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The transition trajectories of self-injurious thoughts and behaviours among children from a biopsychosocial perspective

Nature Mental Health volume 1, pages 782–791 (2023)Cite this article

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Abstract

Although self-injurious thoughts and behaviours (SITB) among children pose an imminent public health concern, the comprehensive understanding of SITB transitions remains unclear. Here we used the longitudinal data of 7,270 children from the Adolescent Brain and Cognitive DevelopmentSM Study (ABCD Study). We found that SITB transitions are linked to altered cortical areas of the dorsal lateral prefrontal cortex and the posterior cingulate cortex and altered functional connectivity between the default mode and attention networks. Additionally, high behaviour inhibition and general psychopathology (that is, p-factor) were identified as risk factors for SITB transitions, while the presence of robust family support and school support served as protective factors. Our study extends prior cross-sectional investigations by elucidating the temporal precedence of specific biopsychosocial factors, underscoring their potential predictive significance in SITB occurrence. Early identification of these factors holds great promise for targeted prevention, addressing the pressing public health concerns associated with SITB.

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Fig. 1
Fig. 2: Brain structure associated with SIT and SIB transition.
Fig. 3: Functional connectivity associated with SIT and SIB transition.

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Data availability

Data are from the ABCD Study. Access to ABCD Study data is restricted to protect participants’ privacy. Users must create an account through the National Institute of Mental Health (NIMH) Data Archive, and they may then complete the necessary steps to gain access (https://data-archive.nimh.nih.gov/abcd). Processed data from this study (including the MRI and rsfMRI) have been uploaded to the NIMH Data Archive (NDA). Information on how to access ABCD data through the NDA is available on the ABCD Study data sharing webpage: https://abcdstudy.org/scientists_data_sharing.html. The ABCD data used in this report came from ABCD release 4.0 (https://doi.org/10.15154/1523041). All variables included in the current study are listed and described in Supplementary Table 1.

Code availability

Scripts that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Knipe, D., Padmanathan, P., Newton-Howes, G., Chan, L. F. & Kapur, N. Suicide and self-harm. Lancet 399, 1903–1916 (2022).

    Article  PubMed  Google Scholar 

  2. Liu, R. T., Walsh, R. F., Sheehan, A. E., Cheek, S. M. & Sanzari, C. M. Prevalence and correlates of suicide and nonsuicidal self-injury in children: a systematic review and meta-analysis. JAMA Psychiatry 79, 718–726 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Turecki, G. & Brent, D. A. Suicide and suicidal behaviour. Lancet 387, 1227–1239 (2016).

    Article  PubMed  Google Scholar 

  4. van Heeringen, K. & Mann, J. J. The neurobiology of suicide. Lancet Psychiatry 1, 63–72 (2014).

    Article  PubMed  Google Scholar 

  5. Kuehn, K. S. A meta-analysis on the affect regulation function of real-time self-injurious thoughts and behaviours. Nat. Hum. Behav. 6, 964–974 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Aleman, A. & Denys, D. Mental health: a road map for suicide research and prevention. Nature 509, 421–423 (2014).

    Article  PubMed  Google Scholar 

  7. Castellví, P. et al. Longitudinal association between self-injurious thoughts and behaviors and suicidal behavior in adolescents and young adults: a systematic review with meta-analysis. J. Affect. Disord. 215, 37–48 (2017).

    Article  PubMed  Google Scholar 

  8. Turecki, G. et al. Suicide and suicide risk. Nat. Rev. Dis. Primers 5, 74 (2019).

    Article  PubMed  Google Scholar 

  9. Nock, M. K., Wedig, M. M., Janis, I. B. & Deliberto, T. L. Self-injurious thoughts and behaviors. in A Guide To Assessments That Work (eds Hunsley, J. & Mash, E. J.) 158–179 (Oxford University Press, 2008).

  10. Schmaal, L. et al. Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies. Mol. Psychiatry 25, 408–427 (2020).

    Article  PubMed  Google Scholar 

  11. Brañas, M. J. et al. Neuroimaging studies of nonsuicidal self-injury in youth: a systematic review. Life 11, 729 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ordaz, S. J., Goyer, M. S., Ho, T. C., Singh, M. K. & Gotlib, I. H. Network basis of suicidal ideation in depressed adolescents. J. Affect. Disord. 226, 92–99 (2018).

    Article  PubMed  Google Scholar 

  13. Auerbach, R. P., Pagliaccio, D., Allison, G. O., Alqueza, K. L. & Alonso, M. F. Neural correlates associated with suicide and nonsuicidal self-injury in youth. Biol. Psychiatry 89, 119–133 (2021).

    Article  PubMed  Google Scholar 

  14. Fatt, C. R. C. et al. Dysfunction of default mode network is associated with active suicidal ideation in youths and young adults with depression: findings from the T-RAD study. J. Psychiatric Res. 142, 258–262 (2021).

    Article  Google Scholar 

  15. Kirshenbaum, J. S. et al. Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics. J. Child Psychol. Psychiatry 63, 701–714 (2022).

    Article  PubMed  Google Scholar 

  16. Gray, J. A. Perspectives on anxiety and impulsivity: a commentary. J. Res. Pers. 21, 493–509 (1987).

    Article  Google Scholar 

  17. Cipriano, A., Cella, S. & Cotrufo, P. Nonsuicidal self-injury: a systematic review. Front. Psychol. 8, 1946 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Klonsky, E. D. & May, A. M. The Three-Step Theory (3ST): a new theory of suicide rooted in the ‘ideation-to-action’ framework. Int. J.Cogn. Ther. 8, 114–129 (2015).

    Article  Google Scholar 

  19. Bryan, C. J., Kyron, M. & Page, A. C. BIS sensitivity, BAS sensitivity, and recent suicide attempts. Pers. Individ. Differ. 191, 111552 (2022).

    Article  Google Scholar 

  20. O’Connor, R. C. & Forgan, G. Suicidal thinking and perfectionism: the role of goal adjustment and behavioral inhibition/activation systems (BIS/BAS). J. Ration. Emot. Cogn. Behav. Ther. 25, 321–341 (2007).

    Article  Google Scholar 

  21. Ammerman, B. A., Kleiman, E. M., Jenkins, A. L., Berman, M. E. & McCloskey, M. S. Using propensity scores to examine the association between behavioral inhibition/activation and nonsuicidal and suicidal self-injury. Crisis 38, 227–236 (2016).

    Article  PubMed  Google Scholar 

  22. Franklin, J. C. et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143, 187 (2017).

    Article  PubMed  Google Scholar 

  23. Sevincok, D., Ozbay, H. C., Ozbek, M. M., Tunagur, M. T. & Aksu, H. ADHD symptoms in relation to internalizing and externalizing symptoms in children: the mediating role of sluggish cognitive tempo. Nord. J. Psychiatry 74, 265–272 (2020).

    Article  PubMed  Google Scholar 

  24. Hurtig, T., Taanila, A., Moilanen, I., Nordström, T. & Ebeling, H. Suicidal and self-harm behaviour associated with adolescent attention deficit hyperactivity disorder—a study in the Northern Finland Birth Cohort 1986. Nord. J. Psychiatry 66, 320–328 (2012).

    Article  PubMed  Google Scholar 

  25. Caspi, A. et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin. Psychol. Sci. 2, 119–137 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Patalay, P. et al. A general psychopathology factor in early adolescence. Br. J. Psychiatry 207, 15–22 (2015).

    Article  PubMed  Google Scholar 

  27. Cui, Z. et al. Linking individual differences in personalized functional network topography to psychopathology in youth. Biol. Psychiatry 92, 973–983 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hoertel, N. et al. Mental disorders and risk of suicide attempt: a national prospective study. Mol. Psychiatry 20, 718–726 (2015).

    Article  PubMed  Google Scholar 

  29. Ruan-Iu, L. et al. Identifying youth at risk for suicidal thoughts and behaviors using the ‘p’ factor in primary care: an exploratory study. Arch. Suicide Res. 27, 1047–1062 (2023).

    Article  PubMed  Google Scholar 

  30. Moore, T. M. et al. Criterion validity and relationships between alternative hierarchical dimensional models of general and specific psychopathology. J. Abnorm. Psychol. 129, 677 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Miller, A. B., Esposito-Smythers, C. & Leichtweis, R. N. Role of social support in adolescent suicidal ideation and suicide attempts. J. Adolesc. Health 56, 286–292 (2015).

    Article  PubMed  Google Scholar 

  32. Kleiman, E. M. & Liu, R. T. Social support as a protective factor in suicide: findings from two nationally representative samples. J. Affect. Dis. 150, 540–545 (2013).

    Article  PubMed  Google Scholar 

  33. Yuan, Z. et al. Your support is my healing: the impact of perceived social support on adolescent NSSI—a sequential mediation analysis. Curr. Psychol. https://doi.org/10.1007/s12144-023-04286-w (2023).

  34. Rodríguez-Otero, J. E., Campos-Mouriño, X., Meilán-Fernández, D., Pintos-Bailón, S. & Cabo-Escribano, G. Where is the social in the biopsychosocial model of suicide prevention? Int. J. Soc. Psychiatry 68, 1403–1410 (2022).

    Article  PubMed  Google Scholar 

  35. Zhang, R. et al. Dorsolateral and ventrolateral prefrontal cortex structural changes relative to suicidal ideation in patients with depression. Acta Neuropsychiatrica 32, 84–91 (2020).

    Article  PubMed  Google Scholar 

  36. Spinella, M. Neurobehavioral correlates of impulsivity: evidence of prefrontal involvement. Int. J. Neurosci. 114, 95–104 (2004).

    Article  PubMed  Google Scholar 

  37. Cassels, M., Neufeld, S., van Harmelen, A. L., Goodyer, I. & Wilkinson, P. Prospective pathways from impulsivity to non-suicidal self-injury among youth. Arch. Suicide Res. 26, 534–547 (2022).

    Article  PubMed  Google Scholar 

  38. Fuchs, P. N., Peng, Y. B., Boyette-Davis, J. A. & Uhelski, M. L. The anterior cingulate cortex and pain processing. Front. Integr. Neurosci. 8, 35 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Franklin, J. C., Hessel, E. T. & Prinstein, M. J. Clarifying the role of pain tolerance in suicidal capability. Psychiatry Res. 189, 362–367 (2011).

    Article  PubMed  Google Scholar 

  40. Casey, B., Jones, R. M. & Somerville, L. H. Braking and accelerating of the adolescent brain. J. Res. Adolesc. 21, 21–33 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kelly, A. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X. & Milham, M. P. Competition between functional brain networks mediates behavioral variability. NeuroImage 39, 527–537 (2008).

    Article  PubMed  Google Scholar 

  42. Pu, W. et al. Alterations of brain functional architecture associated with psychopathic traits in male adolescents with conduct disorder. Sci. Rep. 7, 1–11 (2017).

    Article  Google Scholar 

  43. Hu, M.-L. et al. A review of the functional and anatomical default mode network in schizophrenia. Neurosci. Bull. 33, 73–84 (2017).

    Article  PubMed  Google Scholar 

  44. Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl Acad. Sci. USA 103, 10046–10051 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wainberg, M., Jacobs, G. R., Voineskos, A. N. & Tripathy, S. J. Neurobiological, familial and genetic risk factors for dimensional psychopathology in the Adolescent Brain Cognitive Development study. Mol. Psychiatry 27, 2731–2741 (2022).

    Article  PubMed  Google Scholar 

  46. Scholten, M. R. M., van Honk, J., Aleman, A. & Kahn, R. S. Behavioral inhibition system (BIS), behavioral activation system (BAS) and schizophrenia: relationship with psychopathology and physiology. J. Psychiatric Res. 40, 638–645 (2006).

    Article  Google Scholar 

  47. Czyz, E. K., Glenn, C. R., Busby, D. & King, C. A. Daily patterns in nonsuicidal self-injury and coping among recently hospitalized youth at risk for suicide. Psychiatry Res. 281, 112588 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Klonsky, E. D., May, A. M. & Saffer, B. Y. Suicide, suicide attempts, and suicidal ideation. Annu. Rev. Clin. Psychol. 12, 307–330 (2016).

    Article  PubMed  Google Scholar 

  49. Baiden, P., Stewart, S. L. & Fallon, B. The role of adverse childhood experiences as determinants of non-suicidal self-injury among children and adolescents referred to community and inpatient mental health settings. Child Abuse Neglect 69, 163–176 (2017).

    Article  PubMed  Google Scholar 

  50. Wan, Y. et al. Associations of adverse childhood experiences and social support with self-injurious behaviour and suicidality in adolescents. Br. J. Psychiatry 214, 146–152 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Harrell, F. E. Regression modeling strategies. Bios 330, 14 (2017).

    Google Scholar 

  52. Kuo, Y.-F., Raji, M. A. & Goodwin, J. S. Association of disability with mortality from opioid overdose among US Medicare adults. JAMA Netw. Open 2, e1915638–e1915638 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bujang, M. A., Sa’at, N., Bakar, T. M. I. T. A. & Joo, L. C. Sample size guidelines for logistic regression from observational studies with large population: emphasis on the accuracy between statistics and parameters based on real life clinical data. Malays. J. Med. Sci. 25, 122 (2018).

    PubMed  PubMed Central  Google Scholar 

  54. Lin, M., Lucas, H. C. Jr. & Shmueli, G. Research commentary—too big to fail: large samples and the p-value problem. Inf. Syst. Res. 24, 906–917 (2013).

    Article  Google Scholar 

  55. Jafari, M. & Ansari-Pour, N. Why, when and how to adjust your P values? Cell J. 20, 604 (2019).

    PubMed  Google Scholar 

  56. Kapur, N., Cooper, J., O’Connor, R. C. & Hawton, K. Non-suicidal self-injury v. attempted suicide: new diagnosis or false dichotomy? Br. J. Psychiatry 202, 326–328 (2013).

    Article  PubMed  Google Scholar 

  57. Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kaufman, J. et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J. Am. Acad. Child Adolesc. Psychiatry 36, 980–988 (1997).

    Article  PubMed  Google Scholar 

  59. Hagler, D. J. Jr. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 202, 116091 (2019).

    Article  PubMed  Google Scholar 

  60. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15 (2010).

    Article  PubMed  Google Scholar 

  61. Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebr. Cortex 26, 288–303 (2016).

    Article  Google Scholar 

  62. Pagliaccio, D. et al. Revising the BIS/BAS scale to study development: measurement invariance and normative effects of age and sex from childhood through adulthood. Psychol. Assess. 28, 429 (2016).

    Article  PubMed  Google Scholar 

  63. Barch, D. M. et al. Demographic and mental health assessments in the adolescent brain and cognitive development study: updates and age-related trajectories. Dev. Cogn. Neurosci. 52, 101031 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Carver, C. S. & White, T. L. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS scales. J. Pers. Soc. Psychol. 67, 319 (1994).

    Article  Google Scholar 

  65. Achenbach, T. M. & Ruffle, T. M. The Child Behavior Checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatr. Rev. 21, 265–271 (2000).

    Article  PubMed  Google Scholar 

  66. Durham, E. L. et al. Association of gray matter volumes with general and specific dimensions of psychopathology in children. Neuropsychopharmacology 46, 1333–1339 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Van Dijk, M. T., Murphy, E., Posner, J. E., Talati, A. & Weissman, M. M. Association of multigenerational family history of depression with lifetime depressive and other psychiatric disorders in children: results from the adolescent brain cognitive development (ABCD) study. JAMA Psychiatry 78, 778–787 (2021).

    Article  PubMed  Google Scholar 

  68. DeVille, D. C. et al. Prevalence and family-related factors associated with suicidal ideation, suicide attempts, and self-injury in children aged 9 to 10 years. JAMA Netw. Open 3, e1920956 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Schaefer, E. S. Children’s reports of parental behavior: an inventory. Child Dev. 36, 413–424 (1965).

    Article  PubMed  Google Scholar 

  70. Arthur, M. W. et al. Measuring risk and protection in communities using the Communities That Care Youth Survey. Eval. Program Plan. 30, 197–211 (2007).

    Article  Google Scholar 

  71. Zucker, R. A. et al. Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: rationale, description of measures, and early data. Dev. Cogn. Neurosci. 32, 107–120 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Chen, P. J. et al. Parental education and youth suicidal behaviours: a systematic review and meta-analysis. Epidemiol. Psychiatric Sci. 31, e19 (2022).

    Article  Google Scholar 

  73. Ridley, M., Rao, G., Schilbach, F. & Patel, V. Poverty, depression, and anxiety: causal evidence and mechanisms. Science 370, eaay0214 (2020).

    Article  PubMed  Google Scholar 

  74. Ponnet, K. et al. Suicidal behaviour in adolescents: associations with parental marital status and perceived parent–adolescent relationship. J. Affect. Disord. 89, 107–113 (2005).

    Article  PubMed  Google Scholar 

  75. Dennis, E., Manza, P. & Volkow, N. D. Socioeconomic status, BMI, and brain development in children. Transl. Psychiatry 12, 33 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  76. DeJoseph, M. L., Herzberg, M. P., Sifre, R. D., Berry, D. & Thomas, K. M. Measurement matters: an individual differences examination of family socioeconomic factors, latent dimensions of children’s experiences, and resting state functional brain connectivity in the ABCD sample. Dev. Cogn. Neurosci. 53, 101043 (2022).

    Article  PubMed  Google Scholar 

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Acknowledgements

D.Q. was supported by the Shuimu Tsinghua Scholar Program of Tsinghua University. We thank the ABCD participants and their families for their time and dedication to this project. Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org) and are held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA0401048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD repository grows and changes over time. The ABCD data used in this report came from ABCD release 4.0 (https://doi.org/10.15154/1523041). DOIs can be found at https://nda.nih.gov/.

Author information

Author notes

  1. These authors contributed equally: Xue Wen, Diyang Qu.

Authors and Affiliations

  1. Vanke School of Public Health, Tsinghua University, Beijing, China

    Xue Wen, Diyang Qu, Xuan Zhang, Yinzhe Wang & Runsen Chen

  2. Institute for Healthy China, Tsinghua University, Beijing, China

    Xue Wen, Diyang Qu & Runsen Chen

  3. Chinese Institute for Brain Research, Beijing, China

    Zaixu Cui

  4. Wulituo Hospital of Shijingshan District, Beijing, China

    Xiaoqian Zhang

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Contributions

R.C., Xiaoqian Zhang, Z.C., X.W. and D.Q. designed the study. X.W. and D.Q. analysed the data. X.W., D.Q., R.C., Xiaoqian Zhang and Z.C. interpreted data. X.W. and D.Q. wrote the first draft of the manuscript. X.W. made the tables and figures. R.C., D.Q., Xiaoqian Zhang, Y.W., Xuan Zhang and Z.C. revised the manuscript critically. All authors contributed feedback and approved the final manuscript.

Corresponding authors

Correspondence to Zaixu Cui, Xiaoqian Zhang or Runsen Chen.

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Nature Mental Health thanks Daiane Borges Machado and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Extended Data

Extended Data Fig. 1 Psychosocial characteristics of SIT and SIB transition group.

a–c: Psychological characteristics (NControl = 6743, NSIT = 282, NSIB = 241) measured by Behavioral Inhibition/Activation Scales (BIS/BAS), including behavioral inhibition (BIS, motivation to avoid aversive outcomes), reward responsiveness (that is, sensitivity to reward), and drive (that is, intensity of goal-directed behavior). d–g: Psychopathological characteristics (NControl = 6747, NSIT = 282, NSIB = 241), including general psychopathology (that is, p-factor) and dimensional psychopathology (that is, conduct problems, internalizing, and ADHD). h, i: Social characteristics (NControl = 6732, NSIT = 282, NSIB = 240), including family support and school support. Center lines in the boxplots represent median values, box limits represent the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers. Abbreviations: Control = no-transition control group; SIT = self-injurious thoughts transition group; SIB = self-injurious behaviors transition group.

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Figs. 1–3.

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Wen, X., Qu, D., Zhang, X. et al. The transition trajectories of self-injurious thoughts and behaviours among children from a biopsychosocial perspective. Nat. Mental Health 1, 782–791 (2023). https://doi.org/10.1038/s44220-023-00130-z

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