Skip to main content
Download PDF

Psychological characteristics and emotional difficulties underlying school refusal in adolescents using functional near-infrared spectroscopy

BMC Psychiatry volume 23, Article number: 898 (2023) Cite this article

Abstract

Background

This study aims to explore the psychological characteristics, related emotional problems and potential NIR brain function mechanism of adolescents who refuse to attend school.

Methods

The study included 38 adolescents (12–18 years old) who were not attending school and 35 healthy controls (12–18 years old) who are attending school regularly. Participants completed (1) general demographics, (2) Eysenck Personality Questionnaire (EPQ), (3) Zung Self-Rating Depression Scale (SDS), (4) Zung Self-Rating Anxiety Scale (SAS), and (5) Symptom Checklist-90 (SCL-90). In addition to the clinical tests, participants completed functional near-infrared spectroscopy (fNIRS). Mental health, personality, and emotional state were evaluated in both groups to explore the differences and to understand the underlying mechanisms of school refusal during adolescence.

Results

Adolescents who did not attend school had higher neuroticism scores on the Eysenck Personality Questionnaire than healthy controls (p(FDR) < 0.001), introversion and concealment scores were lower than those of healthy controls (p(FDR) < 0.001), there was no significant difference in psychoticism scores between groups. SDS, SAS, SCL-90 scores and factor scores were higher than those of healthy control group (p(FDR) < 0.001), NIR functional brain imaging was different from healthy control group in the 12 and 27 channels (p(FDR) = 0.030, p(FDR) = 0.018), and no difference was found in the remaining channels (p(FDR) > 0.05). There were statistically significant differences in age and gender between the adolescents who refused school and the control group (p(FDR) < 0.001).

Conclusion

School refusal adolescents are relatively introverted and sensitive and need more attention in daily life. Although the adolescents’ emotional problems did not reach the diagnostic criteria of depressive disorder and anxiety disorder, their scores were still higher than those of the control group, suggesting that we should pay more attention to their emotional problems in order to better help them return to school. Using fNIRS, it was found that abnormalities in frontal lobe regions in adolescents with school refusal behaviors, which would contribute to early diagnosis and timely intervention of school refusal behaviors.

Peer Review reports

Introduction

School refusal is a common emotional problem in teenagers. School refusal (SR) refers to the spontaneous reluctance of the child or adolescent to attend school and/or the difficulty in staying at school for the entire day with the knowledge of the parent [1, 2]. Studies have shown the incidence of school refusal in school-aged children varies from 1-5% [3]. There are no known gender differences in SR [4]. However, studies have shown that school refusal is more common in two age groups : children aged between 5 and 7 years old and adolescents aged between 11 and 14 years old [5]. It’s reported that individual factors, family factors and school factors are closely related to adolescents’ school refusal behavior [6,7,8]. Previous studies have shown that extraversion, neuroticism and psychoticism are related to school refusal: extroverted adolescents who fail to meet academic requirements may turn their interest to other things besides learning in order to pursue happiness, resulting in school refusal [9, 10]. Highly neuroticism adolescents have a higher prevalence of school refusal because they are emotionally unstable and are often accompanied by a sense of victimization [8]; Highly psychoticism adolescents show maladjustment nature. These adolescents lack of care for others and strained interpersonal relationships with classmates, which increases the prevalence of school refusal [9]. Inhibited and self-critical personality traits have also been found with school refusal, which is characterized by feeling of shame and self-critical tendencies in social situations. Adolescents with these personalities choose school refusal to avoid social situations that might lead to feelings of shame and devaluation [8]. Mental health problems such as anxiety, depression and oppositional defiant disorder were also related to school refusal [8, 11], for example, adolescents with separation anxiety disorders choose school refusal to draw attention to significant others; school refusal individuals for out-of-school tangible reinforcement often have oppositional defiant disorder [12].Many adolescents suffer from academic burnout due to academic pressures which could be one of the leading causes to SR [13]. SR and truancy have direct short-term consequences which include academic failure, isolation by peer groups, deterioration of parent-child relationship, and violence or delinquency [14, 15]. SR is also associated with long-term negative consequences on the development of socialization, education, and on increasing risk for substance abuse, marital crisis, employment difficulties during adulthood and mental disorders [14,15,16].

Cultural differences in school refusal behavior have been reported [17]. In China, the prevalence of school refusal is increasing year by year, most studies have focused on the definition of school refusal and some influencing factors [18]. Liu et al. suggest that the development and maintenance of school refusal behavior in Chinese adolescents is the result of the interaction between the social environment, family conflict, and individual psychological factors. There are five main aspects: (1) a competition-oriented social environment; (2) a conflict-ridden family living space; (3) a lack of supportive personal living space; (4) a conflict between the pros and cons of being labeled as psychiatric diagnosis; (5) reintegrating into school life [19]. While little attention has been paid to the personality traits, emotional and neural underpinnings of Chinese adolescents’ school refusal behavior. Xu et al. suggest that the development of school refusal is a process from cognition to emotion to externalized behavior and is gradually serious [20]. Therefore, the study of the emotional manifestations and physiological mechanisms of adolescents who have school refusal can provide a basis for early intervention. Focusing on the emotional characteristics of Chinese adolescents who have school-refusal and disseminating this mental health knowledge to schools and parents can effectively improve the early identification of school refusal behavior, support and guide adolescents to seek help from professionals, and reduce the risk of adolescents dropping out of school. No studies about the mechanism of SR is reported until now. Studying the neural underlying mechanisms of school refusal will lead to more accurate early diagnosis of this behavior, which provide early identification and individualized treatment.

Here, in this study, we aim to study the emotional mechanisms and brain correlates of the emotional difficulties that underly school refusal during adolescence, using functional near-infrared spectroscopy (fNIRS). As previous studies reported the emotional problems of adolescents with school refusal [7, 15, 21], in addition, the frontal and temporal cortex is correlated with the emotion and cognition [22,23,24,25]. fNIRS is a new non-invasive technique that is capable of measuring changes in oxygenated and deoxygenated hemoglobin of frontal and temporal cortex. fNIRS has advantages such as low cost, harmless to participants, easy to use, and well endured [26],and has been widely used in the study of mental disorders, such as depressive disorder, bipolar disorder, schizophrenia and others [27]. The verbal fluency test (VFT) is most often used in the collection of fNIRS data. fNIRS in conjunction with the VFT has been widely used in psychiatric research. In various mental disorders, frontal and temporal lobe regions of the brain are significantly less activated (i.e., the increase in Hbo is significantly reduced) during the VFT [28]. For example, in patients with schizophrenia, it has been shown that using the VFT during fNIRS testing reduces the increase in frontal and temporal lobe Hbo in patients compared to controls, implying that patients with schizophrenia experience hemodynamic changes [28, 29]. Depressed patients have reduced levels of left prefrontal activation during the VFT and poor task performance [30]. The VFT strategically accesses lexical-semantic information, so there is a reliance on and activation of the superior medial frontal cortex, the ventral lateral prefrontal cortex (VLPFC), and the anterior temporal lobes during the VFT, especially the left hemisphere [28, 31,32,33].

However, at present, no studies have used fNIRS and VFT to explore the mechanism of brain function in adolescents who refuse to go to school. Therefore, in addition to exploring the psychological characteristics and emotional difficulties of adolescents with SR, we are using fNIRS in combination with VFT to explore differences in brain function between adolescents who refuse school and control adolescents who do not have trouble with school refusal, particularly in the frontal and temporal lobe regions of school refusal in adolescents.

Methods

Participants

In this study, 38 adolescents (aged between 12 and 18 years old) were recruited in the study between February and December 2019. Recruitment was based on patients’ admission to the Children and Adolescents Outpatient Department of Mental Health, the First Hospital of Shanxi Medical University. The inclusion criteria consisted of: (1) age between 12 and 18 years old; (2) functional assessment of school refusal as suggested by Kearney and Albano,2004 [34]; (3) more than 50% of school absence or absence in the 4 weeks prior to the visit. Exclusion criteria consists of an anxiety and depression diagnosis which is assessed by the psychiatrist of the study using the Mini-International Neuropsychiatric Interview (M.I.N.I.)(compatible with the Diagnostic and Statistical Manual of Mental Disorders, fifth Edition (DSM-5)) [35]. We recruited healthy controls (n = 35) locally from advertisement. The inclusion for the HC including aged 12–18 years old, without gender limitation, the flyers for including healthy controls (aged 12–18 years old, without gender limitation, Han nationality) were posted in middle schools in Taiyuan City. All the informed consent were obtained from the participants themselves and their parents/caregivers. (NO. of ethics approval: KYLL-2023-080).

Measures

General demographic data

We collected demographic data from all participants that included age, gender, and education level.

Eysenck Personality Questionnaire

We used the Chinese version of Eysenck Personality Questionnaire (EPQ) that consists of four subscales: extraversion (E), neuroticism (N), psychoticism (P) and lying (L) [36]. Binary answers were provided. The Chinese version of Eysenck Personality Questionnaire has high reliability and validity [37, 38].

Zung Self-rating Depression Scale

The Chinese version of Zung Self-rating Depression Scale (SDS) [39] is used for assessment of depressive symptoms which includes 20 items and each item is graded 1 to 4. Some items 2, 5, 6, 11, 12, 14, 16, 17, 18 and 20 are graded in reverse. Previous studies have found the scale to be appropriate and commonly used by Chinese people [40,41,42]. Mild depression is considered when the total score is between 50 and 59. A moderate depression is considered when the total score is between 60 and 69, and a severe depression is associated to a score above 69. The Chinese version of Zung Self-rating Depression Scale has high reliability and validity [43, 44].

Zung Self-Rating anxiety scale

There are 20 items in the Chinese version of Zung Self-Rating Anxiety Scale (SAS) [45], and each item is graded on 1 to 4 levels. Among them, items 5, 9, 13, 17 and 19 are graded in reverse. Studies demonstrate that the scale can be widely used to screen for anxiety in the Chinese population [40, 42, 46]. A score between 50 and 59 is associated with mild anxiety, a score between 60 and 69 is associated with moderate anxiety, and a score above 69 scores is associated with severe anxiety. The Cronbach’s α coefficient was 0.913, and has high constructive validity [47] .

Symptom Checklist 90

We used the Chinese version of Symptom Checklist 90 (SCL-90),which consists of 90 items with each item graded 1 to 5. SCL-90 includes 10 factors that reflect somatization, obsessive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism, and the other aspects of psychological symptoms(measurement of individual sleep and diet) [48]. If the total score is over 160, or the number of positive items is more than 43, or the mean of factor score is ≥ 2, this will be an indication of mild or above psychological problems. If the mean of each factor score is ≥ 2.5, this indicates that the psychological pain has reached a moderate level or above. If the individual factor score is ≥ 3, this indicates that the pain level has reached a mild or above severe level, indicating the possibility of psychological problems. The Chinese version of SCL-90 has high reliability and validity. The reliability of the general scale was 0.97, and was over 0.67 for all the subscales. Test-retested correlation was over 0.70. SCL-90 had high content validity and constructive validity [49].

Data collection

The hemodynamic responses in the prefrontal cortices and superior temporal cortices was measured by a 52-channel fNIRS system (ETG-4100. Hitachi Medical Co., Tokyo, Japan) with 2 NIR light wavelengths (695 and 830 nm). The fNIRS system contains 16 light detectors and 17 light emitters, all of which were arranged in a 3 × 11 array to form 52 measurement channels. All the participants were asked to perform a Verbal Fluency Task (VFT) in a quiet environment. Participants were asked to seat with eyes open, avoiding excessive body and head movements, and focusing on a cross on the screen. The VFT test comprised a 30-s pre-task period, a 60-s task period, and a 70-s post-task period. During the pre- and post-task periods, the participants were asked to constantly say “one, two, three, four, five” repeatedly. During the task period, the participants were asked to think as many four-character idioms or phrases as possible, which begin with big, white, and sky [29].

fNIRS analysis

Data preprocessing

The near-infrared spectroscopy signals were preprocessed using the NIRS-SPM toolbox, which is a MATLAB-based software package (MATLAB 2013b). The preprocessing steps included: transforming all .csv files into NIRS-SPM available .mat files; checking for participants’ available channels.

Calculate the β-value

The NIRS-SPM toolbox mainly uses the general linear model (GLM) method in data analysis, The GLM is formulated as follows: Y = βX + ε. In this study, β is represents the level of cortical activation during the VFT.

First, low-frequency drift generated by breathing, heartbeat, or other factors was conducted using the discrete cosine transform (DCT). Physiological noise was filtered using a low-pass filter that is based on the hemodynamic response function (HRF).

Second, a GLM was constructed using the time series associated with rest and task performance as the independent variables, the oxyhemoglobin concentration as the dependent variables. The first-order derivative and second-order derivative of the time series were used as covariates in this process.

Third, the value of β was calculated.

Index extraction

The values of β were extracted for 52 channels for participants. The degree of activation of the brain cortex during the VFT task was assessed by δβ value of oxy-hemoglobin (VFT β value minus baseline β value).

Statistical analysis

SPSS 22.0 was used for the data analysis (SPSS Inc, Chicago, IL, USA).

The categorical data (gender) were analyzed with the chi-square test. The numerical data was analyzed using two independent sample test, including age, educational age, duration of SR, total scores and each subscale scores of EPQ, SAS, SDS, SCI-90 (with age, gender and educational years as covariates). A one-way ANOVA was used, with group as the between-group factor (SR group and HC group), and age and gender as covariates. This was used to compare the differences in frontal and temporal cortex activation levels between the SR group and HC group. The p-value was corrected by false discovery rate correction(FDR, the FDR correction method ranks multiple hypotheses according to the magnitude of the p-value and then the significance level of each hypothesis is determined according to the ranked order) [50, 51]. And less than 0.05 was considered statistically significant. Bonferroni test was used for post-hoc analysis to identify the sources of differences (Bonferroni corrections to minimize type I errors, specifically raw p value*number of t tests. Values less than 0.05 reached by the Bonferroni correction were considered statistically significant).

Mean δβ values were extracted for channels with statistically significant results, and correlation between δβ values and clinical symptoms (EPQ subscale scores, SAS total score, SDS total score, and SCL-90 total score) using Pearson’s correlation analysis (with age, gender and educational years as covariates). The p-value was also corrected by FDR.

Results

General demographic data on adolescents who refuse to attend school

Age and gender were significantly different between the SR group and HC group (p < .001, see Table 1). No significant differences were found between the two groups in years of education (p = .677, see Table 1). The average duration of refusal was 10.39 ± 13.29 months for the SR group.

Table 1 Comparison of Demographic data, EPQ and emotional problems in the SR and HC
Full size table

Personality characteristics of SR adolescents

It was found that the school refusal group had lower scores of extraversion (E) and concealment (L), and higher scores of neuroticism (N) than the control group (p < .001), significant after Bonferroni corrections or multiple comparison (4 subscales). There were no significant differences in psychoticism (P) score between the two groups (p = .114, the p-value was corrected using FDR). The results remained the same after controlling for age and gender (see Table 1; Fig. 1).

Fig. 1
figure 1

The difference of EPQ, SDS, SAS and SCL-90 between the two groups

Full size image

Emotional characteristics of SR adolescents

The scores of SAS, SDS, SCL-90 of the SR group were higher than the scores of the HC group (p < .001, the p-value was corrected using FDR). SDS scores of the SR group were in the range of moderate depression. SAS scores were in the range of mild anxiety, and scores of other factors except somatization factor were all higher than 2 in the SR group. These results remained the same after controlling for age and gender (see Table 1; Fig. 1).

Neural correlates of school refusal adolescents

When comparing brain activity between the two groups, it was found that the δβ value in channel 12 in the SR group was higher than in HC group (p = .030, the p-value was corrected using FDR). The δβ value in channel 27 was found lower in SR group than in HC group (p = .018, the p-value was corrected using FDR) (see Table 2; Fig. 2).

Table 2 The differentiated channels between the two groups
Full size table
Fig. 2
figure 2

The difference of NIRS Channels between the two groups

Full size image

We correlated the brain activity in these channels with the duration of school refusal as well with subscales of EPQ, SAS total score, SDS total score, and SCL-90 total score, with age and gender as covariates. We found only a significant correlation between channel 27 and EPQ-E (r=-.486, p = .019, the p-value was corrected using FDR), EPQ-N (r=-.419, p = .047, the p-value was corrected using FDR) (See Table 3)and no other correlations were found. We therefore found that activity in frontal areas was negatively correlated with extraversion and neuroticism. Higher scores in neuroticism which can encompass the negative valence in the SR groups is predicted by lower fNIRS activity in frontal areas.

Table 3 The correlation of clinical variables and differentiated channels
Full size table

Discussion

In this study, we studied the behavioral and neural correlates using fNIRS technology of adolescents with school refusal behavior (SR) and healthy controls.

The significant difference between the gender and age of the SR group and the HC group suggests a mismatch between the gender and age of the participants in the two groups in this study, which is a limitation of this study. During the data analysis, we have included gender and age as covariates to control the effect that demographic data to the results. In addition, in this study, the mean of the age of the participants in the SR group was 14.42, which is representative of adolescents with school refusal behaviors, and this stage is in the middle of an individual’s adolescence. In this stage, in addition to great physical changes, the environment (including society, school, and family) of the individual is also changing, and family conflicts increase during adolescence [52], which suggests that problematic family functioning is associated with adolescents’ school refusal behavior [53].

We assessed their emotional states and personality characteristics using EPQ, SDS, SAS, and SCL-90. First, we found that adolescents with SR scored higher on neuroticism subscale of the EPQ than controls. They also scored lower on extraversion subscale of the EPQ than controls. Lower scores on extraversion and higher scores on neuroticism in EPQ have been previously linked to higher mental health problems [54]. Lower scores in extraversion and higher scores in neuroticism are associated with higher risk for depression and anxiety [54, 55]. Our findings are in line with previous studies on school refusal where they found that children and adolescents are more timid, lonely, and withdrawn compared to controls [56].

Previous studies on SR children found that they score higher on psychoticism and neuroticism and scored lower on extraversion compared to controls [56]. These results suggest that adolescents are more likely to have emotion dysregulation and have higher negative states and lower social processing which can impact acceptance to go to school.

Second, adolescents in the school refusal group displayed higher scores of depression and anxiety using SAS and SDS in comparison to healthy controls. This is in line with other studies where they found higher scores of anxiety and depression in school refusal teenagers [13, 14]. Several factors might be influencing adolescents’ school refusal behavior. Family factors are key to adolescents’ school refusal behavior [57]. Interventions with parents as part of a family therapy can significantly alleviate some of the major negative emotions in adolescents with school refusal behavior, such as anxiety, depression, and help them return to school [58].

These studies suggest that we need to pay close attention to the emotional problems of adolescents who refuse to attend school, and take appropriate measures to intervene and encourage them to re-enter school.

Third, at the brain level, our study found statistically significant differences in channels 12 and 27 between the two groups, suggesting that the temporal and frontal oxygenated hemoglobin concentrations in adolescents with SR differ from healthy controls when conducting a cognitive task, which was consistent with the emotional assessment results in this study. Adolescents with SR exhibited lower brain activity in frontal areas (channel 27) during the cognitive task, in comparison to healthy controls. This brain activity in SR group was found negatively correlated with neuroticism (as part of the EPQ personality test). Lower activity in channel 27 was associated with higher scores in neuroticism. School refusal might stem in part from lack of inhibition and less emotion regulation, suggesting that early psychological intervention is needed for school refusal problem.

Our results suggest that there are likely biological underpinnings for school refusal and that fNIRS technology can capture or predict adolescents that are more at risk to refuse to go to school with a lack of activity in frontal areas in response to cognitive tasks. Further studies addressing brain mechanisms in adolescents using NIRS technology might be helpful to better understand the nature of school refusal behavior.

These results also show that adolescents who refuse to go to school have emotional problems that do not yet reach the diagnostic criteria for mental disorders, and that these emotional problems are related to the duration of school refusal. Therefore, it will be helpful to address this problem with psychological consultation that aims to regulate emotions with cognitive treatments. Families should immediately seek professional help and take timely professional intervention measures for adolescents who refuse to go to school, to enhance the chances of adaptive behaviors and to resolve this serious problematic behavior.

Limitations

One caveats for this study is the relatively small sample size. Future studies with larger sample sizes can explore brain and behavioral differences between the two groups with an increased power. Another limitation is that the age and gender were not matched between groups. Demographic data were added as covariates in our analysis to control for its effects on behavior and brain function. Future studies can conduct follow-up sessions to better explore outcomes.

Conclusion

Adolescents with school refusal behavior have higher scores in neuroticism and higher depression and anxiety. They also show lower activity in frontal areas during cognitive tasks, measured by fNIRS technology. These results suggest that addressing emotion regulation and enhancing cognitive control early in the process can be necessary to improve prognosis. Future studies with cognitive and family interventions might alleviate some of these symptoms and have kids go back to school and continue their education.

Data Availability

The data used and analysed during the current study available from the corresponding author, upon on reasonable request.

References

  1. Elliott JG, Place M, Practitioner, Review. School refusal: developments in conceptualisation and treatment since 2000. J Child Psychol Psychiatr. 2019;60:4–15.

    Article  Google Scholar 

  2. Prabhuswamy M. To go or not to go: School refusal and its clinical correlates: School refusal and clinical correlates. J Paediatr Child Health. 2018;54:1117–20.

    Article  PubMed  Google Scholar 

  3. Burke AE, Silverman WK. The prescriptive treatment of school refusal. Clin Psychol Rev. 1987;7:353–62.

    Article  Google Scholar 

  4. Fremont WP. School Refusal in Children and Adolescents. 2003;68:7.

  5. Heyne D, King NJ, Tonge BJ, Cooper H. School Refusal: Epidemiology and Management. Paediatr Drugs. 2001;3:719–32.

    Article  CAS  PubMed  Google Scholar 

  6. Burrus J, Roberts RD. Dropping Out of High School: Prevalence, Risk Factors, and Remediation Strategi. 2012;9.

  7. Nayak A, Sangoi B, Nachane H. School Refusal Behavior in Indian Children: analysis of Clinical Profile, psychopathology and development of a best-fit risk Assessment Model. Indian J Pediatr. 2018;85:1073–8.

    Article  PubMed  Google Scholar 

  8. Carpentieri R, Iannoni ME, Curto M, Biagiarelli M, Listanti G, Andraos MP, et al. School Refusal Behavior: role of personality styles, Social Functioning, and Psychiatric symptoms in a sample of adolescent help-seekers. Clin Neuropsychiatr. 2022;19:20–8.

    Google Scholar 

  9. Petrides KV, Chamorro-Premuzic T, Frederickson N, Furnham A. Explaining individual differences in scholastic behaviour and achievement. Br J Educ Psychol. 2005;75:239–55.

    Article  CAS  PubMed  Google Scholar 

  10. Swickert RJ, Rosentreter CJ, Hittner JB, Mushrush JE. Extraversion, social support processes, and stress. Pers Indiv Differ. 2002;32:877–91.

    Article  Google Scholar 

  11. Melvin GA, Heyne D, Gray KM, Hastings RP, Totsika V, Tonge BJ et al. The kids and teens at School (KiTeS) Framework: an inclusive Bioecological systems Approach to understanding School Absenteeism and School attendance problems. Front Educ. 2019;4.

  12. Haight C, Kearney CA, Hendron M, Schafer R. Confirmatory analyses of the School Refusal Assessment Scale-Revised: replication and extension to a truancy sample. J Psychopathol Behav Assess. 2011;33:196–204.

    Article  Google Scholar 

  13. Li A, Guessoum SB, Ibrahim N, Lefèvre H, Moro MR, Benoit L. A systematic review of somatic symptoms in School Refusal. Psychosom Med. 2021;83:715–23.

    Article  PubMed  Google Scholar 

  14. Seçer İ, Ulaş S. The Mediator Role of Academic Resilience in the relationship of anxiety sensitivity, Social and adaptive functioning, and School Refusal with School attachment in High School Students. Front Psychol. 2020;11:557.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gonzálvez C, Díaz-Herrero Á, Vicent M, Sanmartín R, Pérez-Sánchez AM, García-Fernández JM. Subtyping of adolescents with School Refusal Behavior: exploring differences across profiles in Self-Concept. Int J Environ Res Public Health. 2019;16:4780.

    Article  PubMed  PubMed Central  Google Scholar 

  16. McCune N, Hynes J. Ten year follow-up of children with school refusal. Ir j Psychol med. 2005;22:56–8.

    Article  Google Scholar 

  17. Tekin I, Erden S, Şirin Ayva AB, Büyüköksüz E. The predictors of school refusal: Depression, anxiety, cognitive distortion and attachment. HumanSciences. 2018;15:1519.

    Article  Google Scholar 

  18. Gonzálvez C, Kearney CA, Jiménez-Ayala CE, Sanmartín R, Vicent M, Inglés CJ, et al. Functional profiles of school refusal behavior and their relationship with depression, anxiety, and stress. Psychiatry Res. 2018;269:140–4.

    Article  PubMed  Google Scholar 

  19. Liu L, Gu H, Zhao X, Wang Y. What contributes to the Development and Maintenance of School Refusal in Chinese adolescents: a qualitative study. Front Psychiatry. 2021;12.

  20. Xu Y, Xue B, Luo H, Hu Z. Epidemiological situation of adolescents’ study-weariness. Health Res. 2022;42:241–5.

    Google Scholar 

  21. Jones AM, West KB, Suveg C. Anxiety in the School setting: a Framework for evidence-based practice. School Mental Health. 2019;11:4–14.

    Article  Google Scholar 

  22. Goodwin GM. Neuropsychological and neuroimaging evidence for the involvement of the frontal lobes in depression. J Psychopharmacol. 1997;11:115–22.

    Article  CAS  PubMed  Google Scholar 

  23. Du J, Rolls ET, Cheng W, Li Y, Gong W, Qiu J, et al. Functional connectivity of the orbitofrontal cortex, anterior cingulate cortex, and inferior frontal gyrus in humans. Cortex. 2020;123:185–99.

    Article  PubMed  Google Scholar 

  24. Kawashima C, Tanaka Y, Inoue A, Nakanishi M, Okamoto K, Maruyama Y, et al. Hyperfunction of left lateral prefrontal cortex and automatic thoughts in social anxiety disorder: a near-infrared spectroscopy study. J Affect Disord. 2016;206:256–60.

    Article  PubMed  Google Scholar 

  25. Besteher B, Gaser C, Langbein K, Dietzek M, Sauer H, Nenadic I. Effects of subclinical depression, anxiety and somatization on brain structure in healthy subjects. J Affect Disord. 2017;215:111–7.

    Article  PubMed  Google Scholar 

  26. Wei Y, Chen Q, Curtin A, Tu L, Tang X, Tang Y, et al. Functional near-infrared spectroscopy (fNIRS) as a tool to assist the diagnosis of major psychiatric disorders in a Chinese population. Eur Arch Psychiatry Clin Neurosci. 2021;271:745–57.

    Article  PubMed  Google Scholar 

  27. Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage. 2012;63:921–35.

    Article  PubMed  Google Scholar 

  28. Yeung MK, Lin J. Probing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: a systematic review and meta-analysis. J Psychiatr Res. 2021;140:416–35.

    Article  PubMed  Google Scholar 

  29. Liang N, Liu S, Li X, Wen D, Li Q, Tong Y, et al. A decrease in hemodynamic response in the right Postcentral cortex is Associated with Treatment-Resistant Auditory Verbal Hallucinations in Schizophrenia: an NIRS Study. Front Neurosci. 2022;16:865738.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Okada G, Okamoto Y, Morinobu S, Yamawaki S, Yokota N. Attenuated left prefrontal activation during a Verbal Fluency Task in patients with Depression. Neuropsychobiology. 2003;47:21–6.

    Article  CAS  PubMed  Google Scholar 

  31. Henry JD, Crawford JR. A Meta-Analytic Review of Verbal Fluency performance in patients with traumatic brain Injury. Neuropsychology. 2004;18:621–8.

    Article  PubMed  Google Scholar 

  32. Robinson G, Shallice T, Bozzali M, Cipolotti L. The differing roles of the frontal cortex in fluency tests. Brain. 2012;135:2202–14.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Wagner S, Sebastian A, Lieb K, Tüscher O, Tadić A. A coordinate-based ALE functional MRI meta-analysis of brain activation during verbal fluency tasks in healthy control subjects. BMC Neurosci. 2014;15:19.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kearney CA, Albano AM. The functional profiles of School Refusal Behavior: diagnostic aspects. Behav Modif. 2004;28:147–61.

    Article  PubMed  Google Scholar 

  35. Sheehan DV. The mini-international neuropsychiatric interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry. J Clin Psychiatry.

  36. Eysenck H, Eysenck S. Manual of the Eysenck Personality Questionnaire. London: University of London Press; 1975.

    Google Scholar 

  37. Cheng Z, Tan L. The factor analysis of the EPQ Structural Validity. Chin J Clin Psychol. 2004;01:9–12.

    Google Scholar 

  38. Gong Y. Eysenck Personality Questionnaire-revised in China. J Psychol Sci. 1984;04:13–20.

    Google Scholar 

  39. Zung W. Zung Self Rating Depression Scale. Arch Gen Psychiatry. 1965;12:63–70.

    Article  CAS  PubMed  Google Scholar 

  40. Shen L, Lao L, Jiang S, Yang H, Ren L, Ying DG-C, et al. A survey of anxiety and depression symptoms among primary-care Physicians in China. Int J Psychiatry Med. 2012;44:257–70.

    Article  PubMed  Google Scholar 

  41. Lee HC, Chiu HFK, Wing YK, Leung CM, Kwong PK, Chung DWS. The Zung Self-Rating Depression Scale: screening for Depression among the Hong Kong Chinese Elderly. J Geriatr Psychiatry Neurol. 1994;7:216–20.

    Article  CAS  PubMed  Google Scholar 

  42. Wang Xq, Lambert CE, Lambert VA. Anxiety, depression and coping strategies in post-hysterectomy Chinese women prior to discharge. Int Nurs Rev. 2007;54:271–9.

    Article  CAS  PubMed  Google Scholar 

  43. Wang C, Cai Z, Xu Q. Zung Self-rating Depression Scale-SDS in 1340 healthy individuals. Chin J Nerv Mental Dis. 1986;05:267–8.

    CAS  Google Scholar 

  44. Wang Z, Chi Y. Zung Self-rating Depression Scale (SDS). Shanghai Archives of Psychiatry. 1984;02:71–2.

    Google Scholar 

  45. Zung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;371:10.

    Google Scholar 

  46. Sun W, Fu J, Chang Y, Wang L. Epidemiological study on risk factors for anxiety disorder among Chinese doctors. Jrnl of Occup Health. 2012;54:1–8.

    Article  Google Scholar 

  47. Tao M, Gao J. The validity and reliability of SAS-CR (SAS-Chinese revised). Chin J Neuro-Psychiatr Disord. 1994;05:1–8.

    Google Scholar 

  48. Hafkenscheid A. Psychometric evaluation of the symptom checklist (SCL-90) in psychiatric inpatients. Pers Indiv Differ. 1993;14:751–6.

    Article  Google Scholar 

  49. Chen S, Li L. SCL-90 reliability and validity, and norm re-comparisons. Chin J Neuro-Psychiatr Disord. 2003;05:323–7.

    Google Scholar 

  50. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279–84.

    Article  CAS  PubMed  Google Scholar 

  51. Singh AK, Dan I. Exploring the false discovery rate in multichannel NIRS. NeuroImage. 2006;33:542–9.

    Article  PubMed  Google Scholar 

  52. Weymouth BB, Buehler C, Zhou N, Henson RA. A Meta-analysis of parent–adolescent conflict: disagreement, hostility, and Youth Maladjustment. J Family Theory Rev. 2016;8:95–112.

    Article  Google Scholar 

  53. Gonzálvez C, Díaz-Herrero Á, Sanmartín R, Vicent M, Pérez-Sánchez AM, García-Fernández JM. Identifying risk profiles of School Refusal Behavior: differences in social anxiety and family functioning among Spanish adolescents. Int J Environ Res Public Health. 2019;16:3731.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Xu Y, Wu D, Xu Y, Zhang J, Peng X. Mediating effect of personality on relationship between optimistic, pessimistic tendencies and depression in university students. Chin J Clin Psychol. 2011;19:116–8.

    CAS  Google Scholar 

  55. Yu L, Sheng X, Lin L, Xu Y, Li X. The relationship between personality traits, sleep status and anxiety in high school students. J Jining Med Univ. 2021;44:411–4.

    Google Scholar 

  56. Wang C, Lin J, Gan N. Clinical diagnosis and personality of children admitted for school refusal. Chin Mental Health J. 2002;16:411–3.

    Google Scholar 

  57. Chockalingam M, Skinner K, Melvin G, Yap MBH. Modifiable parent factors associated with child and adolescent school refusal: a systematic review. Child Psychiatry Hum Dev. 2023;54:1459–75.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhu L, Cheng L, Zhang M, Zheng M, Zhu N. Effectiveness of family therapy for adolescent school refusal. Chin J School Health. 2019;40:396–8.

    Google Scholar 

Download references

Acknowledgements

We sincerely thank all the subjects who participated in this study for their cooperation. The authors have declared that they have no competing or potential conflicts of interest.

Funding

This work was supported by the National Natural Science Foundation of China (82001802,82371551), the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (20200039), the doctoral foundation of Shanxi Medical University (BS201706), 136 Medical Rejuvenation Project of Shanxi Province (Y2022136008) and the special fund for Science and Technology Innovation Teams of Shanxi Province (202204051001027).

Author information

Author notes

  1. Gaizhi Li and Ying Niu should be considered joint first author.

Authors and Affiliations

  1. Department of Psychiatry, First Hospital of Shanxi Medical University, No 85 Jiefang Nan Road, Taiyuan, 030001, Shanxi Province, China

    Gaizhi Li, Xiumei Liang, Zhifen Liu & Ke-Rang Zhang

  2. First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi Province, China

    Gaizhi Li & Ke-Rang Zhang

  3. College of Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi Province, China

    Ying Niu

  4. Department of Psychiatry, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA

    Elissar Andari

Authors
  1. Gaizhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiumei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elissar Andari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhifen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ke-Rang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GL, ZL and KZ co-designed this study. GL and YN drafted the manuscript. GL, YN, XL and EA designed and modified the manuscript. GL and XL designed the statistical analysis. GL and YN contributed equally to this study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhifen Liu or Ke-Rang Zhang.

Ethics declarations

Ethics approval and consent to participate

This study is conducted in accordance with the latest version of the Declaration of Helsinki and is approved by the First Hospital of Shanxi Medical University Institutional Review Board, all the informed consent were obtained from the parents/caregivers of the participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, G., Niu, Y., Liang, X. et al. Psychological characteristics and emotional difficulties underlying school refusal in adolescents using functional near-infrared spectroscopy. BMC Psychiatry 23, 898 (2023). https://doi.org/10.1186/s12888-023-05291-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-023-05291-w

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us