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Moreau, Q.

Publications and source records attributed to Moreau, Q..

13 recordsLinked to original sources

Distinct beta burst motifs exhibit opposing error relationships during motor adaptation

Beta-band activity (13-30 Hz) is a hallmark of human movement, yet a unifying account of its functional role remains unresolved. Although typically described as a sustained oscillation, beta activity is increasingly recognised to consist of transient bursts. More recently, beta bursts have been shown to exhibit heterogeneous waveforms. Here, we ask whether variability in burst shape corresponds to separable computational roles during motor adaptation. Using high-density MEG, we recorded neural activity while participants performed a visuomotor rotation task under either implicit (sensorimotor adaptation) or explicit (strategic re-aiming) learning conditions. Conventional metrics, beta power and burst rate, showed context-dependent modulation during preparation but provided limited insight into trial-by-trial behaviour. In contrast, sorting bursts according to their waveforms revealed a repertoire of burst types with dissociable temporal dynamics and context-dependent modulation. Crucially, during post-movement evaluation, distinct burst subtypes showed opposing and temporally specific relationships with behavioural error: one subtype decreased with increasing error, whereas others increased. Together, these findings indicate that beta activity comprises separable transient events with specific computational roles, and that accounting for waveform diversity is essential for understanding how cortical beta supports adaptive behaviour.

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Beyond deep versus superficial: true laminar inference with MEG

Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.

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Online HD-tRNS over the right temporoparietal junction modulates social inference but not motor coordination

Social interactions are fundamental to human cognition, with the right temporoparietal junction (rTPJ) playing a key role in integrating motor coordination and social inference. Transcranial random noise stimulation (tRNS) is a promising technique for modulating cortical excitability, yet its effect on dynamic social processes remains unexplored. This study aimed to establish a causal link between rTPJ and real-time social interaction by applying high-definition transcranial random noise stimulation (HD-tRNS) during the Human Dynamic Clamp (HDC) paradigm. Specifically, we investigated whether online HD-tRNS modulates motor coordination and social inference when interacting with an adaptive virtual partner (VP). Eighty right-handed participants were assigned to one of two experiments: (Exp1) a block design with active HD-tRNS and sham blocks or (Exp2) a trial-by-trial design with intermixed active and sham conditions. Participants engaged in an interactive coordination task with a covert VP that could behave cooperatively or competitively. Kinematic data and self-reported measures of perceived cooperativeness and humanness were analyzed. HD-tRNS over the rTPJ did not influence motor coordination or task performance. However, in Exp1, participants receiving active stimulation progressively attributed lower cooperativeness and humanness to the competitive VP, suggesting improved detection of competitive intent. This effect was absent in Exp2, indicating that repeated stimulation was necessary for cumulative neuromodulatory effects. Online HD-tRNS over the rTPJ does not acutely impact motor coordination but progressively modulates social inference, particularly in competitive interactions. The findings highlight the rTPJs role in self-other distinction and suggest that repeated short neuromodulation sessions can shape social perception. Significant statementSocial interactions rely on distinguishing between cooperative and competitive behaviors, a process involving the right temporoparietal junction (rTPJ). Using high-definition transcranial random noise stimulation (HD-tRNS), we tested the rTPJs causal role in real-time social inference. While HD-tRNS did not affect motor coordination, repeated stimulation progressively reduced perceived cooperativeness and humanness of a competitive virtual partner, suggesting enhanced sensitivity to competitive intent. Our findings provide insights into social perception mechanisms and suggest HD-tRNS as a potential tool for studying and modulating social cognition, with implications for conditions affecting social inference, such as autism and schizophrenia.

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Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach

AbstractThis study introduces a self-supervised learning (SSL) approach to hyperscanning electroencephalography (EEG) data, targeting the identification of autism spectrum condition (ASC) during social interactions. Hyperscanning enables simultaneous recording of neural activity across interacting individuals, offering a novel path for studying brain-to-brain synchrony in ASC. Leveraging a large-scale, single-brain EEG dataset for SSL pretraining, we developed a multi-brain classification model fine-tuned with hyperscanning data from dyadic interactions involving ASC and neurotypical participants. The SSL model demonstrated superior performance (78.13% accuracy) compared to supervised baselines and logistic regression using spectral EEG biomarkers. These results underscore the efficacy of SSL in addressing the challenges of limited labeled data, enhancing EEG-based diagnostic tools for ASC, and advancing research in social neuroscience.

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Dopaminergic modulation of behavioral and electrocortical markers of interpersonal performance monitoring in Parkinson's Disease: insights from multivariate and univariate analyses

Effective interpersonal interaction necessitates constant monitoring and adaptation to others actions, a process known as interpersonal performance monitoring, which is influenced by the dopaminergic system and marked by specific electrocortical signatures. To explore the connection between deficits in interpersonal performance monitoring and altered neural markers, we assessed patients with Parkinsons Disease (PD) performing coordination tasks with a virtual partner (VP) under two conditions: on dopaminergic medication (PD ON) and after withdrawal (PD OFF). In Interactive trials, which required adaptation to the VPs actions, PD OFF performance was impaired compared to PD ON. EEG analysis revealed in PD OFF increased midfrontal Delta-Theta activity during Interactive trials. Higher Delta-Theta synchronization was associated with improved performance, suggesting compensatory mechanisms. Multivariate EEG analysis distinguished Interactive from Cued trials, especially in PD OFF. Our findings highlight dopamine s role in modulating electrocortical markers of interpersonal performance monitor, with significant implications for understanding and treating PD.

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The bodily appearance of a virtual partner affects the activity of the action observation and action monitoring systems in a minimally interactive task

One pending question in social neuroscience is whether interpersonal interactions are processed differently by the brain depending on the bodily characteristics of the interactor, i.e., their physical appearance. To address this issue, we engaged participants in a minimally interactive task with an avatar either showing bodily features or not while recording their brain activity using Electroencephalography (EEG) in order to investigate indices of action observation and action monitoring processing. Multivariate results showed that bodily compared to non-bodily appearance modulated parieto-occipital neural patterns throughout the entire duration of the observed movement and that, importantly, such patterns differ from the ones related to initial shape processing. Furthermore, among the electrocortical indices of action monitoring, only the early observational Positivity (oPe) was responsive to the bodily appearance of the observed agent under the specific task requirement to predict the partner movement. Taken together, these findings broaden the understanding of how bodily appearance shapes the spatiotemporal processing of an interactors movements. This holds particular relevance in our modern society, where human-virtual agent interactions are rapidly becoming ubiquitous.

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Distinct Social Behavior and Inter-Brain Connectivity in Dyads with Autistic Individuals

Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imitation skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The studys findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.

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Early categorization of social affordances during the visual encoding of bodily stimuli

Interpersonal interactions rely on various communication channels, both verbal and non-verbal, through which information regarding ones intentions and emotions are perceived. Here, we investigated the neural correlates underlying the visual processing of hand postures conveying social affordances (i.e., hand-shaking), compared to control stimuli such as hands performing non-social actions (i.e., grasping) or showing no movement at all. Combining univariate and multivariate analysis on electroencephalography (EEG) data, our results indicate that occipito-temporal electrodes show early differential processing of stimuli conveying social information compared to non-social ones. First, the amplitude of the Early Posterior Negativity (EPN, an Event-Related Potential related to the perception of body parts) is modulated differently during the perception of social and non-social content carried by hands. Moreover, our multivariate classification analysis (MultiVariate Pattern Analysis - MVPA) expanded the univariate results by revealing early (<200ms) categorization of social affordances over occipito-parietal sites. In conclusion, we provide new evidence suggesting that the encoding of socially relevant hand gestures is categorized in the early stages of visual processing.

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A Neurodynamic Model of Inter-Brain Coupling in the Gamma Band

The use of EEG to simultaneously record multiple brains (i.e., hyperscanning) during social interactions has led to the discovery of inter-brain coupling (IBC). IBC is defined as the neural synchronization between people and is considered to be a marker of social interaction. IBC has previously been observed across different frequency bands, including Theta [4-7 Hz]. Given the proximity of this frequency range with behavioral rhythms, models have been able to combine IBC in Theta with sensorimotor coordination patterns. Interestingly, empirical EEG-hyperscanning results also report the emergence of IBC in the Gamma range [>30 Hz]. Gamma oscillations fast and transient nature makes a direct link between Gamma-IBC and other (much slower) interpersonal dynamics difficult, leaving Gamma-IBC without a plausible model. However, at the intra-brain level, Gamma activity is coupled with the dynamics of lower frequencies through cross-frequency coupling (CFC). This paper provides a biophysical explanation for the emergence of Gamma inter-brain coupling using a Kuramoto model of four oscillators divided into two separate (brain) units. By modulating both the degree of inter-brain coupling in the Theta band (i.e., between-units coupling) and CFC (i.e., intra-unit Theta-Gamma coupling), we provide a theoretical explanation of the observed Gamma-IBC phenomenon in the EEG-hyperscanning literature.

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Midfrontal theta tACS facilitates motor coordination in dyadic human-avatar motor interactions

Synchronous interpersonal motor interactions require moment-to-moment predictions and proactive monitoring of the partners actions. Neurophysiologically, this is highlighted by an enhancement of midfrontal theta activity. In the present study we explored the causal role of midfrontal theta for interpersonal motor interactions employing transcranial alternating current stimulation (tACS). We implemented a realistic human-avatar interaction task in immersive virtual reality (IVR) where participants controlled a virtual arm and hand to press a button synchronously with a virtual partner. Participants completed the task while receiving theta (Experiment 1) or beta (control frequency, Experiment 2) EEG-informed tACS over the frontal midline, as well as sham stimulation as a control. Results showed that frontal theta tACS significantly improved behavioural performance (by reducing interpersonal asynchrony) and participants motor strategies (by increasing movement times and reducing reaction times), while beta tACS had no effect on these measures. These results suggest that theta tACS over frontal areas facilitates action monitoring and motor abilities supporting interpersonal interactions.

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The monitoring system is attuned to others' actions during dyadic motor interactions

Successful interpersonal motor interactions necessitate the simultaneous monitoring of our own and our partners actions. To characterize the dynamics of the action monitoring system for tracking self and other behaviors during dyadic synchronous interactions, we combined EEG recordings and immersive Virtual Reality in two tasks where participants were asked to coordinate their actions with those of a Virtual Partner (VP). The two tasks differed in the features to be monitored: the Goal task required participants to predict and monitor the VPs reaching goal; the Spatial task required participants to predict and monitor the VPs reaching trajectory. In both tasks, the VP performed unexpected movement corrections to which the participant needed to adapt. By comparing the neural activity locked to the detection of unexpected changes in the VP action (other-monitoring) or to the participants action-replanning (self-monitoring), we show that during interpersonal interactions the monitoring system is more attuned to others than to ones own actions. Additionally, distinctive neural responses to VPs unexpected goals and trajectory corrections were found: goal corrections were reflected both in early fronto-central and later posterior neural responses while trajectory deviations from the expected movement were reflected only in later and posterior responses. Since these responses were locked to the partners behavior and not to ones own, our results indicate that during interpersonal interactions the action monitoring system is dedicated to evaluating the partners movements. Hence, our results reveal an eminently social role of the monitoring system during motor interactions. Significance StatementNon-verbal synchronous interpersonal interactions require the monitoring of both our actions and those of our partners. Understanding the neural underpinnings of this ability with a focus on the dynamics between self- and other-monitoring is fundamental to the comprehension of social coordination. By combining EEG and immersive Virtual Reality we demonstrate that the monitoring system is more attuned to others actions than to our own. In two tasks, we show that the neural activity associated with unexpected corrections in the goal or the trajectory of an action are locked to the partners actions rather than to the participants subsequent adaptation. This pattern of results highlights a social mode adopted by the monitoring system to handle motor interactions.

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The Human Dynamic Clamp reveals the fronto-parietal network linking real-time social coordination and cognition

How does the brain allow us to interact with others, and above all how does it handle situations when the goals of the interactors overlap (i.e. cooperation) or differ (i.e. competition)? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the Human Dynamic Clamp (HDC), a novel paradigm for studying realistic social behavior between self and other in well-controlled laboratory conditions, with high resolution electroencephalography (EEG). The collection of humanness and intention attribution reports, kinematics and neural data affords an opportunity to relate brain activity to the behavior of the HDC as well as to what the human is doing. Behavioral results demonstrate that sensorimotor coordination influences judgements of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to integration of visuo-motor information from self and other. The two networks overlap over the right parietal region, an area known to be important for interpersonal motor interactions. Furthermore, connectivity analysis highlights how the judgement of humanness and cooperation of others modulate the connection between the right parietal hub and prefrontal cortex. These results reveal how distributed neural dynamics integrates information from low-level sensorimotor mechanisms and high-level social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.\n\nSignificance StatementDaily social interactions require us to coordinate with others and to reflect on their potential motives. This study investigates the brain and behavioral dynamics of these two key aspects of social cognition. Combining high-density electroencephalography and the Human Dynamic Clamp (a Virtual Partner endowed with human-based coordination dynamics), we show first, that several features of sensorimotor coordination influence attribution of intention and judgement of humanness; second, that the right parietal lobe is a key integration hub between information related to self- and other-behavior; and third, that the posterior online social hub is functionally coupled to anterior offline brain structures to support mentalizing about others. Our results stress the complementary nature of low-level and high-level mechanisms that underlie social cognition.

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Frontal and occipito-temporal Theta activity as marker of error monitoring in Human-Avatar joint performance.

Discrepancies between sensory predictions and action outcome are at the base of error coding. However, these phenomena have mainly been studied focusing on individual performance. Here, we explored prediction errors during a human-avatar motor interaction and focused on both the classical frontal error-related brain responses and the activity of the action observation network. Our motor interaction paradigm required healthy individuals to synchronize their reach-to-grasp movements with those of a virtual partner in conditions that did (Interactive) or did not require (Cued) movement prediction and adaptation to the partners actions. Crucially, in 30% of the trials the virtual partner suddenly and unpredictably changed its movement trajectory thereby violating the human participants expectation. These changes elicited error-related neuromarkers (ERN/Pe - Theta/Alpha modulations) over fronto-central electrodes mainly during the Interactive condition. Source localization and connectivity analyses showed that the frontal Theta activity induced by violations of the expected interactive movements was in phase with occipito-temporal Theta activity. These results expand current knowledge about the neural correlates of on-line motor interactions linking the frontal error-monitoring system to visual, body motion-related, responses.

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