Biology subjects

Carollo, A.

Publications and source records attributed to Carollo, A..

3 recordsLinked to original sources

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↗