From Neuroimaging to Computational Modeling of Burnout: The Traditional versus the Fuzzy Approach—A Review

Abstract

Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that have used medical neuroimaging techniques to provide evidence or links regarding changes in brain function in occupational burnout syndrome from a neuroscientific perspective, and then use these to propose a fuzzy-based computational model of burnout.A comprehensive literature search was conducted in two major databases (PubMed and Medline Complete). The search period was 2006–2021, and searches were limited to the English language. Each article was carefully reviewed and appropriately selected on the basis of raw data, validity of methods used, clarity of results, and scales for measuring burnout. The results showed that the brain structures of patients with job burnout that are associated with emotion, motivation, and empathy weresignificantly different from healthy controls. These altered brain regions included the thalamus, hippocampus, amygdala, caudate, striatum, dorso-lateral prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, anterior insula, inferior frontal cingulate cortex, middle frontal cingulate cortex, temporoparietal junction, and grey matter. Deepening our understanding of how these brain structures are related to burnout will pave the way for better approaches fordiagnosis and intervention. As an alternative to the neuroimaging approach, the paper presents a late proposal of the PLUS (personal living usual satisfaction) parameter. It is based on a fuzzy model, wherein the data source is psychological factors—the same or similar to the neuroimaging approach. As the novel approach to searching for neural burnout mechanisms, we have shown that computational models, including those based on fuzzy logic and artificial neural networks, can play an important role in inferring and predicting burnout. Effective computational models of burnout are possible but need further development to ensure accuracy across different populations. There is also a need to identify mechanisms and clinical indicators of chronic fatigue syndrome, stress, burnout, and natural cognitive changes associated with, for example, ageing, in order to introduce more effective differential diagnosis and screening.