Burnout and depression in college students.

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

Processing Self-Related Information Under Non-attentional Conditions Revealed by Visual MMN.

Mismatch negativity (MMN) of event-related potentials (ERPs) is a biomarker reflecting the preattentional change detection under non-attentional conditions. This study was performed to explore whether high self-related information could elicit MMN in the visual channel, indicating the automatic processing of self-related information at the preattentional stage. Thirty-five participants were recruited and asked to list 25 city names including the birthplace. According to the difference of relevance reported from the participants, we divided names of the different cities into high (birthplace as deviants), medium (Xi'an, where participants' university is located, as deviants), and low (totally unrelated cities as standard stimuli) self-related information. Visual MMN (vMMN) was elicited by high self-related information but not by medium self-related information, with an occipital-temporal scalp distribution, indicating that, under non-attentional condition, high self-related information can be effectively processed automatically in the preattentional stage compared with low self-related information. These data provided new electrophysiological evidence for self-related information processing.