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Identifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system that can be studied by using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks. In this study, we aimed at evaluating the feasibility of using dynamic network measures to predict personality traits. Using the electro-encephalography (EEG)/magneto-encephalography (MEG) source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: (1) resting-state EEG data acquired from 56 subjects; (2) resting-state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated. Similar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting-state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results.