Neurophysiological Markers of Emotion Processing in Burnout Syndrome

Abstract

The substantial body of research employing subjective measures indicates that burnout syndrome is associated with cognitive and emotional dysfunctions. The growing amount of neurophysiological and neuroimaging research helps in broadening existing knowledge of the neural mechanisms underlying core burnout components (emotional exhaustion and depersonalization/cynicism) that are inextricably associated with emotional processing. In the presented EEG study, a group of 93 participants (55 women; mean age = 35.8) were selected for the burnout group or the demographically matched control group on the basis of the results of the Maslach Burnout Inventory – General Survey (MBI-GS) and the Areas of Worklife Survey (AWS). Subjects then participated in an EEG experiment using two experimental procedures: a facial recognition task and viewing of passive pictures. The study focuses on analyzing event-related potentials (ERPs): N170, VPP, EPN, and LPP, as indicators of emotional information processing. Our results show that burnout subjects, as compared to the control group, demonstrate significantly weaker response to affect-evoking stimuli, indexed by a decline in VPP amplitude to emotional faces and decreased EPN amplitude in processing emotional scenes. The analysis of N170 and LPP showed no significant between-group difference. The correlation analyses revealed that VPP and EPN, which are ERP components related to emotional processing, are associated with two core burnout symptoms: emotional exhaustion and cynicism. To our knowledge, we are one of the first research groups to use ERPs to demonstrate such a relationship between neurophysiological activity and burnout syndrome in the context of emotional processing. Thus, in conclusion we emphasized that the decreased amplitude of VPP and EPN components in the burnout group may be a neurophysiological manifestation of emotional blunting and may be considered as neurophysiological markers of emotional exhaustion and cynicism. Additionally, we did not observe a decrease in LPP, which may be considered as a marker that significantly differentiates burnout from depression.

Occupational burnout and its overlapping effect with depression and anxiety

“…Specifically, 2 components of event-related potential (ERP) in error-monitoring revealed similarity to ERPs that were observed in anxiety and depressive disorders. However, in emotional information processing, the results were not unequivocal: the ERP patterns in the burnout sample only to some extent resembled the electrophysiological changes that were observed in depression [34].…”

“…It should be noted that since the models are not based on an experimental design, they do not represent "effects while controlling for covariates" (i.e., held constant due to design), but rather "associations adjusted for covariates" (i.e., conditional on values of a specific set of covariates). This distinction is crucial in understanding the paradoxical reversal of coefficient signs often seen when including additional covariates in regression models [50,51]. This reversal is caused by explaining the variance of the dependent variable by 2 or more sources of variance which are also correlated, thus making the explained variance conditional on the size of correlation between the predictors, similarly to partial correlations.…”

New Protocol for Quantitative Analysis of Brain Cortex Electroencephalographic Activity in Patients With Psychiatric Disorders

“…Our investigations were aimed at finding whether there are some biomarkers in the electrical cortical activity of the brain and whether they are characteristic of particular disorders as in some attempts made by John in late eighties (John et al, 1988 ). Some neurophysiological markers were found for example in research on burn-out syndrome (Golonka et al, 2017 ) which is also in the area of our interests (Chow et al, 2018 ).…”

Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders

“…Electrophysiological methods have developed in recent decades (Kamarajan and Porjesz, 2015 ; Martínez-Rodrigo et al, 2017 ). Recently there has been a rapid advance in therapeutic use of Brain-Computer Interfaces (BCI) in which the acquisition of electrical activity of selected areas of brain cortex plays the main role (Mikołajewska and Mikołajewski, 2012 , 2013 , 2014 ; Teruel et al, 2017 ) and Event-Related Potentials (ERP) and other evoked potentials can lead not only to explanation of psychological behaviors in particular situations (Kotyra and Wojcik, 2017a , b ) but also to finding some biomarkers characteristic of psychiatric disorders (Chapman and Bragdon, 1964 ; Sutton et al, 1965 ; Campanella, 2013 ; Golonka et al, 2017 ). Together with the development of neurocomputing, neuroinformatics and artificial intelligence a lot of new tools and possibilities appeared and made their use possible for a wide range of classification tasks in biomedical engineering (Ogiela et al, 2008 ; Szaleniec et al, 2008 , 2013 ) or brain functions simulations which are also a subject of our investigations (Ważny and Wojcik, 2014 ; Wojcik and Ważny, 2015 ).…”