Abstract
Although mitigating burnout has long been a pressing issue in healthcare, recent global disasters, including the COVID-19 pandemic and wars, have exacerbated this problem. Medical professionals are frequently exposed to diverse job-induced distress; furthermore, the importance of people’s sense of coherence (SOC) over work has been addressed to better deal with burnout. However, the neural mechanisms underlying SOC in medical professionals are not sufficiently investigated. In this study, the intrinsic fractional amplitude of low-frequency fluctuations (fALFF) were measured as an indicator of regional brain spontaneous activity using resting-state functional magnetic resonance imaging in registered nurses. The associations between participants’ SOC levels and the fALFF values within brain regions were subsequently explored. The SOC scale scores were positively correlated with fALFF values in the right superior frontal gyrus (SFG) and the left inferior parietal lobule. Furthermore, the SOC levels of the participants mediated the link between their fALFF values in the right SFG and the depersonalization dimension of burnout. The results deepened the understanding of the counter role of SOC on burnout in medical professionals and may provide practical insights for developing efficient interventions.
Introduction
Burnout has become a critical issue in the healthcare system [1–4]. A large proportion of medical professionals show burnout symptoms that may lead to substance abuse, medical errors, and even suicide [5]. They are constantly exposed to various stressors, including patients’ suffering, steep hierarchies, as well as team conflicts [5, 6]. Since the COVID-19 pandemic, medical professionals have experienced higher levels of stress [7–9].
Meanwhile, recent studies emphasized the crucial role of a sense of coherence (SOC) over work in dealing with burnout [3, 10, 11]. An SOC refers to the ability to consider stressful situations as manageable, understandable, and meaningful, which may be nurtured by proper training and learning experiences [5, 12]. The SOC that one can exert over these stressors may have a substantial influence, not only on burnout but also on work performance, especially for less-experienced medical professionals [3, 13–15]. In this line, we also reported that decreased SOC levels were associated with increased depersonalization symptoms of burnout as well as enhanced feelings of empathic distress in registered nurses [12]. For a better understanding of this issue, studying the neural substrates of SOC in medical professionals should be informative.
Resting-state functional magnetic resonance imaging (RS-fMRI) can be a promising tool in investigating neural substrates of SOC. It is because peoples’ intrinsic brain activity may reflect their levels of learning/training and mental maturation, through self-referential mental activity [16–18]. RS-fMRI enables the assessment of stable neural characteristics by measuring spontaneous brain activity while avoiding potential confounders highlighted by task-induced fMRI [19, 20]. However, neural substrates for SOC in medical professionals are still unexplored in a resting state.
In this study, the fractional amplitude of low-frequency fluctuations (fALFF) was used to assess the intensity of spontaneous brain activity by examining the amplitude of hemodynamic oscillations [21, 22]. The fALFF approach has not only characterized psychological trait-level features but also prompted subsequent perceptual sensitivity and cognitive performance [23–25]. In particular, this approach has been utilized to investigate the neural mechanisms underlying the efforts undertaken to achieve long-term goals among people experiencing chronic stress in various professional fields [26, 27]. Furthermore, several previous studies evaluated the neurobiological bases of stress-induced clinical conditions, including depression and anxiety, using the fALFF approach [28–30]. Therefore, fALFF approach is a promising approach to investigate the neural substrates of SOC in medical professionals.
Previous studies have repeatedly shown that the frontoparietal regions are activated in various types of cognitively demanding tasks among medical professionals [31, 32]. Therefore, we predicted that the fALFF values in these brain areas would be associated with the SOC levels in registered nurses.
Materials and methods
Participants
Forty-one registered nurses were enrolled in this study. The sample size was determined based on previous fMRI studies on burnout [33, 34]. Four participants were excluded from the analysis because of excessive head motion during MRI scanning (see S1 File for details). Thus, the data of the remaining 37 participants were used in the analyses (Table 1). No participants met the criteria for any psychiatric disorder per the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I), and none had a history of head trauma, neurological illness, serious medical or surgical illness, or substance abuse. The recruitment of the participants and the original data analysis of this study were performed from 2012 to 2017. This study was approved by the Committee on Medical Ethics of Kyoto University and was conducted per the Code of Ethics of the World Medical Association. All participants gave their written informed consent to participate in the study. Further details are described in S1 File.
Table 1. Demographic characteristics of the participants.
Total (n = 37) | |
---|---|
Age (years): mean ± SD [min–max] | 27.0 ± 3.9 [22–39] |
Sex: male/female | 11/26 |
Handedness: right/left | 35/2 |
IQ: mean ± SD [min–max] | 97.6 ± 8.2 [81–110] |
Years of profession a: mean ± SD [min–max] | 5.1 ± 4.0 [1.0–17.5] |
a Data not available for one participant
Psychological measures
The SOC was estimated using the SOC scale [10, 12]. The SOC scale estimates the degree of participants’ belief that the resources are available to one to meet the demands posed by the stressor, the degree of tendency in challenges, worthy of investment and engagement, and the degree of impression of an environment as structured, predictable, and explicable [10]. A higher SOC scale score represents a greater sense of coherence.
The severity of the depersonalization dimension of burnout was assessed using the Japanese version of the Maslach Burnout Inventory [MBI, 35, 36], which measured participants’ intensity of emotional detachment toward the recipients of one’s care and defensive coping [35, 37], with higher scores denoting more depersonalization. In our previous study, we reported that the “emotional exhaustion” subscales were not correlated with SOC levels [12]. In addition, “personal accomplishment” subscales were reported to reflect the employees’ personalities rather than the burnout dimension [38–40]. Therefore, the emotional exhaustion and personal accomplishment subscales of the MBI were not used in the main analyses of this study.
MRI data acquisition, pre-processing and fALFF calculation
All participants underwent MRI scanning on a 3 T whole-body scanner coupled with an 8-channel phased-array head coil (Trio, Siemens, Erlangen, Germany). Image processing and fALFF calculation were performed using SPM (Wellcome Trust Center for Neuroimaging, London, UK) and DPARSFA toolbox (Data Processing Assistant for Resting-state fMRI Advanced Edition, http://rfmri.org/DPARSF) in MATLAB (MathWorks, Natick, MA, USA). Please see S1 File for details.
Data analyses
To explore the brain regions where the fALFF values were associated with the SOC scale scores, we performed multiple regression analyses throughout the whole brain using a general linear model framework in SPM [41]. Age and sex were entered into the model as covariates of no interest. Based on the previous studies [e.g., 23, 42], we reported clusters that survived the family-wise error (FWE) correction for multiple comparisons with a cluster-level p < 0.05 (at voxel-level, uncorrected p < 0.005). The VOI function in SPM was used to extract the parameter estimates from the significant clusters. We then performed correlation analyses between the fALFF values and the depersonalization scores. If significant correlations were observed, we conducted mediation analyses to determine whether SOC mediates the relationship using the INDIRECT macro for SPSS [43]. We tested the significance of the mediation effect via a bootstrapping strategy within this macro. The mediated effect is considered statistically significant, if a confidence interval does not contain zero. In the current study, the threshold for statistical significance was set at p < 0.05 (two-tailed).
Results
The participants’ SOC scale scores were 91–172 (mean ± SD = 126.2 ± 18.4). The depersonalization dimension of burnout scores were 6–20 (mean ± SD = 11.8 ± 3.5). The outcomes reflected considerable individual differences in these scores in our sample. The additional findings of emotional exhaustion and personal accomplishment subscales are detailed in S2 File, S1 Fig and S1 Table.
Fig 1 and S2 Table present the brain regions where fALFF values were correlated with SOC scale scores. The SOC scale scores were positively correlated with fALFF values in the right superior frontal gyrus (SFG) and the left inferior parietal lobule (IPL). In other words, participants with higher fALFF values in these areas showed higher SOC levels. We did not observe any significant areas where the fALFF values were negatively correlated with SOC scale scores.
Fig 1. Brain regions showing spontaneous brain activity levels associated with SOC.
A statistical threshold was set at cluster-level FWE-corrected p < 0.05. The SOC scale scores were positively correlated with fALFF values in the right superior frontal gyrus and the left inferior parietal lobule. Abbreviations: fALFF = fractional amplitude of low-frequency fluctuations, FWE = family-wise error, SOC = sense of coherence.
The level of depersonalization was negatively correlated with fALFF values in the right SFG (r = −0.36, p = 0.03), while this correlation was not evident in the left IPL (r = −0.29, p = 0.08) (S2 Fig). Then, we conducted mediation analyses to examine whether the SOC mediated the relationship between the fALFF values in the right SFG and depersonalization. The analyses revealed that the SOC was a significant mediator of the relationship (Fig 2).
Fig 2. Mediation analysis.
Mediation analysis found that the SOC mediated the relationship between the fALFF values in the R SFG and the depersonalization. Standardized coefficients and significance indicated by asterisks are reported for each path. *p < 0.05, **p < 0.01. Abbreviations: fALFF = fractional amplitude of low-frequency fluctuations, R = right, SFG = superior frontal gyrus, SOC = sense of coherence.
Discussion
To the best of our knowledge, this is the first report to examine the individual spontaneous neural activity associated with the levels of SOC in medical professionals.
Our results revealed that the fALFF values in the right SFG were positively correlated with SOC; i.e., nurses with higher fALFF values in the right SFG more frequently perceived stressors as controllable. The SFG is involved in various cognitive functions, including inhibition, working memory, and self-monitoring [44, 45]. Especially, this area plays a key role in cognitive control and the reappraisal of negative stimuli [28, 46, 47]. Our results suggest that greater spontaneous SFG activity may reflect more readiness [48, 49] and capacity in stress management via cognitive control/reappraisal [50]. This could be because spontaneous brain activity may implicate the energy consumption for maintaining the brain’s system ready at resting [20], and prompt subsequent cognitive performance when necessary [48, 51]. Along this line, the SFG activity showed an increase during cognitively demanding tasks such as clinical reasoning among medical residents [31, 32]. Taken together with those of previous studies, our findings suggest that the SFG is crucial in determining the capacity to regard stressful situations as manageable, understandable, and meaningful.
We also found that the fALFF values in the right SFG were associated with the depersonalization symptoms of burnout. Previous functional and structural MRI studies have repeatedly found that the dorsolateral prefrontal cortex, including the SFG, is associated with burnout severity [52, 53]. In line with the findings of these previous studies, the results highlight the crucial role of the SFG in burnout. Further, the mediation analysis showed that the SOC was a significant mediator of this relationship. One potential interpretation could be that the SOC may weaken depersonalization by prompting the optimistic reappraisal and understanding of stressful situations with a flexible shifting of perspectives [54, 55]. It may encourage the integration of conflicting stress cues into a coherent sense of experience. In effect, workplace stressors can be rather acknowledged as fulfilling/meaningful than as distressing; however, without sufficient SOC, frequent exposure to these stressors can lead to maladaptive coping.
In addition, the fALFF values in the left IPL were positively correlated with the SOC scale scores. The IPL is known to be involved in language processing, mathematical operations, the perception of facial emotion, and the interpretation of sensory information [56]. These functions are crucial in handling complex medical situations demanding verbal and nonverbal communication [57]. To this end, continued cultivation of such processing may develop into the maturity of SOC via the neuroplasticity effects of learning and experience. The current findings comprise practical hints for a better understanding of SOC and the prevention of burnout among medical professionals.
In summary, our results indicated that the levels of SOC among medical professionals can be indeed reflected in their resting-state brain activity. Participants’ levels of subjective well-being may be captured by the continuum of burnout to work-engagement with SOC as depicted in their spontaneous brain activity. To elaborate, burnout is considered one end of a continuum in the interpersonal relationship people establish at work in a maladaptive manner [37], and it contrasts with the opposite end (work engagement). In the interim, we noticed that fALFF values in the right SFG were linked to enhanced SOC and lessened depersonalization symptoms of burnout. In this viewpoint, individuals’ spontaneous SFG activity could implicate peoples’ SOC intensity that may weaken depersonalization subjectivity, and it may concurrently prompt engagement to work via the feeling of control, which further consolidates coherent experience to envision one’s past, present, and future experiences [58]. Recently, cognitive load theory is garnering more recognition in medical education; this theory draws upon the characteristics of working memory and long-term memory and the relationship between them to determine how people learn [59–61]. Cognitive load theory is of particular relevance to medical education owing to the tasks and professional activities that can be learned to necessitate the simultaneous integration of multiple sets of knowledge, behaviors, and skills at a specific time and place [59–62]. Future neuroimaging studies of SOC, alongside a cognitive load theory flamework, should aid us in better understanding how learners in the medical professions struggle with mastering complex concepts and developing toward expertise.
Recently, we reported that the structural brain correlates with the burnout severity in medical professionals using a voxel-based morphometric technique [4]. The results showed that the volumes of gray matter in the prefrontal cortices, including ventromedial prefrontal cortex, insula, and thalamus play a key role in the individual differences of burnout severity among medical professionals [4]. Combined with the findings of our previous study, the current RS-fMRI results for SOC provide useful information in developing effective interventions for burnout.
However, our study has several limitations that should be taken into consideration when interpreting its results. First, this is a cross-sectional study that precludes any interpretation of potential causality. To further examine the causal relationship between the strength of SOC and fALFF values, longitudinal studies should be conducted. In this endeavor, an interventional study promoting SOC should shed more light on this issue. Second, spontaneous brain activity was evaluated via fALFF in the context of functional segregation. Although this approach has been applied as a reliable method of localizing brain regions in relation to personality/behavioral traits [23, 24], future studies should combine the functional connectivity approach with fALFF to gain further insights into the network context [63]. In addition, studies repeatedly reported that gray matter density and white matter integrity were associated with individual differences in various types of social cognitive abilities in healthy subjects and clinical populations [64–67]. Future studies utilizing multimodal MRI should provide deeper insights into the SOC mechanisms among medical professionals. Finally, our sample consisted of only registered nurses. Thus, our study needs to be carefully re-examined and replicated with more participants from different occupational groups that varied in clinical experience levels.
Despite these limitations, the current study deepens our understanding of the neural underpinnings of SOC among medical professionals. Further studies on this subject will help determine the kinds of intervention and training that effectively impact the subjective experience of coherence/control in medical education.
Supporting information
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Acknowledgments
The authors wish to extend their gratitude to the research team of Kyoto University for their assistance in data acquisition.
Data Availability
All relevant data are within the manuscript and its Supporting Information files.
Funding Statement
This work was supported by grants-in-aid for scientific research A (24243061) to H.T., Young Scientists (20K16654) to J.F., Scientific Research C (21K07544) to S.T. and on Innovative Areas (23120009) too H.T., from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT). These agencies had no further role in the study design, the collection, analysis, and interpretation of data, the writing of the report, or in the decision to submit the paper for publication.
References
- 1.Kunno J, Supawattanabodee B, Sumanasrethakul C, Wiriyasirivaj B, Yubonpunt P. Burnout prevalence and contributing factors among healthcare workers during the COVID-19 pandemic: A cross-sectional survey study in an urban community in Thailand. PLoS One. 2022;17:e0269421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Teo I, Chay J, Cheung YB, Sung SC, Tewani KG, Yeo LF, et al. Healthcare worker stress, anxiety and burnout during the COVID-19 pandemic in Singapore: A 6-month multi-centre prospective study. PLoS One. 2021;16:e0258866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Southwick FS, Southwick SM. The loss of a sense of control as a major contributor to physician burnout: a neuropsychiatric pathway to prevention and recovery. JAMA Psychiatry. 2018;75(7):665–6. [DOI] [PubMed] [Google Scholar]
- 4.Abe K, Tei S, Takahashi H, Fujino J. Structural brain correlates of burnout severity in medical professionals: A voxel-based morphometric study. Neurosci Lett. 2022;772:136484. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shanafelt TD. Enhancing meaning in work: a prescription for preventing physician burnout and promoting patient-centered care. JAMA. 2009;302(12):1338–40. [DOI] [PubMed] [Google Scholar]
- 6.Kleim B, Bingisser MB, Westphal M, Bingisser R. Frozen moments: flashback memories of critical incidents in emergency personnel. Brain Behav. 2015;5(7):e00325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Li Y, Scherer N, Felix L, Kuper H. Prevalence of depression, anxiety and post-traumatic stress disorder in health care workers during the COVID-19 pandemic: A systematic review and meta-analysis. PLoS One. 2021;16(3):e0246454. doi: 10.1371/journal.pone.0246454 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Matsumoto Y, Fujino J, Shiwaku H, Miyajima M, Doi S, Hirai N, et al. Factors affecting mental illness and social stress in hospital workers treating COVID-19: Paradoxical distress during pandemic era. J Psychiatr Res. 2021;137:298–302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Uphoff EP, Lombardo C, Johnston G, Weeks L, Rodgers M, Dawson S, et al. Mental health among healthcare workers and other vulnerable groups during the COVID-19 pandemic and other coronavirus outbreaks: A rapid systematic review. PLoS One. 2021;16(8):e0254821. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Antonovsky A. The structure and properties of the sense of coherence scale. Soc Sci Med. 1993;36(6):725–33. [DOI] [PubMed] [Google Scholar]
- 11.Wolff AC, Ratner PA. Stress, social support, and sense of coherence. West J Nurs Res. 1999;21(2):182–97. [DOI] [PubMed] [Google Scholar]
- 12.Tei S, Becker C, Sugihara G, Kawada R, Fujino J, Sozu T et al. Sense of meaning in work and risk of burnout among medical professionals. Psychiatry Clin Neurosci. 2015;69:123–124. [DOI] [PubMed] [Google Scholar]
- 13.Levert T, Lucas M, Ortlepp K. Burnout in psychiatric nurses: Contributions of the work environment and a sense of coherence. S Afr J Psychol. 2000;30(2):36–43. [Google Scholar]
- 14.West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med. 2018;283(6):516–29. [DOI] [PubMed] [Google Scholar]
- 15.Van der Colff JJ, Rothmann S. Occupational stress, sense of coherence, coping, burnout and work engagement of registered nurses in South Africa. SA J Ind Psychol. 2009;35(1):1–10. [Google Scholar]
- 16.Deco G, Jirsa VK, McIntosh AR. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci. 2011;12(1):43–56. [DOI] [PubMed] [Google Scholar]
- 17.Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(7):4259–4264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lewis CM, Baldassarre A, Committeri G, Romani GL, Corbetta M. Learning sculpts the spontaneous activity of the resting human brain. Proc Natl Acad Sci U S A. 2009;106(41):17558–17563. doi: 10.1073/pnas.0902455106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Gusnard DA, Raichle ME: Searching for a baseline. functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2(10):685–694. [DOI] [PubMed] [Google Scholar]
- 20.Raichle ME. Two views of brain function. Trends Cogn Sci. 2010;14(4):180–190. [DOI] [PubMed] [Google Scholar]
- 21.Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137–141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Yan CG, Zang YF. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci. 2010;4:13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Fujino J, Tei S, Jankowski KF, Kawada R, Murai T, Takahashi H. Role of spontaneous brain activity in explicit and implicit aspects of cognitive flexibility under socially conflicting situations: a resting-state fMRI study using fractional amplitude of low-frequency fluctuations. Neuroscience. 2017;367:60–71 [DOI] [PubMed] [Google Scholar]
- 24.Wei L, Duan X, Zheng C, Wang S, Gao Q, Zhang Z et al. Specific frequency bands of amplitude low-frequency oscillation encodes personality. Hum Brain Mapp. 2014;35(1):331–339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yu M, Xu M, Li X, Chen Z, Song Y, Liu J. The shared neural basis of music and language. Neuroscience. 2017;357:208–219. [DOI] [PubMed] [Google Scholar]
- 26.Wang S, Zhou M, Chen T, Yang X, Chen G, Wang M et al. Grit and the brain: spontaneous activity of the dorsomedial prefrontal cortex mediates the relationship between the trait grit and academic performance. Soc Cogn Affect Neurosci. 2017;12(3):452–460. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kwak KH, Hwang HC, Kim SM, Han DH. Comparison of behavioral changes and brain activity between adolescents with internet gaming disorder and student pro-gamers. Int J Environ Res Public Health. 2020;17(2):441. doi: 10.3390/ijerph17020441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wang S, Zhao Y, Zhang L, Wang X, Wang X, Cheng B et al. Stress and the brain: perceived stress mediates the impact of the superior frontal gyrus spontaneous activity on depressive symptoms in late adolescence. Hum Brain Mapp. 2019;40(17):4982–4993. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Du L, Wang J, Meng B, Yong N, Yang X, Huang Q et al. Early life stress affects limited regional brain activity in depression. Sci Rep. 2016;6:25338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rajasilta O, Häkkinen S, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J et al. Maternal psychological distress associates with alterations in resting‐state low‐frequency fluctuations and distal functional connectivity of the neonate medial prefrontal cortex. Eur J Neurosci. 2023;57(2):242–257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Durning SJ, Costanzo M, Artino AR Jr., Dyrbye LN, Beckman TJ, Schuwirth L et al. Functional Neuroimaging Correlates of Burnout among Internal Medicine Residents and Faculty Members. Front Psychiatry. 2013;4:131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Durning SJ, Costanzo ME, Artino AR, Graner J, van der Vleuten C, Beckman TJ et al. Neural basis of nonanalytical reasoning expertise during clinical evaluation. Brain Behav. 2015;5(3):e00309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.de Andrade AP, Amaro E Jr., Farhat SC, Schvartsman C. Higher burnout scores in paediatric residents are associated with increased brain activity during attentional functional magnetic resonance imaging task. Acta Paediatr. 2016;105(6):705–713. [DOI] [PubMed] [Google Scholar]
- 34.Tei S, Becker C, Kawada R, Fujino J, Jankowski KF, Sugihara G et al. Can we predict burnout severity from empathy-related brain activity? Transl Psychiatry 2014;4:e393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Maslach C. Burnout: The Cost of Caring. Prentice Hall Trade: Englewood Cliffs, NJ, USA, 1982. [Google Scholar]
- 36.Kubo M, Tao M. Burnout among nurses-the relationship between stresses and burnout. J Exp Soc Psychol. 1994;34:33–43. [Google Scholar]
- 37.Leiter MP, Maslach C: Latent burnout profiles. A new approach to understanding theburnout experience. Burnout Res. 2016;3:89–100. [Google Scholar]
- 38.López-Núñez MI, Rubio-Valdehita S, Diaz-Ramiro EM, Aparicio-García ME. Psychological capital, workload, and burnout: what’s new? the impact of personal accomplishment to promote sustainable working conditions. Sustainability. 2020;12(19):8124. [Google Scholar]
- 39.Vîrgă D, Baciu E-L, Lazăr T-A, Lupșa D. Psychological capital protects social workers from burnout and secondary traumatic stress. Sustainability. 2020;12(6):2246. [Google Scholar]
- 40.Lee RT, Ashforth BE. A meta-analytic examination of the correlates of the three dimensions of job burnout. J Appl Psychol. 1996;81(2):123–133. [DOI] [PubMed] [Google Scholar]
- 41.Worsley KJ, Friston KJ: Analysis of fMRI time-series revisited—again. Neuroimage. 1995;2(3):173–181. [DOI] [PubMed] [Google Scholar]
- 42.Fujino J, Kawada R, Tsurumi K, Takeuchi H, Murao T, Takemura A, et al. An fMRI study of decision-making under sunk costs in gambling disorder. Eur Neuropsychopharmacol. 2018;28(12):1371–1381. [DOI] [PubMed] [Google Scholar]
- 43.Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40:879–891. [DOI] [PubMed] [Google Scholar]
- 44.Briggs RG, Khan AB, Chakraborty AR, Abraham CJ, Anderson CD, Karas PJ et al. Anatomy and white matter connections of the superior frontal gyrus. Clin Anat. 2020;33:823–832. [DOI] [PubMed] [Google Scholar]
- 45.Hu S, Ide JS, Zhang S, Chiang-shan RL. The right superior frontal gyrus and individual variation in proactive control of impulsive response. J Neurosci 2016;36(50):12688–12696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Falquez R, Lang S, Dinu-Biringer R, Nees F, Arens E, Kotchoubey B et al. On the relationship between negative affective priming and prefrontal cognitive control mechanisms. Cogn Emot 2016;30(2):225–244. [DOI] [PubMed] [Google Scholar]
- 47.Rodman AM, Jenness JL, Weissman DG, Pine DS, McLaughlin KA. Neurobiological markers of resilience to depression following childhood maltreatment: The role of neural circuits supporting the cognitive control of emotion. Biol psychiatry. 2019;86(6):464–473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp. 2005;26(1):15–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Smith K. Neuroscience: Idle minds. Nature. 2012;489(7416):356–358. [DOI] [PubMed] [Google Scholar]
- 50.Tang YY, Posner MI, Rothbart MK, Volkow ND. Circuitry of self-control and its role in reducing addiction. Trends Cogn Sci. 2015;19(8):439–444. [DOI] [PubMed] [Google Scholar]
- 51.Albert NB, Robertson EM, Miall RC. The resting human brain and motor learning. Curr Biol. 2009;19(12):1023–1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Chow Y, Masiak J, Mikołajewska E, Mikołajewski D, Wójcik GM, Wallace B et al. 2018. Limbic brain structures and burnout-A systematic review. Adv Med Sci. 2018;63:192–198. [DOI] [PubMed] [Google Scholar]
- 53.Gavelin HM, Neely AS, Andersson M, Eskilsson T, Järvholm LS, Boraxbekk CJ. Neural activation in stress-related exhaustion: cross-sectional observations and interventional effects. Psychiatry Res Neuroimaging. 2017;269:17–25. [DOI] [PubMed] [Google Scholar]
- 54.Ochsner KN, Gross JJ. The cognitive control of emotion. Trends Cogn Sci. 2005;9(5):242–249. [DOI] [PubMed] [Google Scholar]
- 55.Tei S, Fujino J, Kawada R, Jankowski KF, Kauppi JP, van den Bos et al. Collaborative roles of temporoparietal junction and dorsolateral prefrontal cortex in different types of behavioural flexibility. Sci Rep. 2017;7(1):1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Igelström KM, Graziano MS. The inferior parietal lobule and temporoparietal junction: a network perspective. Neuropsychologia. 2017;105:70–83. [DOI] [PubMed] [Google Scholar]
- 57.Vogel D, Meyer M, Harendza S. Verbal and non-verbal communication skills including empathy during history taking of undergraduate medical students. BMC Med Educ. 2018;18(1);1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN. Wandering minds: the default network and stimulus-independent thought. Science. 2007;315(5810):393–395 doi: 10.1126/science.1131295 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Leppink J, van den Heuvel A. The evolution of cognitive load theory and its application to medical education. Perspect Med Educ. 2015;4:119–127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Fraser KL, Ayres P, Sweller J. Cognitive load theory for the design of medical simulations. Simul Healthc. 2015;10(5):295–307. doi: 10.1097/SIH.0000000000000097 [DOI] [PubMed] [Google Scholar]
- 61.Young JQ, Sewell JL. Applying cognitive load theory to medical education: construct and measurement challenges. Perspect Med Educ. 2015;4:107–109. doi: 10.1007/s40037-015-0193-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Tei S, Fujino J. The educational value of sense of coherence for grief care. Front Psychol. 2022;13. doi: 10.3389/fpsyg.2022.1037637 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Chen C, Yao J, Lv Y, Zhao X, Zhang X, Lei J et al. Aberrant Functional Connectivity of the Orbitofrontal Cortex Is Associated With Excited Symptoms in First-Episode Drug-Naïve Patients With Schizophrenia. Front Psychiatry. 2022;13:922272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Takeuchi H, Taki Y, Sassa Y, Hashizume H, Sekiguchi A, Fukushima A et al. Regional gray matter density associated with emotional intelligence: Evidence from voxel‐based morphometry. Hum Brain Mapp. 2011;32(9):1497–1510. doi: 10.1002/hbm.21122 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Lenka A, Ingalhalikar M, Shah A, Saini J, Arumugham SS, Hegde S et al. Abnormalities in the white matter tracts in patients with Parkinson disease and psychosis. Neurology. 2020;94(18):e1876–e1884. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Wang Y, Olson IR. The original social network: white matter and social cognition. Trends Cogn. Sci. 2018;22(6):504–516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Fujino J, Takahashi H, Miyata J, Sugihara G, Kubota M, Sasamoto A et al. Impaired empathic abilities and reduced white matter integrity in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2014;48:117–123. [DOI] [PubMed] [Google Scholar]
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