Biophysical mechanisms to relate large-scale brain network dynamics to individual-specific variability of perception

Abstract

Perception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants McGurk susceptibility was negatively correlated to their alpha-band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses - representing the sensory specific auditory/visual areas and modality non-specific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling result in a decrease in alpha band global coherence - mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception & action task paradigms.