Biology subjects

Bizzego, A.

Publications and source records attributed to Bizzego, A..

14 recordsLinked to original sources

bioRxiv2026

Left Temporoparietal Neural Synchrony Tracks Cooperative Performance Across Adult Dyads

Cooperation requires interacting individuals to coordinate their actions toward shared goals, and interpersonal neural synchrony (INS) has been proposed as a neural correlate of this coordination. However, the relationship between INS and cooperative performance remains heterogeneous, and relatively little is known about how it varies across social relationships. The present study examined INS and behavioral performance during a naturalistic cooperative tabletop Jenga task in close-friend, romantic-partner, and mother--child dyads (N = 284 participants). Brain activity was simultaneously recorded using functional near-infrared spectroscopy (fNIRS) hyperscanning, and INS was quantified using wavelet transform coherence across the bilateral inferior frontal gyrus (IFG) and temporoparietal junction (TPJ). Cooperative performance was assessed using tower collapse and the number of valid turns completed during the game. True interacting dyads exhibited significantly greater neural synchrony than surrogate dyads across all examined regional pairings (d range = 0.35-0.55), indicating robust interaction-related inter-brain synchrony. Behavioral performance was not associated with INS at the whole-network level. Region-specific analyses, however, showed that tower collapse was associated with reduced synchrony in the left TPJ-left TPJ (p Bonf = 0.009) and left TPJ-right TPJ (p Bonf = 0.017) pairings, whereas the number of valid turns was unrelated to INS. Follow-up analyses further indicated that the association between tower collapse and left TPJ--left TPJ synchrony was present in close-friend (p Bonf = 0.020) and romantic-partner (p Bonf = 0.026) dyads but not in mother-child dyads. These findings suggest that the behavioral relevance of interpersonal neural synchrony is not uniform, but depends on the neural systems involved, the specific index of cooperative performance, and the relational and developmental context in which interaction unfolds.

neuroscience↗

bioRxiv2026

Wavelet Transform Coherence Under Controlled Temporal Correspondence and Social Co-Presence: Implications for fNIRS Hyperscanning

Interpersonal neural synchrony research using functional near-infrared spectroscopy (fNIRS) frequently relies on wavelet transform coherence (WTC) to quantify temporal correspondence between cortical signals. Yet, whether WTC reliably recovers experimentally defined differences in signal correspondence remains insufficiently established. Here, we evaluated WTC using an auditory-guided finger-tapping paradigm in which matching and non-matching temporal structures were compared within participants and between dyad members during individual and joint performance. Task-evoked analyses confirmed reliable recruitment of contralateral motor and premotor regions across both contexts, whereas the ipsilateral motor cortex was significantly recruited only during joint performance and was the only region differing between sessions. WTC sensitivity was strongly modulated by social co-presence. Within participants, WTC failed to discriminate matching from non-matching temporal structures during individual performance, whereas robust matching-related increases emerged during joint performance, particularly in contralateral motor and premotor regions. Between participants, WTC showed limited and region-specific discrimination when participants were tested separately, but higher coherence for matching temporal structures emerged during joint performance, particularly in motor regions. These findings show that WTC can recover experimentally defined temporal correspondence between fNIRS signals, but that this sensitivity is not context-independent. The within-participant results further suggest that social co-presence may alter the temporal dynamics of the individual neural signals themselves. WTC should therefore be interpreted as a context-sensitive measure whose meaning depends on the functional and social processes generating the signals being compared.

neuroscience↗

bioRxiv2026

Physiological Group Synchrony Captures Individual Perceptions of Trust and Cohesion and Collective Performance through Multi-Channel Network Analysis

Humans coordinate in groups, yet much of what we know about coordination comes from dyadic interaction. In groups, however, how much people synchronize may not be enough. Pairwise synchrony can be distributed across teammates: who synchronizes with whom, and how these connections are distributed, may carry distinct social information. Here, we tested whether physiological group synchrony can be understood as a network topology of interpersonal coordination rather than as an aggregate of dyadic alignments. A total of 33 groups (N = 161 participants) completed cooperative and competitive joint drumming tasks while cardiac inter-beat intervals (IBI) and electrodermal activity (EDA) were recorded simultaneously. Pairwise physiological synchrony was estimated using lagged cross-correlation and used to construct synchrony-based networks. IBI-based networks showed stronger and broader cardiac connectivity during cooperation, with cardiac coupling also increasing during competitive drumming relative to baseline. EDA-based networks also showed task-sensitive changes in node-level connectivity and captured social and behavioral dimensions of group interaction, linking trust and cohesion to node-level connectivity, and collective behavioral synchrony to lower clustering and higher local efficiency. Together, these findings show that the social meaning of physiological group synchrony lies not only in how much individuals align, but in how physiological alignment is distributed across people, relationships, and biological systems. By preserving this configuration, synchrony-based network analysis captures information about group experience that is lost when synchrony is reduced to an average dyadic score, providing a route toward understanding group synchrony as a flexible, selective, and distributed property of social coordination.

physiology↗

bioRxiv2026

Over-Synchrony: Higher Maternal Neuroticism Associates with Stronger Interpersonal Neural Synchrony with Child During Passive and Free Interactions

Interpersonal neural synchrony (INS) in mother-child dyads is often interpreted as a neural marker of relational quality and sensitive caregiving, yet findings on its predictors remain heterogeneous. One possible source of this variability is the diversity of interactional paradigms used in hyperscanning research. This study examined how maternal personality, child temperament, and affective states relate to INS across interaction contexts varying in social interactivity. Thirty-three mother-child dyads (n = 20 female children) participated in a functional near-infrared spectroscopy hyperscanning experiment involving passive video co-exposure, a structured cooperative task, and free interaction. Fronto-temporal activity was recorded simultaneously, and INS was computed using wavelet transform coherence. Above-chance levels of INS emerged in inter-brain region combinations primarily involving the mothers left inferior frontal gyrus (IFG) and the childs right IFG (adjusted ps < 0.030, Cohens d range = 0.14-0.31). Maternal neuroticism was the only significant predictor of INS, with higher levels associated with increased synchrony during passive video co-exposure (adjusted p = 0.012) and free interaction (adjusted p = 0.021), but not during the structured game. These findings indicate that maternal dispositional traits shape INS in a context-dependent manner. Notably, the positive association between neuroticism and INS suggests that heightened neural synchrony may reflect over-attunement in more anxious caregivers, rather than optimal coordination. Excessive synchrony may therefore index tightly coupled, over-monitoring interaction dynamics, consistent with models of affiliative vigilance in anxious parenting. Overall, INS may follow a non-linear pattern in which moderate levels are most adaptive, highlighting its flexible, dynamic, and context-sensitive nature.

neuroscience↗

bioRxiv2025

Emotional and Linguistic Features Predict Prefrontal Functional Connectivity during Ongoing Dialogues: An fNIRS Investigation

Identifying the neural bases of language has been a central focus in neuro-science since the pioneering case studies by Broca and Wernicke. Contemporary research has moved beyond classical modular models to conceptualize language as supported by a distributed network of anatomically and functionally interconnected brain regions. Yet, few studies have explored how these networks operate during spontaneous, real-life speech, limiting our understanding of language in natural contexts. In this study, we collected data from 84 individuals engaged in live conversations. Participants prefrontal brain activity was recorded using functional near-infrared spectroscopy hyperscanning, and functional connectivity was quantified via wavelet transform coherence. Dialogues were manually transcribed, and computational methods were applied to extract emotional and semantic/syntactic features from the speech data. Using linear mixed-effects models, we found that emotional content significantly predicted prefrontal functional connectivity [Formula] Among all emotional predictors, expressed anger was the most robust: higher anger levels were associated with reduced connectivity between the left middle frontal gyrus and the right inferior frontal gyrus [Formula]. While other semantic and syntactic features did not predict overall connectivity, degree assortativity--an index of linguistic structure--was negatively associated with connectivity between the superior frontal gyri and the left inferior frontal gyrus [Formula]. These findings highlight how both affective and structural properties of speech modulate prefrontal connectivity during real-life interaction. More broadly, this work demonstrates the potential of integrating computational linguistics with social neuroscience to uncover the neural mechanisms of real-life social interactions.

neuroscience↗

bioRxiv2025

Interpersonal Neural Synchrony Across Levels of Interpersonal Closeness and Social Interactivity

Interpersonal neural synchrony is a fundamental aspect of social interactions, offering insights into the neural mechanisms underlying human connection and developmental outcomes. So far, hyperscanning studies have examined synchrony across different dyads and tasks, leading to inconsistencies in experimental findings and limiting cross-study comparability. This variability has posed challenges for building a unified theoretical framework for neural synchrony. This study investigated the effects of interpersonal closeness and social interactivity on neural synchrony using functional near-infrared spectroscopy hyperscanning. We recorded brain activity from 142 dyads (70 close-friend, 39 romantic-partner, and 33 mother-child dyads) across three interaction conditions: video co-exposure (passive), a cooperative game (structured active), and free interaction (unstructured active). Neural synchrony was computed between participants bilateral inferior frontal gyrus (IFG) and temporoparietal junction (TPJ) using wavelet transform coherence. Results showed that true dyads exhibited significantly higher synchrony than noninteracting surrogate dyads (qs <.001, Cohens d range: 0.17-0.32), particularly in combinations involving the right IFG. Mother-child dyads displayed lower synchrony than adult-adult dyads at the network (p <.001) and local level of analysis, pointing to possible developmental and maturational influences on neural synchrony. At the network level, synchrony was highest during video co-exposure, followed by the cooperative game and free interaction (p <.001). However, left IFG-left IFG and left IFG-right TPJ synchrony peaked during the cooperative game. Although these effects were statistically significant, the overall impact of social interactivity on interpersonal neural synchrony was small, suggesting that the complexity and richness of social exchanges alone may only modestly influence neural synchrony in naturalistic contexts. By comparing different types of dyads and interaction contexts, this study highlights factors that may guide future hypothesis-driven hyperscanning research and contribute incremental evidence to ongoing efforts to understand the neural mechanisms underlying human social interactions.

neuroscience↗

bioRxiv2024

Emotional Content and Semantic Structure of Dialogues Predict Interpersonal Neural Synchrony in the Prefrontal Cortex: a Hyperscanning Studywith Functional Near-Infrared Spectroscopy

A fundamental characteristic of social exchanges is the synchronization of individuals behaviors, physiological responses, and neural activity. However, the association between how individuals communicate in terms of emotional content and expressed associative knowledge and interpersonal synchrony has been scarcely investigated so far. This study addresses this research gap by bridging recent advances in cognitive neuroscience data, affective computing, and cognitive data science frameworks. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, prefrontal neural data were collected during social interactions involving 84 participants (i.e., 42 dyads) aged 18-35 years. Wavelet transform coherence was used to assess interpersonal neural synchrony between participants. We used manual transcription of dialogues and automated methods to codify transcriptions as emotional levels and syntactic/semantic networks. Our quantitative findings reveal higher than random expectations levels of interpersonal neural synchrony in the superior frontal gyrus (q = .038) and the bilateral middle frontal gyri (q < .001, q < .001). Linear mixed models based on dialogues emotional content only significantly predicted interpersonal neural synchrony across the prefrontal cortex[Formula] . Conversely, models relying on syntactic/semantic features were more effective at the local level, for predicting brain synchrony in the right middle frontal gyrus [Formula] Generally, models based on the emotional content of dialogues were not effective when limited to data from one region of interest at a time, whereas models based on syntactic/semantic features show the opposite trend, losing predictive power when incorporating data from all regions of interest. Moreover, we found an interplay between emotions and associative knowledge in predicting brain synchrony, providing quantitative support to the major role played by these linguistic components in social interactions and in prefrontal processes. Our study identifies a mind-brain duality in emotions and associative knowledge reflecting neural synchrony levels, opening new ways for investigating human interactions.

neuroscience↗

bioRxiv2020

Gene-environment interactions in face categorisation: experience with a nanny and oxytocin receptor genotype interact to reduce face categorization reaction times

Human faces are relevant stimuli that capture attention, provide information about group belonging and elicit automatic prepared responses. While early experiences with other race faces plays a critical role in acquiring face expertise, the exact mechanism through which it exerts its influence is still to be elucidated. In particular, the influence of genetic factors and the role of a multi-ethnic context has not been explored. The aim of this study was to investigate how caregiving experiences with nannies and oxytocin receptor gene (OXTR) genotypes interact in regulating other-race categorisation mechanisms in adults. Information about single nucleotide polymorphisms of the OXTR (rs53576) and experiences with own- and other-race nannies was collected from 89 Singaporean adults, who completed a visual categorization task of face stimuli (Chinese or Javanese). Participants were grouped into A/A homozygotes and G-carriers and assigned a score to account for the type of nanny experience. A General Linear Model was used to estimate the effect of nanny experience, genetic group and their interaction on categorization reaction time. A significant main effect of the nanny experience (p<.001) and of the interaction between genetic group and experience (p=.008) was found. Post-hoc analysis revealed a significant negative correlation between nanny experience and reaction time for A/A homozygotes (r=-0.52, p<.001) but no significant correlation for G-carriers. In summary, a significant gene-environment interaction on face categorization was found. This finding appears to represent an indirect pathway through which genes and experiences interact to shape mature social sensitivity in human adults. HighlightsEarly nanny experience interacts with oxytocin receptor genotype in affecting the speed of face categorisation. Individuals with other-race nanny experience show faster categorisation response times. Gene-environment interactions are present in face categorisation.

genetics↗

bioRxiv2020

Father-child dyads exhibit unique inter-subject synchronisation during co-viewing of animation video stimuli

Inter-subject synchronisation reflects the entrainment of two individuals to each others brain signals during passive joint tasks. Within the parent-child dyad, the temporal coordination of signals indicates an attunement to each others emotional states. Despite the ubiquity with which parents and their children watch screen media together, no study has investigated intersubject synchronisation in father-child dyads during such a co-viewing activity. The present study examined whether father-child dyads would exhibit unique inter-subject synchronisation during co-viewing of narrative visual scenes that is unique to the dyad and hence would not be observed in control dyads (i.e. randomly paired signals). Hyperscanning fNIRS was used to record the prefrontal cortex (PFC) signals of 29 fathers and their preschool-aged children (11 girls, 18 boys) as each pair engaged in a typical activity of watching childrens shows together. Three 1-min video clips from "Brave", "Peppa Pig" and "The Incredibles" were presented to each dyad and childrens ratings of video positivity and familiarity were obtained afterwards. PFC activity was analysed according to four clusters: medial left, medial right, frontal left and frontal right clusters. Results from synchrony analyses demonstrated that true father-child dyads showed significantly greater inter-subject synchronisation than control dyads, especially in the medial left cluster during the emotionally arousing conflict scene. Dyads with older fathers displayed less synchrony and older fathers, compared to younger ones, were also found to exhibit greater activity in the frontal right cluster. These findings point to a unique inter-subject synchronisation that exists in father-child relationships during shared co-viewing of narrative scenes which could be potentially modulated by parental age.

neuroscience↗

bioRxiv2020

Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals

A key access point to the functioning of the Autonomic Nervous System is the investigation of peripheral signals. Wearable Devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. But achievable data quality can be lower, subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and Electrodermal Activity signals is validated with a standard set of Signal Quality Indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of 6 different physiological measures collected from 18 subjects with WDs. This study indicates the need of validating the use of WD in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducibility of results.

physiology↗

bioRxiv2020

Maternal Anxious Attachment Style is Associated with Reduced Mother-Child Brain-to-Brain Synchrony

Synchrony in developmental science reflects the coordination of mother and child to the same mental state. Mentalisation processes are influenced by individual attachment styles. A mother with an anxious-related attachment style tends to engage in emotional mentalisation that relies on her childs social cues. During an everyday joint activity of watching television shows together, we hypothesised that anxiously-attached mothers are less able to match their mental state to characters in the shows as their attention is likely detracted from the show and directed towards the child. We predict that this mismatch in mothers and childs emotional states would be reflected in reduced dyadic brain-to-brain synchrony. To test this hypothesis, we profiled mothers Maternal Anxiety score using the Preoccupation and Need for Approval subscales of the Attachment Style Questionnaire (ASQ) and used functional Near-infrared Spectroscopy (fNIRS) hyperscanning with 33 mother-child dyads to measure prefrontal cortex (PFC) synchrony while the dyads watched three 1-min animation videos together. Greater Maternal Anxiety is associated with less synchrony in the medial right prefrontal cluster implicated in mentalisation processes. Anxiously-attached mothers appear to exhibit less brain-to-brain synchrony with their child which suggests differences in intersubjective shared experiences that potentially undermines the quality of bonding during everyday joint activities.

neuroscience↗

bioRxiv2019

Integrating deep and radiomics features in cancer bioimaging

Almost every clinical specialty will use artificial intelligence in the future. The first area of practical impact is expected to be the rapid and accurate interpretation of image streams such as radiology scans, histo-pathology slides, ophthalmic imaging, and any other bioimaging diagnostic systems, enriched by clinical phenotypes used as outcome labels or additional descriptors. In this study, we introduce a machine learning framework for automatic image interpretation that combines the current pattern recognition approach (\"radiomics\") with Deep Learning (DL). As a first application in cancer bioimaging, we apply the framework for prognosis of locoregional recurrence in head and neck squamous cell carcinoma (N=298) from Computed Tomography (CT) and Positron Emission Tomography (PET) imaging. The DL architecture is composed of two parallel cascades of Convolutional Neural Network (CNN) layers merging in a softmax classification layer. The network is first pretrained on head and neck tumor stage diagnosis, then finetuned on the prognostic task by internal transfer learning. In parallel, radiomics features (e.g., shape of the tumor mass, texture and pixels intensity statistics) are derived by predefined feature extractors on the CT/PET pairs. We compare and mix deep learning and radiomics features into a unifying classification pipeline (RADLER), where model selection and evaluation are based on a data analysis plan developed in the MAQC initiative for reproducible biomarkers. On the multimodal CT/PET cancer dataset, the mixed deep learning/radiomics approach is more accurate than using only one feature type, or image mode. Further, RADLER significantly improves over published results on the same data.

bioinformatics↗

bioRxiv2018

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g. from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDAs MAQC project, designed to analyse causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test 3 different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with 4 datasets (5, 10, 20 or 30 classes), for a total 53000 tiles at 512 x 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for random features and random labels diagnostic tests to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning backbone pipeline and the HINT (Histological Imaging - Newsy Tiles) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for Digital Pathology.\n\nAuthor summaryIn this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. It is indeed a top priority that reproducibility-by-design gets adopted as standard practice in building and validating AI methods in the healthcare domain. Here we introduce DAPPER, a first framework to evaluate deep features and classifiers in digital pathology, based on a rigorous data analysis plan originally developed in the FDAs MAQC initiative for predictive biomarkers from massive omics data. We apply DAPPER on models trained to identify tissue of origin from the HINT benchmark dataset of 53000 tiles from 787 Whole Slide Images in the Genotype-Tissue Expression (GTEx) project. We analyze accuracy and feature stability of different deep learning architectures (VGG, ResNet and Inception) as feature extractors and classifiers (a fully connected multilayer, SVMs and Random Forests) on up to 20 classes. Further, we use the deep features from the VGG model (trained on HINT) on the 1300 annotated tiles of the KIMIA24 dataset for identification of slide of origin (24 classes). The DAPPER software is available together with the HINT benchmark dataset.

bioinformatics↗

bioRxiv2017

DBD-RCO: Derivative Based Detection And Reverse Combinatorial Optimization To Improve Heart Beat Detection For Wearable Devices

The diffusion of wearable sensors enables the monitoring of heart physiology in real-life contexts. Wearable technology is characterized by important advantages but also by technical limitations that affect the quality of the collected signals in terms of movement artifacts and presence of noise. Therefore specific signal processing algorithms are required to cope with the lower quality and different characteristic of signals collected with wearable sensor units. Here we propose and validate a pipeline to detect heartbeats in cardiac signals, extract the Inter Beat Intervals (IBI) and compute the Heart Rate Variability (HRV) indicators from wearable devices. In particular, we describe the novel Derivative-Based Detection (DBD) algorithm to estimate the beat position in Blood Volume Pulse (BVP) signals and the Reverse Combinatorial Optimization (RCO) algorithm to identify and correct IBI extraction errors. The pipeline is first validated on data from clinical-grade sensors, then on a benchmark dataset including examples of movement artifacts in a real-life context. The accuracy of the DBD algorithm is assessed in terms of precision and recall of the detection; error in the IBI values is quantified by root mean square error. The reliability of HRV indicators is evaluated by the Bland-Altman ratio. The DBD algorithm performs better than a state-of-art algorithm for both medical-grade and wearable devices. However, as already found in similar studies, worse reliability is found with the BVP signal in computing frequency domain HRV indicators, in particular with wearable devices.

physiology↗