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  • Perspective
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Neuroimaging insights into adolescent depression risk and development

Abstract

Adolescence is a period of increased risk for the onset of depression. However, the neurobiological mechanisms underlying this vulnerability remain poorly understood despite substantial investment and efforts in this research area. In this Perspective, we review existing literature on the links between the adolescent brain and depression risk and development, and discuss methodological and conceptual challenges related to quantifying brain features and measuring depression. We highlight the importance of considering both large-scale longitudinal cohort studies and smaller focused investigator-led studies with deep phenotyping to advance our understanding of the neural basis of depression in adolescence. Emphasizing the need to embrace the developmental context and refine our methodologies, we propose several considerations to help current and future researchers advance our understanding of how the developing brain influences depression risk in today’s youth.

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Fig. 1: Summary of the considerations presented in this Perspective.
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References

  1. Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry 27, 281–295 (2022).

    Article  PubMed  Google Scholar 

  2. Sawyer, S. M., Azzopardi, P. S., Wickremarathne, D. & Patton, G. C. The age of adolescence. Lancet Child Adolesc. Health 2, 223–228 (2018).

    Article  PubMed  Google Scholar 

  3. McGorry, P. D. et al. The Lancet Psychiatry Commission on youth mental health. Lancet Psychiatry 11, 731–774 (2024).

    Article  PubMed  Google Scholar 

  4. Arnett, J. J. Emerging Adulthood: The Winding Road from the Late Teens Through the Twenties (Oxford Univ. Press, 2014).

  5. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1789–1858 (2018).

    Article  Google Scholar 

  6. Thapar, A., Eyre, O., Patel, V. & Brent, D. Depression in young people. Lancet 400, 617–631 (2022).

    Article  PubMed  Google Scholar 

  7. Keyes, K. M. & Platt, J. M. Annual research review: sex, gender, and internalizing conditions among adolescents in the 21st century—trends, causes, consequences. J. Child Psychol. Psychiatry 65, 384–407 (2023).

    Article  PubMed  Google Scholar 

  8. Ho, T. C. Predicting depression risk in adolescents from multimodal data: current evidence and future directions. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 346–348 (2022).

    PubMed  Google Scholar 

  9. Tervo-Clemmens, B., Marek, S. & Barch, D. M. Tailoring psychiatric neuroimaging to translational goals. JAMA Psychiatry 80, 765–766 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Schmaal, L. The search for clinically useful neuroimaging markers of depression—a worthwhile pursuit or a futile quest? JAMA Psychiatry 79, 845–846 (2022).

    Article  PubMed  Google Scholar 

  11. Gratton, C., Nelson, S. M. & Gordon, E. M. Brain–behavior correlations: two paths toward reliability. Neuron 110, 1446–1449 (2022).

    Article  PubMed  Google Scholar 

  12. Dhamala, E., Yeo, B. T. T. & Holmes, A. J. One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry. Biol. Psychiatry 93, 717–728 (2023).

    Article  PubMed  Google Scholar 

  13. 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 

  14. Mascarell Maričić, L. et al. The IMAGEN study: a decade of imaging genetics in adolescents. Mol. Psychiatry 25, 2648–2671 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bethlehem, Ra. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Edde, M., Leroux, G., Altena, E. & Chanraud, S. Functional brain connectivity changes across the human life span: from fetal development to old age. J. Neurosci. Res. 99, 236–262 (2021).

    Article  PubMed  Google Scholar 

  17. Blakemore, S. J. Imaging brain development: the adolescent brain. Neuroimage 61, 397–406 (2012).

    Article  PubMed  Google Scholar 

  18. Paus, T. How environment and genes shape the adolescent brain. Horm. Behav. 64, 195–202 (2013).

    Article  PubMed  Google Scholar 

  19. Tooley, U. A., Bassett, D. S. & Mackey, A. P. Environmental influences on the pace of brain development. Nat. Rev. Neurosci. 22, 372–384 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).

    Article  PubMed  Google Scholar 

  21. Schmaal, L. et al. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing. Transl. Psychiatry 10, 172–172 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shen, X. et al. Brain structural associations with depression in a large early adolescent sample (the ABCD study®). eClinicalMedicine 42, 101204 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Opel, N. et al. Mediation of the influence of childhood maltreatment on depression relapse by cortical structure: a 2-year longitudinal observational study. Lancet Psychiatry 6, 318–326 (2019).

    Article  PubMed  Google Scholar 

  24. Skranes, J. et al. Cortical surface area and IQ in very-low-birth-weight (VLBW) young adults. Cortex 49, 2264–2271 (2013).

    Article  PubMed  Google Scholar 

  25. Ho, T. C. et al. Subcortical shape alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder Working Group. Hum. Brain Mapp. 43, 341–351 (2022).

    Article  PubMed  Google Scholar 

  26. Schmaal, L. et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 21, 806–812 (2016).

    Article  PubMed  Google Scholar 

  27. Weissman, D. G. et al. Reduced hippocampal and amygdala volume as a mechanism underlying stress sensitization to depression following childhood trauma. Depress. Anxiety 37, 916–925 (2020).

    Article  PubMed  Google Scholar 

  28. Rao, U. et al. Hippocampal changes associated with early-life adversity and vulnerability to depression. Biol. Psychiatry 67, 357–364 (2010).

    Article  PubMed  Google Scholar 

  29. Hubachek, S. et al. Hippocampal subregion volume in high-risk offspring is associated with increases in depressive symptoms across the transition to adolescence. J. Affect. Disord. 281, 358–366 (2021).

    Article  PubMed  Google Scholar 

  30. Hurtado, H. et al. Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents. J. Affect. Disord. 344, 619–627 (2024).

    Article  PubMed  Google Scholar 

  31. Toenders, Y. J. et al. Neuroimaging predictors of onset and course of depression in childhood and adolescence: a systematic review of longitudinal studies. Dev. Cogn. Neurosci. 39, 100700 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nelson, B. D., Perlman, G., Klein, D. N., Kotov, R. & Hajcak, G. Blunted neural response to rewards as a prospective predictor of the development of depression in adolescent girls. Am. J. Psychiatry 173, 1223–1230 (2016).

    Article  PubMed  Google Scholar 

  33. Stringaris, A. et al. The brain’s response to reward anticipation and depression in adolescence: dimensionality, specificity, and longitudinal predictions in a community-based sample. Am. J. Psychiatry 172, 1215–1223 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Foland‐Ross, L. C. et al. Cortical thickness predicts the first onset of major depression in adolescence. Int. J. Dev. Neurosci. 46, 125–131 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Vulser, H. et al. Early variations in white matter microstructure and depression outcome in adolescents with subthreshold depression. Am. J. Psychiatry 175, 1255–1264 (2018).

    Article  PubMed  Google Scholar 

  36. Luby, J. L., Barch, D., Whalen, D., Tillman, R. & Belden, A. Association between early life adversity and risk for poor emotional and physical health in adolescence: a putative mechanistic neurodevelopmental pathway. JAMA Pediatr. 171, 1168 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Papmeyer, M. et al. Cortical thickness in individuals at high familial risk of mood disorders as they develop major depressive disorder. Biol. Psychiatry 78, 58–66 (2015).

    Article  PubMed  Google Scholar 

  38. Little, K. et al. Association between serotonin transporter genotype, brain structure and adolescent-onset major depressive disorder: a longitudinal prospective study. Transl. Psychiatry 4, e445 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Luby, J. L. et al. Developmental trajectories of the orbitofrontal cortex and anhedonia in middle childhood and risk for substance use in adolescence in a longitudinal sample of depressed and healthy preschoolers. Am. J. Psychiatry 175, 1010–1021 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Toenders, Y. J. et al. Predicting depression onset in young people based on clinical, cognitive, environmental, and neurobiological data. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 376–384 (2022).

    PubMed  Google Scholar 

  41. Zhi, D. et al. Triple interactions between the environment, brain, and behavior in children: an ABCD study. Biol. Psychiatry 95, 828–838 (2024).

    Article  PubMed  Google Scholar 

  42. Yoon, L. et al. Frontolimbic network topology associated with risk and presence of depression in adolescents: a study using a composite risk score in Brazil. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 8, 426–435 (2023).

    PubMed  Google Scholar 

  43. Jin, J. et al. Structural connectivity between rostral anterior cingulate cortex and amygdala predicts first onset of depressive disorders in adolescence. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 249–255 (2022).

    PubMed  Google Scholar 

  44. Tse, N. Y. et al. A mega-analysis of functional connectivity and network abnormalities in youth depression. Nat. Ment. Health 2, 1169–1182 (2024).

    Article  Google Scholar 

  45. Shen, X. et al. A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank. Nat. Commun. 11, 2301 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Bos, M. G. N., Peters, S., van de Kamp, F. C., Crone, E. A. & Tamnes, C. K. Emerging depression in adolescence coincides with accelerated frontal cortical thinning. J. Child Psychol. Psychiatry 59, 994–1002 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Albaugh, M. D. et al. Age-related volumetric change of limbic structures and subclinical anxious/depressed symptomatology in typically developing children and adolescents. Biol. Psychol. 124, 133–140 (2017).

    Article  PubMed  Google Scholar 

  48. Schmaal, L. et al. Brain structural signatures of adolescent depressive symptom trajectories: a longitudinal magnetic resonance imaging study. J. Am. Acad. Child Adolesc. Psychiatry 56, 593–601.e9 (2017).

    Article  PubMed  Google Scholar 

  49. Strikwerda-Brown, C. et al. Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence. Soc. Cogn. Affect. Neurosci. 10, 961–968 (2015).

    Article  PubMed  Google Scholar 

  50. Davey, C. G. et al. Functional brain-imaging correlates of negative affectivity and the onset of first-episode depression. Psychol. Med. 45, 1001–1009 (2015).

    Article  PubMed  Google Scholar 

  51. Jalbrzikowski, M. et al. Development of white matter microstructure and intrinsic functional connectivity between the amygdala and ventromedial prefrontal cortex: associations with anxiety and depression. Biol. Psychiatry 82, 511–521 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 263, 119636 (2022).

    Article  PubMed  Google Scholar 

  53. Winter, N. R. et al. Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities. JAMA Psychiatry 79, 879 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Uhlhaas, P. J. et al. Towards a youth mental health paradigm: a perspective and roadmap. Mol. Psychiatry 28, 3171–3181 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Rolle, C. E. et al. Cortical connectivity moderators of antidepressant vs placebo treatment response in major depressive disorder: secondary analysis of a randomized clinical trial. JAMA Psychiatry 77, 397 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Cattarinussi, G., Delvecchio, G., Maggioni, E., Bressi, C. & Brambilla, P. Ultra-high field imaging in major depressive disorder: a review of structural and functional studies. J. Affect. Disord. 290, 65–73 (2021).

    Article  PubMed  Google Scholar 

  57. Makowski, C. et al. Leveraging the Adolescent Brain Cognitive Development study to improve behavioral prediction from neuroimaging in smaller replication samples. Cereb. Cortex 34, bhae223 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Hubbard, N. A. et al. The Human Connectome Project of adolescent anxiety and depression dataset. Sci. Data 11, 837 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Auerbach, R. P. et al. Reward-related neural circuitry in depressed and anxious adolescents: a human connectome project. J. Am. Acad. Child Adolesc. Psychiatry 61, 308–320 (2022).

    Article  PubMed  Google Scholar 

  61. Fan, S., Wang, Y., Wang, Y. & Zang, Y. Revisiting resting-state functional connectivity of the amygdala and subgenual anterior cingulate cortex in depressed adolescents and adults. Biol. Psychiatry Cogn. Neurosci. Neuroimaging https://doi.org/10.1016/j.bpsc.2024.11.004 (2024).

  62. Wolfers, T. et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry 75, 1146 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Mills, K. L. et al. Inter-individual variability in structural brain development from late childhood to young adulthood. Neuroimage 242, 118450 (2021).

    Article  PubMed  Google Scholar 

  64. Tamnes, C. K., Bos, M. G. N., van de Kamp, F. C., Peters, S. & Crone, E. A. Longitudinal development of hippocampal subregions from childhood to adulthood. Dev. Cogn. Neurosci. 30, 212–222 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. McGrath, J. J. et al. Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries. Lancet Psychiatry 10, 668–681 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Marquand, A. F., Rezek, I., Buitelaar, J. & Beckmann, C. F. Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol. Psychiatry 80, 552–561 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711–1734 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Sun, X. et al. Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome. Biol. Psychiatry 94, 936–947 (2023).

    Article  PubMed  Google Scholar 

  69. Lv, J. et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol. Psychiatry 26, 3512–3523 (2021).

    Article  PubMed  Google Scholar 

  70. Wolfers, T. et al. Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder. Hum. Brain Mapp. 42, 2546–2555 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Segal, A. et al. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat. Neurosci. 26, 1613–1629 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Carrera, E. & Tononi, G. Diaschisis: past, present, future. Brain 137, 2408–2422 (2014).

    Article  PubMed  Google Scholar 

  73. Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159–172 (2015).

    Article  PubMed  Google Scholar 

  74. Maciejewski, D. F. et al. Most fare well—but some do not: distinct profiles of mood variability development and their association with adjustment during adolescence. Dev. Psychol. 55, 434–448 (2019).

    Article  PubMed  Google Scholar 

  75. Kjelkenes, R. et al. Deviations from normative brain white and gray matter structure are associated with psychopathology in youth. Dev. Cogn. Neurosci. 58, 101173 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Mulder, J. D. & Hamaker, E. L. Three extensions of the Random Intercept Cross-Lagged Panel Model. Struct. Equ. Modeling 28, 638–648 (2021).

    Article  Google Scholar 

  77. Westlin, C. et al. Improving the study of brain–behavior relationships by revisiting basic assumptions. Trends Cogn. Sci. 27, 246–257 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Tiego, J. et al. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. Nat. Ment. Health 1, 304–315 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association, 2013).

  80. International Classification of Diseases 11th Revision (World Health Organization, 2022); https://icdcdn.who.int/icd11referenceguide/en/html/index.html

  81. Kotov, R. et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126, 454–477 (2017).

    Article  PubMed  Google Scholar 

  82. Insel, T. et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    Article  PubMed  Google Scholar 

  83. Borsboom, D. A network theory of mental disorders. World Psychiatry 16, 5–13 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Eaton, N. R. et al. A review of approaches and models in psychopathology conceptualization research. Nat. Rev. Psychol. 2, 622–636 (2023).

    Article  Google Scholar 

  85. Kessler, R. C. et al. Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys. Epidemiol. Psychiatr. Sci. 24, 210–226 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Simms, L. J. et al. Development of measures for the Hierarchical Taxonomy of Psychopathology (HiTOP): a collaborative scale development project. Assessment 29, 3–16 (2022).

    Article  PubMed  Google Scholar 

  87. Levin‐Aspenson, H. F. & Greene, A. L. Rethinking trauma‐related psychopathology in the Hierarchical Taxonomy of Psychopathology (HiTOP). J. Trauma. Stress 37, 361–371 (2024).

    Article  PubMed  Google Scholar 

  88. Ringwald, W. R., Forbes, M. K. & Wright, A. G. C. Meta-analysis of structural evidence for the Hierarchical Taxonomy of Psychopathology (HiTOP) model. Psychol. Med. 53, 533–546 (2023).

    PubMed  Google Scholar 

  89. Tackett, J. L. & Hallquist, M. The need to grow: developmental considerations and challenges for modern psychiatric taxonomies. J. Psychopathol. Clin. Sci. 131, 660–663 (2022).

    Article  PubMed  Google Scholar 

  90. Forbes, M. K. et al. A hierarchical model of the symptom-level structure of psychopathology in youth. Clin. Psychol. Sci. 13, 278–300 (2024).

    Article  Google Scholar 

  91. Waszczuk, M. A. et al. The prognostic utility of personality traits versus past psychiatric diagnoses: predicting future mental health and functioning. Clin. Psychol. Sci. 10, 734–751 (2022).

    Article  PubMed  Google Scholar 

  92. Sunderland, M. & Slade, T. The relationship between internalizing psychopathology and suicidality, treatment seeking, and disability in the Australian population. J. Affect. Disord. 171, 6–12 (2015).

    Article  PubMed  Google Scholar 

  93. Kim, H. et al. Internalizing psychopathology and all‐cause mortality: a comparison of transdiagnostic vs. diagnosis‐based risk prediction. World Psychiatry 20, 276–282 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Levin-Aspenson, H. F., Watson, D., Clark, L. A. & Zimmerman, M. What Is the general factor of psychopathology? Consistency of the p factor across samples. Assessment 28, 1035–1049 (2021).

    Article  PubMed  Google Scholar 

  95. Fried, E. I. Studying mental health problems as systems, not syndromes. Curr. Dir. Psychol. Sci. 31, 500–508 (2022).

    Article  Google Scholar 

  96. Scheffer, M. et al. A dynamical systems view of psychiatric disorders—theory: a review. JAMA Psychiatry 81, 618–623 (2024).

    Article  PubMed  Google Scholar 

  97. Borsboom, D., Haslbeck, J. M. B. & Robinaugh, D. J. Systems‐based approaches to mental disorders are the only game in town. World Psychiatry 21, 420–422 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Weavers, B. et al. The antecedents and outcomes of persistent and remitting adolescent depressive symptom trajectories: a longitudinal, population-based English study. Lancet Psychiatry 8, 1053–1061 (2021).

    Article  PubMed  Google Scholar 

  99. Kuppens, P. It’s about time: a special section on affect dynamics. Emot. Rev. 7, 297–300 (2015).

    Article  Google Scholar 

  100. Fried, E. I., Proppert, R. K. K. & Rieble, C. L. Building an early warning system for depression: rationale, objectives, and methods of the WARN-D study. Clin. Psychol. Eur. 5, e10075 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Schoevers, R. A. et al. Affect fluctuations examined with ecological momentary assessment in patients with current or remitted depression and anxiety disorders. Psychol. Med. 51, 1906–1915 (2021).

    Article  PubMed  Google Scholar 

  102. Silk, J. S. et al. Daily emotional dynamics in depressed youth: a cell phone ecological momentary assessment study. J. Exp. Child Psychol. 110, 241–257 (2011).

    Article  PubMed  Google Scholar 

  103. Cousins, J. C. et al. The bidirectional association between daytime affect and nighttime sleep in youth with anxiety and depression. J. Pediatr. Psychol. 36, 969–979 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Walsh, R. J. et al. Social contexts, momentary mood and affective variability in early adolescence: an exploratory ecological momentary assessment study. J. Early Adolesc. 44, 59–95 (2024).

    Article  Google Scholar 

  105. Henderson, C., Robinson, E., Evans-Lacko, S. & Thornicroft, G. Relationships between anti-stigma programme awareness, disclosure comfort and intended help-seeking regarding a mental health problem. Br. J. Psychiatry 211, 316–322 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Sampogna, G. et al. The impact of social marketing campaigns on reducing mental health stigma: results from the 2009–2014 Time to Change programme. Eur. Psychiatry 40, 116–122 (2017).

    Article  PubMed  Google Scholar 

  107. Collishaw, S. Annual research review: secular trends in child and adolescent mental health. J. Child Psychol. Psychiatry 56, 370–393 (2015).

    Article  PubMed  Google Scholar 

  108. Orben, A., Meier, A., Dalgleish, T. & Blakemore, S.-J. Mechanisms linking social media use to adolescent mental health vulnerability. Nat. Rev. Psychol. 3, 407–423 (2024).

    Article  Google Scholar 

  109. Högberg, B. Educational stressors and secular trends in school stress and mental health problems in adolescents. Soc. Sci. Med. 270, 113616 (2021).

    Article  PubMed  Google Scholar 

  110. Foulkes, L. & Andrews, J. L. Are mental health awareness efforts contributing to the rise in reported mental health problems? A call to test the prevalence inflation hypothesis. New Ideas Psychol. 69, 101010 (2023).

    Article  Google Scholar 

  111. Foulkes, L. What Mental Illness Really Is … (and What It Isn’t) (Vintage, 2022).

  112. McCashin, D. & Murphy, C. M. Using TikTok for public and youth mental health—a systematic review and content analysis. Clin. Child Psychol. Psychiatry 28, 279–306 (2023).

    Article  PubMed  Google Scholar 

  113. Driven into the Darkness: How TikTok’s ‘For You’ Feed Encourages Self-Harm and Suicidal Ideation (Amnesty International, 2021); https://www.amnesty.org/en/documents/POL40/7350/2023/en/

  114. MacSweeney, N., Bowman, S. & Kelly, C. More than just characters in a story: effective and meaningful involvement of young people in mental health research. J. Public Ment. Health 18, 14–16 (2019).

    Article  Google Scholar 

  115. Toenders, Y. J. et al. From developmental neuroscience to policy: a novel framework based on participatory research. Dev. Cogn. Neurosci. 67, 101398 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Crone, E. A. & Achterberg, M. Prosocial development in adolescence. Curr. Opin. Psychol. 44, 220–225 (2022).

    Article  PubMed  Google Scholar 

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Acknowledgments

N.M. and C.K.T. are supported by the South-Eastern Norway Regional Health Authority (number 2023012). Y.J.T. is supported by Convergence | Healthy Start, a program of the Convergence Alliance—Delft University of Technology, Erasmus University Rotterdam and Erasmus Medical Center. C.K.T. is supported by the South-Eastern Norway Regional Health Authority (number 2021070 and number 500189) and the Research Council of Norway (number 288083 and number 323951).

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N.M.: conceptualization; writing—original draft, writing—review and editing; visualization, project administration. Y.J.T.: conceptualization; writing—original draft, writing—review and editing. C.K.T.: conceptualization; writing—review and editing; funding acquisition.

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Correspondence to Niamh MacSweeney.

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MacSweeney, N., Toenders, Y.J. & Tamnes, C.K. Neuroimaging insights into adolescent depression risk and development. Nat. Mental Health 3, 772–779 (2025). https://doi.org/10.1038/s44220-025-00453-z

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