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静息态 BOLD 时间变异性在感觉运动和突显网络中,是特质情绪智力的基础,并解释了情绪调节策略的差异。

Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait emotional intelligence and explains differences in emotion regulation strategies.

机构信息

Clinical and Affective Neuroscience Lab - Cli.A.N Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy.

Centre for Medical Sciences, CISMed, University of Trento, Trento, Italy.

出版信息

Sci Rep. 2022 Sep 7;12(1):15163. doi: 10.1038/s41598-022-19477-x.

Abstract

A converging body of behavioural findings supports the hypothesis that the dispositional use of emotion regulation (ER) strategies depends on trait emotional intelligence (trait EI) levels. Unfortunately, neuroscientific investigations of such relationship are missing. To fill this gap, we analysed trait measures and resting state data from 79 healthy participants to investigate whether trait EI and ER processes are associated to similar neural circuits. An unsupervised machine learning approach (independent component analysis) was used to decompose resting-sate functional networks and to assess whether they predict trait EI and specific ER strategies. Individual differences results showed that high trait EI significantly predicts and negatively correlates with the frequency of use of typical dysfunctional ER strategies. Crucially, we observed that an increased BOLD temporal variability within sensorimotor and salience networks was associated with both high trait EI and the frequency of use of cognitive reappraisal. By contrast, a decreased variability in salience network was associated with the use of suppression. These findings support the tight connection between trait EI and individual tendency to use functional ER strategies, and provide the first evidence that modulations of BOLD temporal variability in specific brain networks may be pivotal in explaining this relationship.

摘要

越来越多的行为研究结果支持这样一种假设,即特质情绪智力(trait EI)水平决定了个体习惯性地使用情绪调节(ER)策略。然而,目前关于这种关系的神经科学研究还很缺乏。为了填补这一空白,我们分析了 79 名健康参与者的特质测量和静息态数据,以探究特质 EI 和 ER 过程是否与相似的神经回路相关。我们使用无监督机器学习方法(独立成分分析)来分解静息态功能网络,并评估它们是否可以预测特质 EI 和特定的 ER 策略。个体差异的结果表明,高特质 EI 显著预测并负相关于典型的功能失调性 ER 策略的使用频率。至关重要的是,我们观察到,感觉运动网络和突显网络内的 BOLD 时间变异性增加与高特质 EI 和认知重评的使用频率有关。相比之下,突显网络的变异性降低与抑制的使用有关。这些发现支持了特质 EI 与个体使用功能性 ER 策略的倾向之间的紧密联系,并首次提供了证据,表明特定脑网络中 BOLD 时间变异性的调节可能是解释这种关系的关键。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7779/9452559/c25cd28cbca6/41598_2022_19477_Fig1_HTML.jpg

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