchrony and infant brain function

Kangaroo mother care (KMC) is an intervention involving skin-to-skin contact that promotes physiological stability and supports long-term neurodevelopment in preterm infants. However, the underlying neurophysiological mechanisms remain unclear. We aimed to investigate the immediate effects of the first KMC on infants' brain function, mother-infant inter-brain synchrony, as well as their associations. Fifty-eight preterm infants (gestational age < 32 weeks or birth weight < 1500 g) and their mothers underwent synchronous dual-electroencephalography recording before and during the first KMC session. Infant brain function was assessed via power spectrum energy and graph theory-based network metrics, and mother-infant inter-brain synchrony was quantified using phase-locking value (PLV), from which inter-brain density and inter-brain strength were calculated. Correlation analyses were performed between infant intra-brain metrics and inter-brain synchrony indicators.During the first KMC, preterm infants showed enhanced theta, alpha, and beta power alongside reduced relative delta power, while brain network topological metrics remained stable. Concurrently, mother-infant inter-brain synchrony was significantly enhanced across all frequency bands, as evidenced by increased inter-brain density and strength (all p < .001). Furthermore, in the alpha band, inter-brain strength correlated positively with infant local efficiency and clustering coefficient, and in the beta band, it was positively correlated with infant small-worldness. The first KMC session can immediately enhance both preterm infant single-brain activity and mother-infant inter-brain synchrony. The strength of inter-brain synchrony is associated with the infant's intra-brain network organization, suggesting that KMC may promote intra-brain development in preterm infants via enhancing mother-infant inter-brain synchrony.

q-bio.NC↗

arXiv2026

Beyond Point Estimates: Toward Proper Statistical Inferencing and Reporting of Intraclass Correlation Coefficients

Reporting test-retest reliability using the intraclass correlation coefficient (ICC) has received increasing attention due to the criticisms of poor transparency and replicability in neuroimaging research, as well as many other biomedical studies. Numerous studies have thus evaluated the reliability of their findings by comparing ICCs, however, they often failed to test statistical differences between ICCs or report confidence intervals. Relying solely on point estimates may preclude valid inference about population-level differences and compromise the reliability of conclusions. To address this issue, this study systematically reviewed the use of ICC in articles published in NeuroImage from 2022 to 2024, highlighting the prevalence of misreporting and misuse of ICCs. We further provide practical guidelines for conducting appropriate statistical inference on ICCs. For practitioners in this area, we introduce an online application for statistical testing and sample size estimation when utilizing ICCs. We recalculated confidence intervals and formally tested ICC values reported in the reviewed articles, thereby reassessing the original inferences. Our results demonstrate that exclusive reliance on point estimates could lead to unreliable or even misleading conclusions. Specifically, only two of the eleven reviewed articles provided unequivocally valid statistical inferences based on ICCs, whereas two articles failed to yield any valid inference at all, raising serious concerns about the replicability of findings in this field. These results underscore the urgent need for rigorous inferential frameworks when reporting and interpreting ICCs.

stat.ME↗

arXiv2025

The Prevalence of Misreporting and Misinterpreting Correlation Coefficients in Biomedical Literature

Correlation coefficient is widely used in biomedical and biological literature, yet its frequent misuse and misinterpretation undermine the credibility and reproducibility of the scientific findings. We systematically reviewed 1326 records of correlation analyses across 310 articles published in Science, Nature, and Nature Neuroscience in 2022. Our analysis revealed a troubling pattern of poor statistical reporting and inferring: 58.71% (95% CI: [53.23%, 64.19%], 182/310) of studies did not explicitly report sample sizes, and 98.06% (95% CI: [96.53%, 99.60%], 304/310) failed to provide confidence intervals for correlation coefficients. Among 177 articles inferring correlation strength, 45.25% (95% CI: [38.42%, 53.10%], 81/177) relied solely on point estimates, while 53.63% (95% CI: [46.90%, 61.58%], 96/177) drew conclusions based on null hypothesis significance testing. This widespread omission and misuse highlight a systematic gap in both statistic literacy and editorial standards. We advocate clear reporting guidelines mandating effect sizes and confidence intervals in correlation analyses to enhance the transparency, rigor, and reproducibility of quantitative life sciences research.

stat.ME↗

arXiv2025

Label-Free Intraoperative Imaging of Hemodynamics using Deep Learning

Intraoperative visualization of hemodynamics is crucial for accurate diagnosis and informed surgical decision-making. In neurosurgery, indocyanine green fluorescence imaging (ICG-FI) is the gold standard for assessing blood flow and identifying vascular structures. However, it is limited by time-consuming data acquisition, mandatory waiting periods, potential allergic reactions, and operational complexities. Label-free alternatives, such as laser speckle contrast imaging (LSCI) and white light imaging (WLI), offer real-time vascular assessment but cannot resolve arterial-venous differentiation or blood flow direction determination. To address these challenges, we present a label-free cross-modal generation framework to synthesize mean transition time (MTT) maps from LSCI and WLI. MTT maps encode local hemodynamics, enabling artery-vein differentiation and flow direction inference. Experimental validation in rat brains demonstrates that the proposed method presents clear vasculature delineation, accurate artery-vein differentiation, and reliable blood flow direction decoding, while reducing total imaging time by 95.8% compared to conventional ICG protocols. This approach offers a fast, efficient, and contrast-free solution for continuous intraoperative surgical guidance.

physics.med-ph↗

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↗

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↗

arXiv2022

Random matrix description of dynamically backscattered coherent waves propagating in a wide-field-illuminated random medium

The wave propagation in random medium plays a critical role in optics and quantum physics. Multiple scattering of coherent wave in a random medium determines the transport procedure. Brownian motions of the scatterers perturb each propagation trajectory and form dynamic speckle patterns in the backscattered direction. In this study, we applied the random matrix theory (RMT) to investigate the eigenvalue density of the backscattered intensity matrix. We find that the dynamic speckle patterns can be utilized to decouple the singly and multiply backscattered components. The Wishart random matrix of multiple scattering component is well described by the Marcenko-Pastur law, while the single scattering part has low-rank characteristic. We therefore propose a strategy for estimating the first and the second order moments of single and multiple scattering components, respectively, based on the Marcenko-Pastur law and trace analysis. Electric field Monte Carlo simulation and in-vivo experiments demonstrate its potential applications in hidden absorbing object detection and in-vivo blood flow imaging. Our method can be applied to other coherent domain elastic scattering phenomenon for wide-filed propagation of microwave, ultrasound and etc.

physics.optics↗

arXiv2021

Statistical Estimation of Ballistic Signal in Visible Light OCT Based on Random Matrix Description

Visible light optical coherence tomography (vis-OCT) provides a unique tool for imaging the structure and oxygen metabolism in tissues. However, since it works in the spectral domain, vis-OCT still suffers from noises due to the multiple scatterings, e.g. for imaging the human fundus. In this study, we modeled the OCT signals as a hybrid of single and multiple scattering components using Wishart random matrix description, with which the single scattering component thus can be separated out using the low-rank characteristics of the matrix. The model was validated using Monte Carlo simulation. We further demonstrated that this model could significantly improve the imaging performances in human fundus, showing more details of the vascular structure than the current vis-OCT and an increase of signal-to-noise ratio (SNR) up to more than 10dB. The layer structure of the retina can be better revealed with more than 3dB suppression of the blood scattering in OCT signals.

physics.med-ph↗

arXiv2021

Extracting single- and multiple-scattering components in laser speckle contrast imaging of tissue blood flow

Random matrix theory provides new insights into multiple scattering in random media. In a recent study, we demonstrated the statistical separation of single- and multiple-scattering components based on a Wishart random matrix. The first- and second-order moments were estimated through a Wishart random matrix constructed using dynamically-backscattered speckle images. In this study, this new strategy was applied to laser speckle contrast imaging (LSCI) of in-vivo blood flow. The random matrix-based method was adapted and parameterized using electric field Monte Carlo simulations and in-vitro blood flow phantom experiments. The new method was further applied in in-vivo experiments, demonstrating the benefits of separating the single- and multiple-scattering components, and was compared with the traditional temporal LASCA method. More specifically, the new method captures stimulus-induced functional changes in blood flow and tissue perfusion in the superficial and deeper layers. The new method extends the ability of LSCI to image functional and pathological changes.

physics.med-ph↗

arXiv2019

Gamma band oscillations reflect sensory and affective dimensions of pain

Pain is a multidimensional process, which can be modulated by emotions, however, the mechanisms underlying this modulation are unknown. We used pictures with different emotional valence (negative, positive, neutral) as primes and applied electrical painful stimuli as targets to healthy participants. We assessed pain intensity and unpleasantness ratings and recorded electroencephalograms (EEG). We found that pain unpleasantness, and not pain intensity ratings were modulated by emotion, with increased ratings for negative and decreased for positive pictures. We also found two consecutive gamma band oscillations (GBOs) related to pain processing from time frequency analyses of the EEG signals. An early GBO had a cortical distribution contralateral to the painful stimulus, and its amplitude was positively correlated with intensity and unpleasantness ratings, but not with prime valence. The late GBO had a centroparietal distribution and its amplitude was larger for negative compared to neutral and positive pictures. The emotional modulation effect (negative versus positive) of the late GBO amplitude was positively correlated with pain unpleasantness. The early GBO might reflect the overall pain perception, possibly involving the thalamocortical circuit, while the late GBO might be related to the affective dimension of pain and top-down related processes.

q-bio.NC↗