ArticleScientific reports2022
Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait emotional intelligence and explains differences in emotion regulation strategies.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Neural fingerprint of the dark triad: Resting state BOLD power (fALFF) alterations in executive and default mode networks.Cognitive, affective & behavioral neuroscience · 2026Article
- Emotional Dysregulation and Temperament in Adolescents with Acute Psychiatric Conditions: Gender Differences and the Role of Psychiatric Diagnosis.Journal of clinical medicine · 2026Article
- From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.Brain sciences · 2025Review
- Resting-state BOLD temporal variability of the default mode network predicts spontaneous mind wandering, which is negatively associated with mindfulness skills.Frontiers in human neuroscience · 2025Article
- Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis.Frontiers in human neuroscience · 2025Article
- The sustained effect of 5-week EmotionCore mindfulness training on emotion regulation and emotional intelligence: heterogeneous benefits for depression and anxiety across subgroups.Frontiers in psychiatry · 2025Article
- Decoding acceptance and reappraisal strategies from resting state macro networks.Scientific reports · 2024Article
- Resting-state functional connectivity and structural differences between smokers and healthy non-smokers.Scientific reports · 2024Article
- Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach.Social cognitive and affective neuroscience · 2024Article
- Neural variability in three major psychiatric disorders.Molecular psychiatry · 2023Article
- The connectome-based prediction of trust propensity in older adults: A resting-state functional magnetic resonance imaging study.Human brain mapping · 2023Article
- Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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