Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical...

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( $$ ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.