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Gravel, N. G.

Publications and source records attributed to Gravel, N. G..

2 recordsLinked to original sources

Interpersonal alignment in infra-slow EEG rhythms anticipates mutual recognition

Across multiple timescales, neural rhythms play a crucial role in coordinating cognitive and physiological processes, with slower frequencies potentially relevant during social interaction. While electroencephalography (EEG) has been widely used to study such processes, examining these slower aspects during social interaction presents unique methodological challenges. Here we address this issue by investigating infra-slow EEG, respiratory, and electrodermal signals recorded simultaneously during a perceptual crossing experiment (PCE)--a recently established paradigm in social cognition and EEG hyperscanning. Through innovative spectral-characterization methods and a novel dyadic regression approach, we establish a methodological framework for examining infra-slow dynamics that coordinate brain and body during social interaction. Our analysis revealed that, when time-locked to task responses, the spectral power of infra-slow oscillations (ISOs) at 0.05 Hz and 0.1 Hz showed enhanced inter-participant similarity in dyads achieving mutual recognition relative to those that did not. Although not strictly simultaneous, this inter-personal alignment in the spectral power of ISOs was accompanied by concurrent dynamics both in respiratory pressure and electrodermal activity, suggesting that ISOs reflect the integration of autonomic, cognitive, and social processes, with their alignment facilitating shared understanding and joint action. Crucially, we find that task-responses in participants who successfully engaged in the PCE task were coupled to the phase of the 0.1 Hz rhythm. Additionally, we show that ISOs and aperiodic activity tracked behaviorally relevant transitions, notably prior to and during the perceptual crossing task. Taken together, our findings highlight an important role for ISOs in mediating the temporal dynamics of social cognition and show that inter-participant alignment in ISOs and physiological signals can serve as a proxy for the complex interplay between body physiology, cognition, and social behavior.

neuroscience↗

Assessing uncertainty in connective field estimations from resting state fMRI activity

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants. Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies. HighlightsO_LILocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling. C_LIO_LIBayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations. C_LIO_LI3T observations were qualitatively similar to those previously reported at 7T. C_LI

neuroscience↗