Visuo-haptic interactions are gaining interest in perception research because of their relevance to robotics and rehabilitation. While the benefits of integrating concurrent visuo-tactile cues are well-documented, the temporal dynamics of competitive interactions between vision and touch remain less understood. Building on extant empirical evidence of unilateral intersensory switching, we ask whether bilateral detection of visual and tactile stimuli can be explained by cross-sensory competition and explore the underlying sensorineural dynamics via a multi-level neural network model. Tested against results from a speeded reaction time task to unilateral and bilateral visual, tactile, and visuo-tactile targets, the network successfully predicts higher switch costs for sequential identification of unisensory stimuli of different modalities on the same hand rather than across hands. Our network also captures significant effects of inter-stimulus interval, trial type (i.e. repeat vs. switch), and modality type (i.e. visual, tactile, or visuo-tactile). Additionally, a sensitivity analysis on specific network connections differentiated between potential mechanisms underlying unisensory and multisensory processing of bilateral targets. Both network predictions and behavioral data reveal a slowdown in response to the repeated presentation of simultaneous visuo-tactile targets, suggesting that cross-sensory inhibition may contribute to offline influences of vision on bilateral tactile perception.

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions.

In science and engineering mathematical modeling serves as a tool for the understanding of processes and systems and as a testing bed for several hypotheses, e.g., concerning the testing (prediction) of functional limits by simulations. A brief overview of current modeling strategies in brain research is given, spatial scales ranging from single neuron to large scale activity of and between brain regions are considered. The models are mainly time-invariant. Three time-variant modeling strategies, which enable a model-based signal analysis, are described and applied to large scale signals. The first is derived from adaptive filter theory and covers linear system and linear as well as nonlinear process models. The second is based on modeled brain source signals, i.e., the inverse problem must be solved. The third strategy consists of a generalization of Dynamic Causal Modeling (DCM); DCM is frequently used for analysis of directed interactions between brain structures. Examples are derived from neonatal electroencephalography (EEG) monitoring of preterm and fullterm newborns. A further example is based on high-density recordings of event-related potentials (ERPs) and shows the combination of a time-variant ERP-based source model, as a part of a realistic head model, with a multivariate process model to analyze the time evolution of interactions between source processes before and during the execution of a complex motoric task. In two other examples hemodynamic signals (functional magnetic resonance imaging-fMRI) are utilized for analysis of interactions between brain regions, where nonlinear, multivariate models are used.