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Psychological resilience correlates with EEG source-space brain network flexibility
Author(s) -
Véronique Paban,
Julien Modolo,
Ahmad Mheich,
Mahmoud Hassan
Publication year - 2019
Publication title -
network neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.128
H-Index - 18
ISSN - 2472-1751
DOI - 10.1162/netn_a_00079
Subject(s) - electroencephalography , personality , flexibility (engineering) , resilience (materials science) , psychology , psychological resilience , resting state fmri , metric (unit) , big five personality traits , computer science , artificial intelligence , cognitive psychology , mathematics , social psychology , neuroscience , statistics , physics , operations management , economics , thermodynamics
We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

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