Predicting individual decision-making responses based on the functional connectivity of resting-state EEG

Objective. Despite increasing evidence revealing the relationship between task-related brain activity and decision-making, the association between resting-state functional connectivity and decision-making remains unknown. Approach. In this study, we investigated the potential relationship between the network revealed in the resting-state electroencephalogram (EEG) and decision responses and further predicted individuals' acceptance rates during the ultimatum game (UG) based on the functional connectivity revealed in the resting-state EEG. Main results. The results of this study demonstrated a significant relationship between the resting-state frontal-occipital connectivity and the UG acceptance rate in the alpha band. Increased acceptance rates were accompanied by a larger clustering coefficient and global and local efficiency as well as a shorter characteristic path length. Compared to the low-acceptance group, the high-acceptance group exhibited stronger frontal-occipital linkages. Finally, a multiple linear regression model based on the resting-state EEG network properties was adopted to predict the acceptance rates when subjects made their decision in the UG task. Significance. Together, the findings of this study may deepen our knowledge of decision-making and provide a potential physiological biomarker to predict the decision-making responses of subjects.