心理韧性与脑电图源空间脑网络灵活性相关。
Psychological resilience correlates with EEG source-space brain network flexibility.
作者信息
Paban Véronique, Modolo Julien, Mheich Ahmad, Hassan Mahmoud
机构信息
Aix Marseille University, CNRS, LNSC, Marseille, France.
University of Rennes, INSERM, LTSI-U1099, F-35000 Rennes, France.
出版信息
Netw Neurosci. 2019 Mar 1;3(2):539-550. doi: 10.1162/netn_a_00079. eCollection 2019.
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.
我们旨在确定人类静息功能网络的动力学特性与人格最显著特征之一(即心理韧性)之间的潜在关系。为解决这个问题,我们使用了从45名健康受试者记录的静息态脑电图(EEG)数据。心理韧性使用10项Connor-Davidson心理韧性量表(CD-RISC)进行量化。通过使用滑动窗口方法,采用EEG源空间连接方法构建每个EEG频段(δ波、θ波、α波和β波)的脑网络。使用网络灵活性评估脑网络动力学,网络灵活性与给定节点随时间改变其模块归属的趋势相关。结果显示,在δ波、α波和β波频段内的有限数量脑区中,心理韧性与脑网络灵活性呈负相关。这项研究提供了证据,表明作为动态功能网络指标的网络灵活性与通过人格测试评估的心理韧性密切相关。除了这一原理证明,即可以识别出代表人格特质的基于EEG的可靠量值之外,这还促使人们进一步研究人格的全谱及其与功能网络的关系。