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Brain Network Dynamics Correlate with Personality Traits.
Brain Connectivity ( IF 2.4 ) Pub Date : 2020-04-08 , DOI: 10.1089/brain.2019.0723
Aya Kabbara 1 , Veronique Paban 2 , Arnaud Weill 2 , Julien Modolo 1 , Mahmoud Hassan 1
Affiliation  

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.

中文翻译:

脑网络动力学与人格特质相关。

识别人格特质的神经底物是一个非常有趣的话题。另一方面,现在确定大脑是一个动态的网络系统,可以使用功能连接技术进行研究。但是,目前对功能连接性中与人格相关的差异的大多数理解是通过静态分析获得的,该分析无法捕获大脑网络的复杂动力学特性。在这项研究中,我们旨在评估使用动态网络测度预测人格特质的可行性。使用脑电图(EEG)/磁脑图(MEG)源连接方法与滑动窗口方法相结合,从两个数据集中重建了动态功能性大脑网络:(1)从56名受试者获得的静息状态脑电数据;(2)由人类Connectome项目提供的静止状态MEG数据。然后,评估了几个动态功能连接性指标。根据神经质,两种方式(EEG和MEG)获得了类似的观察结果,与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。(2)由人类Connectome项目提供的静止状态MEG数据。然后,评估了几个动态功能连接性指标。根据神经质,两种方式(EEG和MEG)获得了相似的观察结果,与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。(2)由人类Connectome项目提供的静止状态MEG数据。然后,评估了几个动态功能连接性指标。根据神经质,两种方式(EEG和MEG)获得了类似的观察结果,与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。评估了几个动态功能连接性指标。根据神经质,两种方式(EEG和MEG)获得了相似的观察结果,与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。评估了几个动态功能连接性指标。根据神经质,两种方式(EEG和MEG)获得了类似的观察结果,与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。与静息状态脑网络的动态变化呈负相关。特别是,观察到这种人格特质与颞叶区域动态变化之间的显着关系。结果还表明,外向性和开放性与大脑网络的动力学呈正相关。这些发现突出了追踪功能性大脑网络动态的重要性,以增进我们对人格神经底物的理解。
更新日期:2020-04-08
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