Introduction: 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 which can be studied 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. Objective: In this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits. Method: Using the EEG/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. Results: 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. Conclusion: These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.
This study aims to investigate the effects of individual differences in trait coping on brain networks at rest using electroencephalography (EEG) data. EEG recordings were processed using graph theory analysis. Active and passive coping styles were determined according to the factor structure of the Brief Coping Orientation to Problems Experienced questionnaire. A structural equation modeling analysis indicated that the influence of coping strategies on quality of life varies in strength and direction. In particular, active coping strategies were positively correlated with the psychological dimension. Graph measures, at both global and nodal levels, were used to identify the brain network properties in accordance with passive versus active coping styles. Preliminary evidence showed that both the global and nodal graph metrics were affected by the coping strategy in the delta band. During resting state, passive coping strategy participants had network topology characterized by a high global efficiency, indicating an important level of integration between distant brain areas and a high local efficiency and transitivity, suggesting a high local communication between adjacent regions. Various regions, such as the paracentral lobule, posterior cingulate, and other frontal or parietal areas, seemed to play a key role, suggesting that processes such as emotional load are highly solicited in passive coping individuals. In active coping participants, the superior temporal gyrus seemed to be of importance when neurons oscillated in the theta and alpha frequencies.
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 which can be studied using functional connectivity techniques. However, much of the current understanding of personalityrelated differences in functional connectivity has been obtained through the stationary analysis, which do not capture the complex dynamical properties of brain networks. In this study, we aimed to evaluate the feasibility of dynamic network measures to predict personality traits. Using the EEGsource connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from EEG data acquired from 45 subjects during resting state. Then, several dynamic functional connectivity metrics were evaluated. Results showed a negative correlation between neuroticism and the dynamic variability of temporal lobe regions in terms of strength. In addition, a positive correlation was found between agreeableness and centrality variability of the posterior cingulate, which is known as a key hub in resting state networks. Results also revealed that extraversion is positively correlated with the dynamics of the superior parietal region. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.
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