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.

Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait Emotional Intelligence and explains differences in Emotion regulation strategies / Zanella, Federico; Monachesi, Bianca; Grecucci, Alessandro. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - ELETTRONICO. - 2022, 12:(2022), pp. 1516301-1516312. [10.1038/s41598-022-19477-x]

Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait Emotional Intelligence and explains differences in Emotion regulation strategies

Bianca Monachesi;Alessandro Grecucci
2022-01-01

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.
2022
Zanella, Federico; Monachesi, Bianca; Grecucci, Alessandro
Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait Emotional Intelligence and explains differences in Emotion regulation strategies / Zanella, Federico; Monachesi, Bianca; Grecucci, Alessandro. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - ELETTRONICO. - 2022, 12:(2022), pp. 1516301-1516312. [10.1038/s41598-022-19477-x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/352882
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