Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level


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Aydın S.

COGNITIVE NEURODYNAMICS, vol.17, no.2, pp.331-344, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.1007/s11571-022-09843-w
  • Journal Name: COGNITIVE NEURODYNAMICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.331-344
  • Keywords: EEG, Emotion regulation, Brain default mode network, Graph theory, Deep learning, FUNCTIONAL CONNECTIVITY, EXPRESSIVE SUPPRESSION, EIGENMODES, PARAMETERS, ALGORITHM, SCIENCE
  • Hacettepe University Affiliated: Yes

Abstract

https://link.springer.com/article/10.1007/s11571-022-09843-w

ABSTRACT:

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 (0.5 - 45 Hz) 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.

Discussion and conclusion

In the present study, healthy adults having different cognitive abilities in management of negative emotions in daily life were identified by resting-state Graph Theoretic network measures in both EO and EC states. The individuals were grouped according to their use of positive or negative cognitive/behavioral ERS. For each group, connectivity matrices were estimated by examining DTF based on Granger causality insensitive to volume conduction. BCT was used to compute the network measures from adjacency matrices, i.e. binary transformation of connectivity matrices according to non-overlapped short EEG segments across 61-channel recordings (VEOG recordings were not included in connectivity estimations). The groups were firstly classified by using LSTMNs driven by six different network measures together (CCLEGETQr) with respect to both states (EO, EC) and frequency band intervals (full-band:0.5−40.5 Hz0.5−40.5 Hz, delta:0.5−4 Hz0.5−4 Hz, theta:4.5−8 Hz4.5−8 Hz, alpha:8.5−12.5 Hz8.5−12.5 Hz, beta:13−30Hz13−30Hz, gamma:30.5−40.5 Hz30.5−40.5 Hz). In comparing both cognitive and behavioral opposing ERS, the highest classification performance was provided by full-band specific measures in EC state that refer the default mode network (DMN) of the brain. Eyes-opening can induce significant neural activities due to many external stimuli (Gorantla 2020). Therefore, eyes-closed resting-state can be conducive to understanding the dynamic characteristics of the brain (Liu and Wu 2020). The current results are compatible with these DMN research.

Regarding EC state, the main full-band specific findings are discussed in following items:

  • Frequent use of rumination is found to be characterized by high modularity due to maladaptive and repetitive negative thoughts that trigger re-experiencing negative emotions. In recent neuro-imaging studies reveal that depression causes the increase in network modularity in resting-state (, Li, BJ, Friston, K, 2018). It is well known that ruminative thoughts that result in failure to manage negative emotions lead to depression.Therefore, the present electrophysiological findings are clearly consistent with neuro-imaging results.
  • The frequent use expressive suppression is found to be characterized by high network resilience. Conceptually, functional network resilience has been linked with cognitive skills in both healthy (Stern 2018), and neuro-degenerative disorders (Cabeza 2018). Therefore, neuro-imaging discussions about network resilience supports the present results including the lower resilience originated from rumination and the higher resilience originated from behavioral ERs.
  • Behavioral ERs provide the increase in network integration in comparison to cognitive ERs.
  • The large number of outliers were commonly observed in LE estimations in each group. These outliers might be originated from age differences among the individuals with varying ages lied between 20 and 65 because of the fact that LE was reported as incremental until adulthood in healthy subjects and then dropped with aging, while GE was found to be almost unchanged over the lifetime (Gao and Gilmore 2011).

In conclusion, Graph Theoretical global connectivity measures are found to be useful in discriminating opposing ERS in resting-state. In other words, the scores of the psychological metrics can be correlated with full-band network measures by means of segregation, integration and modularity of the brain. In particular, both segregation and integration are found to be highly sensitive to not only frequency band interval but also cognitive abilities, while the resilience represented as network assortativity is found to be almost insensitive to frequency interval. Since the brain is composed of spatially embedded complex sub-networks, there must be a balance between integration and segregation of neural information flow result in cognition and behavior in healthy brains (Bullmore and Sporns 2009). The later studies show that the number and strength of neural connections can change with aging, but the optimal balance occurred between neuronal wiring costs and communication efficiency (Bullmore and Sporns 2012; Cao 2014). Thus, the current overall findings can be concluded that cortico-functional balance is impaired by the presence of ruminative and negative thoughts. The present new findings are also compatible with the more recent neuro-imaging studies including structural connectivity analysis based on fMRI (Wang et al. 2021).