Liu Jian-Ping, Gu Si-Yu, Song Chun-Mei, Yang Hu-Cheng, Shi Yang, Gu Yu-Fang, Wang Shu-Fang, Chen Ying-Zhu
School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
Department of Neurology, Yancheng Clinical Medical College of Yangzhou University, Yancheng Third People's Hospital, Yancheng, China.
Front Public Health. 2025 Jun 16;13:1595550. doi: 10.3389/fpubh.2025.1595550. eCollection 2025.
Occupational burnout is a significant problem among nurses, linked to negative outcomes. Understanding its neurobiological basis is crucial, yet remains limited.
Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 40 female nurses with occupational burnout and 40 healthy controls. Degree centrality (DC) was calculated to identify functional hubs, and subsequent functional connectivity (FC) analysis was performed. Group differences in DC and FC were statistically compared. Their correlations with Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scores were assessed, and a classification model was built using DC and FC features to distinguish between burnout and control groups.
The burnout group showed significantly decreased DC in bilateral precuneus and reduced FC between left precuneus and right medial orbitofrontal cortex (mOFC) compared to the healthy control group. These neuroimaging markers correlated with clinical burnout dimensions: precuneus DC negatively associated with emotional exhaustion and depersonalization, while precuneus-mOFC connectivity positively correlated with personal accomplishment. A linear discriminant analysis model combining DC and FC measures achieved 85% classification accuracy (sensitivity 80%, specificity 90%) in distinguishing burnout from controls.
These findings identify the precuneus and its mOFC connectivity as key neural substrates of occupational burnout, suggesting disrupted integration between self-referential processing and reward/emotion regulation systems. Our results advance understanding of burnout's neurobiological mechanisms and demonstrate the potential of neuroimaging markers for objective burnout assessment.
职业倦怠是护士群体中一个严重的问题,与负面后果相关。了解其神经生物学基础至关重要,但目前仍很有限。
对40名患有职业倦怠的女性护士和40名健康对照者进行静息态功能磁共振成像(rs-fMRI)数据采集。计算度中心性(DC)以识别功能枢纽,并随后进行功能连接(FC)分析。对DC和FC的组间差异进行统计学比较。评估它们与马氏职业倦怠量表-人类服务调查(MBI-HSS)得分的相关性,并使用DC和FC特征建立一个分类模型,以区分倦怠组和对照组。
与健康对照组相比,倦怠组双侧楔前叶的DC显著降低,左侧楔前叶与右侧内侧眶额皮质(mOFC)之间的FC减少。这些神经影像学标志物与临床倦怠维度相关:楔前叶DC与情感耗竭和去个性化呈负相关,而楔前叶-mOFC连接与个人成就感呈正相关。结合DC和FC测量的线性判别分析模型在区分倦怠组和对照组方面达到了85%的分类准确率(敏感性80%,特异性90%)。
这些发现确定了楔前叶及其与mOFC的连接是职业倦怠的关键神经基质,表明自我参照加工与奖励/情绪调节系统之间的整合受到破坏。我们的结果推进了对倦怠神经生物学机制的理解,并证明了神经影像学标志物在客观评估倦怠方面的潜力。