Exploring attentional modulation of SSVEPs via large-scale brain dynamics modeling

Steady-state visual evoked potentials (SSVEPs) are brain nonlinear responses evoked by repetitive visual stimuli with specific frequencies. In addition to the frequency of visual stimuli, visual attention can influence SSVEPs. For instance, an attended stimulus was found to evoke enhanced SSVEPs. However, because of significant challenges associated with quantifying attention levels during SSVEP experiments, the mechanism underlying the attentional modulation of SSVEPs has not been fully established. In the present study, we addressed this issue via large-scale brain dynamics modeling. Consistent with previous experimental observations, our model successfully reproduced the phenomenon of the attentional modulation of SSVEPs, and showed that both the power and the signal-noise ratio of SSVEPs were positively related to attention levels. We also found that when attention levels were high, the brain network displayed higher local and more global efficiencies, and the inter-network connectivity among the visual network, the default mode network, and the dorsal attentional network increased. In addition, our model supported a negative relationship between the features of the intermodulation component of SSVEPs with respect to attention levels. Overall, these results represent a quantification of the effect of attention on SSVEPs, and highlight the potential of large-scale brain dynamics modeling in elucidating cognitive mechanisms.