Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

A review of approaches and models in psychopathology conceptualization research

Nature Reviews Psychology volume 2, pages 622–636 (2023)Cite this article

Subjects

Abstract

Mental disorder classification provides a definitional framework that underlies applied clinical and research efforts to understand, assess, predict, prevent and ameliorate the burden of psychopathology. Many classification frameworks exist, perhaps most notable being the ‘authoritative’ systems of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and the 11th revision of the International Classification of Diseases. However, numerous limitations of official classification systems have been identified, fostering the development of empirically derived, statistical and psychometric alternative classification approaches, which attempt to overcome those limitations. In this Review, we describe three such advances: transdiagnostic dimensional approaches (such as the Hierarchical Taxonomy of Psychopathology; HiTOP), network approaches and clinical staging approaches. We discuss their strengths, limitations, divergence, overlap, and scientific and clinical utility, with a focus on the potential synthesis and integration of disparate approaches towards better classification of mental disorders.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Links between factors in dimensional models.
Fig. 2: Group-level and person-specific network models.
Fig. 3: A depiction of clinical staging approaches.

Similar content being viewed by others

References

  1. Diagnostic And Statistical Manual Of Mental Disorders 5th edn (American Psychiatric Association, 2013).

  2. International Classification Of Diseases For Mortality And Morbidity Statistics 11th revn (World Health Organization, 2018).

  3. Eaton, N. R., South, S. C. & Krueger, R. F. in Contemporary Directions In Psychopathology: Scientific Foundations Of DSM-V And ICD-11 (eds Millon, T., Krueger, R. & Simonsen, E.) 223–241 (Guilford, 2010).

  4. Borsboom, D., Cramer, A. O., Schmittmann, V. D., Epskamp, S. & Waldorp, L. J. The small world of psychopathology. PLoS One 6, e27407 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fried, E. I. & Nesse, R. M. Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. J. Affect. Disord. 172, 96–102 (2015).

    Article  PubMed  Google Scholar 

  6. Krueger, R. F. & Eaton, N. R. Personality traits and the classification of mental disorders: toward a more complete integration in DSM-5 and an empirical model of psychopathology. Pers. Disord. 1, 97–118 (2010).

    Article  Google Scholar 

  7. Galatzer-Levy, I. R. & Bryant, R. A. 636,120 ways to have posttraumatic stress disorder. Perspect. Psychol. Sci. 8, 651–662 (2013).

    Article  PubMed  Google Scholar 

  8. Vize, C. E., Ringwald, W. R., Edershile, E. A. & Wright, A. G. C. Antagonism in daily life: an exploratory ecological momentary assessment study. Clin. Psychol. Sci. 10, 90–108 (2022).

    Article  PubMed  Google Scholar 

  9. Krueger, R. F. et al. Progress in achieving quantitative classification of psychopathology. World Psychiat. 17, 282–293 (2018).

    Article  Google Scholar 

  10. Regier, D. A. et al. DSM-5 field trials in the United States and Canada. Part II: Test–retest reliability of selected categorical diagnoses. Am. J. Psychiat. 170, 59–70 (2013).

    Article  PubMed  Google Scholar 

  11. Kim, W., Woo, Y. S., Chae, J.-H. & Bahk, W.-M. The diagnostic stability of DSM-IV diagnoses: an examination of major depressive disorder, bipolar I disorder, and schizophrenia in Korean patients. Clin. Psychopharmacol. Neurosci. 9, 117–121 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kozak, M. J. & Cuthbert, B. N. The NIMH research domain criteria initiative: background, issues, and pragmatics. Psychophysiology 53, 286–297 (2016).

    Article  PubMed  Google Scholar 

  13. Marquand, A. F., Wolfers, T., Mennes, M., Buitelaar, J. & Beckmann, C. F. Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol. Psychiat. Cogn. Neurosci. Neuroimaging 1, 433–447 (2016).

    Google Scholar 

  14. Cuthbert, B. N. Research domain criteria: toward future psychiatric nosologies. Dial. Clin. Neurosci. 17, 89–97 (2015).

    Article  Google Scholar 

  15. Hyman, S. E. Can neuroscience be integrated into the DSM-V? Nat. Rev. Neurosci. 8, 725–732 (2007).

    Article  PubMed  Google Scholar 

  16. Shackman, A. J. & Fox, A. S. Getting serious about variation: lessons for clinical neuroscience (a commentary on ‘the myth of optimality in clinical neuroscience’). Trends Cogn. Sci. 22, 368–369 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  18. Cuthbert, B. N. & Insel, T. R. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 11, 126 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hofmann, S. G. & Hayes, S. C. The future of intervention science: process-based therapy. Clin. Psychol. Sci. 7, 37–50 (2019).

    Article  PubMed  Google Scholar 

  20. Lilienfeld, S. O. & Treadway, M. T. Clashing diagnostic approaches: DSM–ICD versus RDoC. Annu. Rev. Clin. Psychol. 12, 435–463 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. McGorry, P. D., Hickie, I. B., Yung, A. R., Pantelis, C. & Jackson, H. J. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust. NZ J. Psychiat. 40, 616–622 (2006).

    Article  Google Scholar 

  22. Sharp, C. & Wall, K. DSM-5 level of personality functioning: refocusing personality disorder on what it means to be human. Annu. Rev. Clin. Psychol. 17, 313–337 (2021).

    Article  PubMed  Google Scholar 

  23. Sauer-Zavala, S. et al. Current definitions of ‘transdiagnostic’ in treatment development: a search for consensus. Behav. Ther. 48, 128–138 (2017).

    Article  PubMed  Google Scholar 

  24. Eaton, N. R., Rodriguez-Seijas, C., Carragher, N. & Krueger, R. F. Transdiagnostic factors of psychopathology and substance use disorders: a review. Soc. Psychiat. Psychiatr. Epidemiol. 50, 171–182 (2015).

    Article  Google Scholar 

  25. Eaton, N. R. et al. The structure and predictive validity of the internalizing disorders. J. Abnorm. Psychol. 122, 86–92 (2013).

    Article  PubMed  Google Scholar 

  26. Haslam, N., Holland, E. & Kuppens, P. Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychol. Med. 42, 903–920 (2012).

    Article  PubMed  Google Scholar 

  27. Lahey, B. B., Krueger, R. F., Rathouz, P. J., Waldman, I. D. & Zald, D. H. A hierarchical causal taxonomy of psychopathology across the life span. Psychol. Bull. 143, 142–186 (2017).

    Article  PubMed  Google Scholar 

  28. Ruscio, A. M. Normal versus pathological mood: implications for diagnosis. Annu. Rev. Clin. Psychol. 15, 179–205 (2019).

    Article  PubMed  Google Scholar 

  29. Pincus, H. A., Davis, W. W. & McQueen, L. E. ‘Subthreshold’ mental disorders: a review and synthesis of studies on minor depression and other ‘brand names’. Br. J. Psychiat. 174, 288–296 (1999).

    Article  Google Scholar 

  30. 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 

  31. Krueger, R. F. & Eaton, N. R. Structural Validity And The Classification Of Mental Disorders (Oxford Univ. Press, 2012).

  32. Forbes, M. K. et al. Three recommendations based on a comparison of the reliability and validity of the predominant models used in research on the empirical structure of psychopathology. J. Abnorm. Psychol. 130, 297–317 (2021).

    Article  PubMed  Google Scholar 

  33. Greene, A. L. et al. Are fit indices used to test psychopathology structure biased? A simulation study. J. Abnorm. Psychol. 128, 740–764 (2019).

    Article  PubMed  Google Scholar 

  34. Greene, A. L. et al. Misbegotten methodologies and forgotten lessons from Tom Swift’s electric factor analysis machine: a demonstration with competing structural models of psychopathology. Psychol. Meth. https://doi.org/10.1037/met0000465 (2022).

    Article  Google Scholar 

  35. Goldberg, L. R. An alternative ‘description of personality’: the big-five factor structure. J. Pers. Soc. Psychol. 59, 1216–1229 (1990).

    Article  PubMed  Google Scholar 

  36. Costa, P. T. Jr & McCrae, R. R. Four ways five factors are basic. Pers. Individ. Differ. 13, 653–665 (1992).

    Article  Google Scholar 

  37. Digman, J. M. Personality structure: emergence of the five-factor model. Annu. Rev. Psychol. 41, 417–440 (1990).

    Article  Google Scholar 

  38. John, O. P., Naumann, L. P. & Soto, C. J. in Handbook Of Personality: Theory And Research (eds Robins, R. W., John, O. P. & Pervin, L. A.) 114–158 (Guilford, 2008).

  39. Krueger, R. F. & Markon, K. E. The role of the DSM-5 personality trait model in moving toward a quantitative and empirically based approach to classifying personality and psychopathology. Annu. Rev. Clin. Psychol. 10, 477–501 (2014).

    Article  PubMed  Google Scholar 

  40. Markon, K. E., Krueger, R. F. & Watson, D. Delineating the structure of normal and abnormal personality: an integrative hierarchical approach. J. Pers. Soc. Psychol. 88, 139–157 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Suzuki, T., Samuel, D. B., Pahlen, S. & Krueger, R. F. DSM-5 alternative personality disorder model traits as maladaptive extreme variants of the five-factor model: an item-response theory analysis. J. Abnorm. Psychol. 124, 343–354 (2015).

    Article  PubMed  Google Scholar 

  42. Chmielewski, M., Bagby, R. M., Markon, K., Ring, A. J. & Ryder, A. G. Openness to experience, intellect, schizotypal personality disorder, and psychoticism: resolving the controversy. J. Pers. Disord. 28, 483–499 (2014).

    Article  PubMed  Google Scholar 

  43. Krueger, R. F. & Hobbs, K. A. An overview of the DSM-5 alternative model of personality disorders. Psychopathology 53, 126–132 (2020).

    Article  PubMed  Google Scholar 

  44. Krueger, R. F., Derringer, J., Markon, K. E., Watson, D. & Skodol, A. E. Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychol. Med. 42, 1879–1890 (2012).

    Article  PubMed  Google Scholar 

  45. Kotov, R. et al. The hierarchical taxonomy of psychopathology (HiTOP): a quantitative nosology based on consensus of evidence. Annu. Rev. Clin. Psychol. 17, 83–108 (2021).

    Article  PubMed  Google Scholar 

  46. Kotov, R. et al. Validity and utility of hierarchical taxonomy of psychopathology (HiTOP): I. Psychosis superspectrum. World Psychiat. 19, 151–172 (2020).

    Article  Google Scholar 

  47. Waszczuk, M. A. et al. Redefining phenotypes to advance psychiatric genetics: implications from hierarchical taxonomy of psychopathology. J. Abnorm. Psychol. 129, 143–161 (2020).

    Article  PubMed  Google Scholar 

  48. 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 

  49. Lahey, B. B. et al. Is there a general factor of prevalent psychopathology during adulthood? J. Abnorm. Psychol. 121, 971–977 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 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 

  51. Caspi, A. & Moffitt, T. E. All for one and one for all: mental disorders in one dimension. Am. J. Psychiat. 175, 831–844 (2018).

    Article  PubMed  Google Scholar 

  52. Fried, E. I., Greene, A. L. & Eaton, N. R. The p factor is the sum of its parts, for now. World Psychiat. 20, 69–70 (2021).

    Article  Google Scholar 

  53. Smith, G. T., Atkinson, E. A., Davis, H. A., Riley, E. N. & Oltmanns, J. R. The general factor of psychopathology. Annu. Rev. Clin. Psychol. 16, 75–98 (2020).

    Article  PubMed  Google Scholar 

  54. Achenbach, T. M. & Verhulst, F. Achenbach System Of Empirically Based Assessment (ASEBA) (Burlington, 2010).

  55. Harkness, A. R., McNulty, J. L. & Ben-Porath, Y. S. The personality psychopathology five (PSY-5): constructs and MMPI-2 scales. Psychol. Assess. 7, 104–114 (1995).

    Article  Google Scholar 

  56. Widiger, T. A. et al. Personality in a hierarchical model of psychopathology. Clin. Psychol. Sci. 7, 77–92 (2019).

    Article  Google Scholar 

  57. Brandes, C. M. & Tackett, J. L. Contextualizing neuroticism in the hierarchical taxonomy of psychopathology. J. Res. Pers. 81, 238–245 (2019).

    Article  Google Scholar 

  58. Lynam, D. R. & Miller, J. D. The basic trait of antagonism: an unfortunately underappreciated construct. J. Res. Pers. 81, 118–126 (2019).

    Article  Google Scholar 

  59. Mullins-Sweatt, S. N., DeShong, H. L., Lengel, G. J., Helle, A. C. & Krueger, R. F. Disinhibition as a unifying construct in understanding how personality dispositions undergird psychopathology. J. Res. Pers. 80, 55–61 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Watson, D., Stanton, K., Khoo, S., Ellickson-Larew, S. & Stasik-O’Brien, S. M. Extraversion and psychopathology: a multilevel hierarchical review. J. Res. Pers. 81, 1–10 (2019).

    Article  Google Scholar 

  61. Widiger, T. A. & Crego, C. HiTOP thought disorder, DSM-5 psychoticism, and five factor model openness. J. Res. Pers. 80, 72–77 (2019).

    Article  Google Scholar 

  62. Kessler, R. C. et al. Development of lifetime comorbidity in the World Health Organization world mental health surveys. Arch. Gen. Psychiat. 68, 90–100 (2011).

    Article  PubMed  Google Scholar 

  63. Conway, C. C. et al. A hierarchical taxonomy of psychopathology can transform mental health research. Perspect. Psychol. Sci. 14, 419–436 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 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 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Naragon-Gainey, K. & Watson, D. The anxiety disorders and suicidal ideation: accounting for co-morbidity via underlying personality traits. Psychol. Med. 41, 1437–1447 (2011).

    Article  PubMed  Google Scholar 

  66. 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 

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

    Article  Google Scholar 

  68. Forbush, K. T. et al. A new approach to eating‐disorder classification: using empirical methods to delineate diagnostic dimensions and inform care. Int. J. Eat. Disord. 51, 710–721 (2018).

    Article  PubMed  Google Scholar 

  69. Watts, A. L., Lane, S. P., Bonifay, W., Steinley, D. & Meyer, F. A. Building theories on top of, and not independent of, statistical models: the case of the p-factor. Psychol. Inq. 31, 310–320 (2020).

    Article  PubMed  Google Scholar 

  70. 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 

  71. Watts, A. L., Poore, H. E. & Waldman, I. D. Riskier tests of the validity of the bifactor model of psychopathology. Clin. Psychol. Sci. 7, 1285–1303 (2019).

    Article  Google Scholar 

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

    Article  Google Scholar 

  73. Robinaugh, D. J., Hoekstra, R. H., Toner, E. R. & Borsboom, D. The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research. Psychol. Med. 50, 353–366 (2020).

    Article  PubMed  Google Scholar 

  74. Wichers, M., Wigman, J. & Myin-Germeys, I. Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory. Emot. Rev. 7, 362–367 (2015).

    Article  Google Scholar 

  75. Borsboom, D. in Philosophical Issues In Psychiatry IV: Psychiatric Nosology (ed. Kendler, K. S.) 80–97 (Oxford Univ. Press, 2017).

  76. Borsboom, D. et al. Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs. Psychol. Med. 46, 1567–1579 (2016).

    Article  PubMed  Google Scholar 

  77. Cramer, A. O., Waldorp, L. J., Van Der Maas, H. L. & Borsboom, D. Comorbidity: a network perspective. Behav. Brain Sci. 33, 137–150 (2010).

    Article  PubMed  Google Scholar 

  78. Beltz, A. M., Wright, A. G., Sprague, B. N. & Molenaar, P. C. Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment 23, 447–458 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Wright, A. G., Beltz, A. M., Gates, K. M., Molenaar, P. & Simms, L. J. Examining the dynamic structure of daily internalizing and externalizing behavior at multiple levels of analysis. Front. Psychol. 6, 1914 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Bulteel, K., Tuerlinckx, F., Brose, A. & Ceulemans, E. Improved insight into and prediction of network dynamics by combining VAR and dimension reduction. Multivar. Behav. Res. 53, 853–875 (2018).

    Article  Google Scholar 

  81. Wright, A. G. & Woods, W. C. Personalized models of psychopathology. Annu. Rev. Clin. Psychol. 16, 49–74 (2020).

    Article  PubMed  Google Scholar 

  82. Olthof, M. et al. Critical fluctuations as an early-warning signal for sudden gains and losses in patients receiving psychotherapy for mood disorders. Clin. Psychol. Sci. 8, 25–35 (2020).

    Article  Google Scholar 

  83. Helmich, M. A. et al. Early warning signals and critical transitions in psychopathology: challenges and recommendations. Curr. Opin. Psychol. 41, 51–58 (2021).

    Article  PubMed  Google Scholar 

  84. Wichers, M., Smit, A. C. & Snippe, E. Early warning signals based on momentary affect dynamics can expose nearby transitions in depression: a confirmatory single-subject time-series study. J. Pers. Oriented Res. 6, 1–15 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Wichers, M., Groot, P. C., Psychosystems, E. & Group, E. Critical slowing down as a personalized early warning signal for depression. Psychother. Psychosom. 85, 114–116 (2016).

    Article  PubMed  Google Scholar 

  86. van de Leemput, I. A. et al. Critical slowing down as early warning for the onset and termination of depression. Proc. Natl Acad. Sci.USA 111, 87–92 (2014).

    Article  PubMed  Google Scholar 

  87. Helmich, M. A. et al. Detecting impending symptom transitions using early-warning signals in individuals receiving treatment for depression. Clin. Psychol. Sci. https://doi.org/10.1177/21677026221137006 (2021).

    Article  Google Scholar 

  88. Schreuder, M., Wigman, J., Smit, A., Hartman, C. & Wichers, M. Anticipating transitions in mental health in at-risk youth: a large-scale diary study into early warning signals. Eur. Psychiat. 64, S455–S455 (2021).

    Article  Google Scholar 

  89. Robinaugh, D. J., Haslbeck, J. M., Ryan, O., Fried, E. I. & Waldorp, L. J. Invisible hands and fine calipers: a call to use formal theory as a toolkit for theory construction. Perspect. Psychol. Sci. 16, 725–743 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Borsboom, D., van der Maas, H. L. J., Dalege, J., Kievit, R. A. & Haig, B. D. Theory construction methodology: a practical framework for building theories in psychology. Perspect. Psychol. Sci. 16, 756–766 (2021).

    Article  PubMed  Google Scholar 

  91. Fried, E. I. Lack of theory building and testing impedes progress in the factor and network literature. Psychol. Inq. 31, 271–288 (2020).

    Article  Google Scholar 

  92. Bringmann, L. F. & Eronen, M. I. Don’t blame the model: reconsidering the network approach to psychopathology. Psychol. Rev. 125, 606–615 (2018).

    Article  PubMed  Google Scholar 

  93. Bringmann, L. F. et al. What do centrality measures measure in psychological networks? J. Abnorm. Psychol. 128, 892–903 (2019).

    Article  PubMed  Google Scholar 

  94. Pe, M. L. et al. Emotion-network density in major depressive disorder. Clin. Psychol. Sci. 3, 292–300 (2015).

    Article  Google Scholar 

  95. Wigman, J. T., de Vos, S., Wichers, M., van Os, J. & Bartels-Velthuis, A. A. A transdiagnostic network approach to psychosis. Schizophr. Bull. 43, 122–132 (2017).

    Article  PubMed  Google Scholar 

  96. van Borkulo, C. et al. Association of symptom network structure with the course of depression. JAMA Psychiat. 72, 1219–1226 (2015).

    Article  Google Scholar 

  97. Schweren, L., Van Borkulo, C. D., Fried, E. & Goodyer, I. M. Assessment of symptom network density as a prognostic marker of treatment response in adolescent depression. JAMA Psychiat. 75, 98–100 (2018).

    Article  Google Scholar 

  98. De Vos, S. et al. An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks. PLoS One 12, e0178586 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Lutz, W. et al. Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study. Sci. Rep. 8, 7819 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Boschloo, L., van Borkulo, C. D., Borsboom, D. & Schoevers, R. A. A prospective study on how symptoms in a network predict the onset of depression. Psychother. Psychosom. 85, 183–184 (2016).

    Article  PubMed  Google Scholar 

  101. Groen, R. N. et al. Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks. BMC Med. 18, 308 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Rodebaugh, T. L. et al. Does centrality in a cross-sectional network suggest intervention targets for social anxiety disorder? J. Consult. Clin. Psychol. 86, 831–844 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Spiller, T. R. et al. On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology. BMC Med. 18, 297 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Hallquist, M. N., Wright, A. G. & Molenaar, P. C. Problems with centrality measures in psychopathology symptom networks: why network psychometrics cannot escape psychometric theory. Multivar. Behav. Res. 56, 199–223 (2019).

    Article  Google Scholar 

  105. Dablander, F. & Hinne, M. Node centrality measures are a poor substitute for causal inference. Sci. Rep. 9, 6846 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  106. DeYoung, C. G. & Krueger, R. F. To wish impossible things: on the ontological status of latent variables and the prospects for theory in psychology. Psychol. Inq. 31, 289–296 (2020).

    Article  Google Scholar 

  107. Granger, C. W. Investigating causal relations by econometric models and cross-spectral methods. J. Econom. Soc. 37, 424–438 (1969).

    Google Scholar 

  108. Bringmann, L. F. et al. Assessing temporal emotion dynamics using networks. Assessment 23, 425–435 (2016).

    Article  PubMed  Google Scholar 

  109. Forbes, M. K., Wright, A. G., Markon, K. E. & Krueger, R. F. Evidence that psychopathology symptom networks have limited replicability. J. Abnorm. Psychol. 126, 969–988 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Forbes, M. K., Wright, A. G., Markon, K. E. & Krueger, R. F. Quantifying the reliability and replicability of psychopathology network characteristics. Multivar. Behav. Res. 56, 224–242 (2019).

    Article  Google Scholar 

  111. Haslbeck, J., Ryan, O., Robinaugh, D. J., Waldorp, L. J. & Borsboom, D. Modeling psychopathology: from data models to formal theories. Psychol. Meth. 27, 930–957 (2021).

    Google Scholar 

  112. Epskamp, S., Borsboom, D. & Fried, E. I. Estimating psychological networks and their accuracy: a tutorial paper. Behav. Res. Meth. 50, 195–212 (2018).

    Article  Google Scholar 

  113. Forbes, M. K., Wright, A. G., Markon, K. E. & Krueger, R. F. On unreplicable inferences in psychopathology symptom networks and the importance of unreliable parameter estimates. Multivar. Behav. Res. 56, 368–376 (2021).

    Article  Google Scholar 

  114. Borsboom, D. et al. False alarm? A comprehensive reanalysis of “Evidence that psychopathology symptom networks have limited replicability” by Forbes, Wright, Markon, and Krueger (2017). J. Abnorm. Psychol. 126, 989–999 (2017).

    Article  PubMed  Google Scholar 

  115. Borsboom, D., Robinaugh, D. J., Group, T. P., Rhemtulla, M. & Cramer, A. O. Robustness and replicability of psychopathology networks. World Psychiat. 17, 143–144 (2018).

    Article  Google Scholar 

  116. Fried, E. I. et al. Replicability and generalizability of posttraumatic stress disorder (PTSD) networks: a cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clin. Psychol. Sci. 6, 335–351 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Fried, E. I., van Borkulo, C. D. & Epskamp, S. On the importance of estimating parameter uncertainty in network psychometrics: a response to Forbes et al. (2019). Multivar. Behav. Res. 56, 243–248 (2020).

    Article  Google Scholar 

  118. Lin, S.-Y., Fried, E. I. & Eaton, N. R. The association of life stress with substance use symptoms: a network analysis and replication. J. Abnorm. Psychol. 129, 204–214 (2020).

    Article  PubMed  Google Scholar 

  119. Epskamp, S. & Fried, E. I. A tutorial on regularized partial correlation networks. Psychol. Meth. 23, 617–634 (2018).

    Article  Google Scholar 

  120. Bringmann, L. F. Person-specific networks in psychopathology: past, present and future. Curr. Opin. Psychol. 41, 59–64 (2021).

    Article  PubMed  Google Scholar 

  121. McGorry, P. D. & Hickie, I. B. Clinical Staging In Psychiatry: Making Diagnosis Work For Research And Treatment (Cambridge Univ. Press, 2019).

  122. McGorry, P. et al. Biomarkers and clinical staging in psychiatry. World Psychiat. 13, 211–223 (2014).

    Article  Google Scholar 

  123. Iorfino, F. et al. Clinical stage transitions in persons aged 12 to 25 years presenting to early intervention mental health services with anxiety, mood, and psychotic disorders. JAMA Psychiat. 76, 1167–1175 (2019).

    Article  Google Scholar 

  124. Filia, K. et al. Clinical and functional characteristics of a subsample of young people presenting for primary mental healthcare at headspace services across Australia. Soc. Psychiat. Psychiatr. Epidemiol. 56, 1311–1323 (2021).

    Article  Google Scholar 

  125. Purcell, R. et al. Demographic and clinical characteristics of young people seeking help at youth mental health services: baseline findings of the Transitions Study. Early Interv. Psychiat. 9, 487–497 (2015).

    Article  Google Scholar 

  126. Hickie, I. B. et al. Applying clinical staging to young people who present for mental health care. Early Interv. Psychiat. 7, 31–43 (2013).

    Article  Google Scholar 

  127. Romanowska, S. et al. Social and role functioning in youth at risk of serious mental illness. Early Interv. Psychiat. 14, 463–469 (2020).

    Article  Google Scholar 

  128. Cross, S. P., Hermens, D. F. & Hickie, I. B. Treatment patterns and short‐term outcomes in an early intervention youth mental health service. Early Interv. Psychiat. 10, 88–97 (2016).

    Article  Google Scholar 

  129. Nogovitsyn, N. et al. Aberrant limbic brain structures in young individuals at risk for mental illness. Psychiat. Clin. Neurosci. 74, 294–302 (2020).

    Article  Google Scholar 

  130. Scott, E. M. et al. Dysregulated sleep–wake cycles in young people are associated with emerging stages of major mental disorders. Early Interv. Psychiat. 10, 63–70 (2016).

    Article  Google Scholar 

  131. Stowkowy, J. et al. Sleep disturbances in youth at‐risk for serious mental illness. Early Interv. Psychiat. 14, 373–378 (2020).

    Article  Google Scholar 

  132. Romanowska, S. et al. Neurocognitive deficits in a transdiagnostic clinical staging model. Psychiat. Res. 270, 1137–1142 (2018).

    Article  Google Scholar 

  133. McGorry, P. D. & Mei, C. Clinical staging for youth mental disorders: progress in reforming diagnosis and clinical care. Annu. Rev. Dev. Psychol. 3, 15–39 (2021).

    Article  Google Scholar 

  134. Sacks, D. D. et al. White matter integrity according to the stage of mental disorder in youth. Psychiat. Res. Neuroimag. 307, 111218 (2021).

    Article  Google Scholar 

  135. Hermens, D. F. et al. Neuropsychological profile according to the clinical stage of young persons presenting for mental health care. BMC Psychol. 1, 8 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Eggins, P. S., Hatton, S. N., Hermens, D. F., Hickie, I. B. & Lagopoulos, J. Subcortical volumetric differences between clinical stages of young people with affective and psychotic disorders. Psychiat. Res. Neuroimag. 271, 8–16 (2018).

    Article  Google Scholar 

  137. Lagopoulos, J. et al. Microstructural white matter changes are correlated with the stage of psychiatric illness. Transl. Psychiat. 3, e248 (2013).

    Article  Google Scholar 

  138. Lagopoulos, J., Hermens, D. F., Naismith, S. L., Scott, E. M. & Hickie, I. B. Frontal lobe changes occur early in the course of affective disorders in young people. BMC Psychiat. 12, 4 (2012).

    Article  Google Scholar 

  139. Naismith, S. L. et al. Circadian profiles in young people during the early stages of affective disorder. Transl. Psychiat. 2, e123 (2012).

    Article  Google Scholar 

  140. Shah, J. L. et al. Transdiagnostic clinical staging in youth mental health: a first international consensus statement. World Psychiat. 19, 233–242 (2020).

    Article  Google Scholar 

  141. Cross, S. P., Scott, J. & Hickie, I. B. Predicting early transition from sub-syndromal presentations to major mental disorders. BJPsych Open 3, 223–227 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Carpenter, J. S. et al. Combining clinical stage and pathophysiological mechanisms to understand illness trajectories in young people with emerging mood and psychotic syndromes. Med. J. Aust. 211, S12–S22 (2019).

    Google Scholar 

  143. Hartmann, J. A. et al. Broad clinical high‐risk mental state (CHARMS): methodology of a cohort study validating criteria for pluripotent risk. Early Interv. Psychiat. 13, 379–386 (2019).

    Article  Google Scholar 

  144. Hartmann, J. A. et al. Pluripotential risk and clinical staging: theoretical considerations and preliminary data from a transdiagnostic risk identification approach. Front. Psychiat. 11, 553578 (2021).

    Article  Google Scholar 

  145. Caspi, A. et al. Longitudinal assessment of mental health disorders and comorbidities across 4 decades among participants in the Dunedin birth cohort study. JAMA Netw. Open 3, e203221 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  146. McGorry, P. & Nelson, B. Why we need a transdiagnostic staging approach to emerging psychopathology, early diagnosis, and treatment. JAMA Psychiat. 73, 191–192 (2016).

    Article  Google Scholar 

  147. Scott, J. et al. Prevalence of self‐reported subthreshold phenotypes of major mental disorders and their association with functional impairment, treatment and full‐threshold syndromes in a community‐residing cohort of young adults. Early Interv. Psychiat. 15, 306–313 (2021).

    Article  Google Scholar 

  148. Lahey, B. B., Zald, D. H., Hakes, J. K., Krueger, R. F. & Rathouz, P. J. Patterns of heterotypic continuity associated with the cross-sectional correlational structure of prevalent mental disorders in adults. JAMA Psychiat. 71, 989–996 (2014).

    Article  Google Scholar 

  149. Plana-Ripoll, O. et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiat. 76, 259–270 (2019).

    Article  Google Scholar 

  150. Chanen, A. M., Berk, M. & Thompson, K. Integrating early intervention for borderline personality disorder and mood disorders. Harv. Rev. Psychiat. 24, 330–341 (2016).

    Article  Google Scholar 

  151. Hartmann, J. A., Nelson, B., Ratheesh, A., Treen, D. & McGorry, P. D. At-risk studies and clinical antecedents of psychosis, bipolar disorder and depression: a scoping review in the context of clinical staging. Psychol. Med. 49, 177–189 (2019).

    Article  PubMed  Google Scholar 

  152. McGorry, P. & Van Os, J. Redeeming diagnosis in psychiatry: timing versus specificity. Lancet 381, 343–345 (2013).

    Article  PubMed  Google Scholar 

  153. Kendler, K. S. Classification of psychopathology: conceptual and historical background. World Psychiat. 17, 241–242 (2018).

    Article  Google Scholar 

  154. Leibenluft, E. Categories and dimensions, brain and behavior: the yins and yangs of psychopathology. JAMA Psychiat. 71, 15–17 (2014).

    Article  Google Scholar 

  155. Boffa, R. J., Constanti, M., Floyd, C. N. & Wierzbicki, A. S. Hypertension in adults: summary of updated NICE guidance. Br. Med. J 367, l5310 (2019).

    Article  Google Scholar 

  156. Jablensky, A. Psychiatric classifications: validity and utility. World Psychiat. 15, 26–31 (2016).

    Article  Google Scholar 

  157. Hamaker, E. L. Why Researchers Should Think “Within-Person”: A Paradigmatic Rationale 43–61 (Guilford, 2012).

  158. Fisher, A. J. Toward a dynamic model of psychological assessment: implications for personalized care. J. Consult. Clin. Psychol. 83, 825–836 (2015).

    Article  PubMed  Google Scholar 

  159. Fisher, A. J. & Boswell, J. F. Enhancing the personalization of psychotherapy with dynamic assessment and modeling. Assessment 23, 496–506 (2016).

    Article  PubMed  Google Scholar 

  160. Molenaar, P. C. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement 2, 201–218 (2004).

    Google Scholar 

  161. Piccirillo, M. L. & Rodebaugh, T. L. Foundations of idiographic methods in psychology and applications for psychotherapy. Clin. Psychol. Rev. 71, 90–100 (2019).

    Article  PubMed  Google Scholar 

  162. Bolger, N., Davis, A. & Rafaeli, E. Diary methods: capturing life as it is lived. Annu. Rev. Psychol. 54, 579–616 (2003).

    Article  PubMed  Google Scholar 

  163. McNeish, D. & Hamaker, E. L. A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychol. Methods 25, 610–635 (2020).

    Article  PubMed  Google Scholar 

  164. Bringmann, L., Lemmens, L., Huibers, M., Borsboom, D. & Tuerlinckx, F. Revealing the dynamic network structure of the beck depression inventory-II. Psychol. Med. 45, 747–757 (2015).

    Article  PubMed  Google Scholar 

  165. Snippe, E., Doornbos, B., Schoevers, R. A., Wardenaar, K. J. & Wichers, M. Individual and common patterns in the order of symptom improvement during outpatient treatment for major depression. J. Affect. Disord. 290, 81–88 (2021).

    Article  PubMed  Google Scholar 

  166. Beck, E. D. & Jackson, J. J. in Measuring And Modeling Persons And Situations 465–497 (Elsevier, 2021).

  167. Trull, T. J. & Ebner-Priemer, U. The role of ambulatory assessment in psychological science. Curr. Dir. Psychol. Sci. 23, 466–470 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  168. Trull, T. J. & Ebner-Priemer, U. W. Ambulatory assessment in psychopathology research: a review of recommended reporting guidelines and current practices. J. Abnorm. Psychol. 129, 56–63 (2020).

    Article  PubMed  Google Scholar 

  169. Koval, P. et al. Emotional inertia and external events: the roles of exposure, reactivity, and recovery. Emotion 15, 625–636 (2015).

    Article  PubMed  Google Scholar 

  170. Kuppens, P., Allen, N. B. & Sheeber, L. B. Emotional inertia and psychological maladjustment. Psychol. Sci. 21, 984–991 (2010).

    Article  PubMed  Google Scholar 

  171. Kuppens, P. et al. Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion 12, 283–289 (2012).

    Article  PubMed  Google Scholar 

  172. Ebner-Priemer, U. W., Eid, M., Kleindienst, N., Stabenow, S. & Trull, T. J. Analytic strategies for understanding affective (in)stability and other dynamic processes in psychopathology. J. Abnorm. Psychol. 118, 195–202 (2009).

    Article  PubMed  Google Scholar 

  173. Myin‐Germeys, I. et al. Experience sampling methodology in mental health research: new insights and technical developments. World Psychiat. 17, 123–132 (2018).

    Article  Google Scholar 

  174. Sperry, S. H., Barrantes-Vidal, N. & Kwapil, T. R. The association of affective temperaments and bipolar spectrum psychopathology: an experience sampling study. Motiv. Emot. 42, 126–136 (2018).

    Article  Google Scholar 

  175. Trull, T. J., Lane, S. P., Koval, P. & Ebner-Priemer, U. W. Affective dynamics in psychopathology. Emot. Rev. 7, 355–361 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  176. Yang, X. et al. Socioemotional dynamics of emotion regulation and depressive symptoms: a person-specific network approach. Innov. Aging 2, 15–16 (2018).

    Article  PubMed Central  Google Scholar 

  177. Elmer, T., Geschwind, N., Peeters, F., Wichers, M. & Bringmann, L. Getting stuck in social isolation: solitude inertia and depressive symptoms. J. Abnorm. Psychol. 129, 713–723 (2020).

    Article  PubMed  Google Scholar 

  178. van Winkel, M. et al. Unraveling the role of loneliness in depression: the relationship between daily life experience and behavior. Psychiatry 80, 104–117 (2017).

    Article  PubMed  Google Scholar 

  179. Hong, R. Y. & Paunonen, S. V. Personality vulnerabilities to psychopathology: relations between trait structure and affective‐cognitive processes. J. Pers. 79, 527–562 (2011).

    Article  PubMed  Google Scholar 

  180. Myin-Germeys, I., Krabbendam, L., Jolles, J., Delespaul, P. A. & van Os, J. Are cognitive impairments associated with sensitivity to stress in schizophrenia? An experience sampling study. Am. J. Psychiat. 159, 443–449 (2002).

    Article  PubMed  Google Scholar 

  181. Nieman, D. H. et al. Protocol across study: longitudinal transdiagnostic cognitive functioning, psychiatric symptoms, and biological parameters in patients with a psychiatric disorder. BMC Psychiat. 20, 212 (2020).

    Article  Google Scholar 

  182. Sadikaj, G., Moskowitz, D. S., Russell, J. J., Zuroff, D. C. & Paris, J. Quarrelsome behavior in borderline personality disorder: influence of behavioral and affective reactivity to perceptions of others. J. Abnorm. Psychol. 122, 195–207 (2013).

    Article  PubMed  Google Scholar 

  183. Trull, T. J. Ambulatory assessment of borderline personality disorder. Psychopathology 51, 137–140 (2018).

    Article  PubMed  Google Scholar 

  184. Dejonckheere, E. et al. Complex affect dynamics add limited information to the prediction of psychological well-being. Nat. Hum. Behav. 3, 478–491 (2019).

    Article  PubMed  Google Scholar 

  185. Russell, J. J., Moskowitz, D. S., Zuroff, D. C., Sookman, D. & Paris, J. Stability and variability of affective experience and interpersonal behavior in borderline personality disorder. J. Abnorm. Psychol. 116, 578–588 (2007).

    Article  PubMed  Google Scholar 

  186. Keltner, D. & Kring, A. M. Emotion, social function, and psychopathology. Rev. Gen. Psychol. 2, 320–342 (1998).

    Article  Google Scholar 

  187. Cía, A. H. et al. Lifetime prevalence and age-of-onset of mental disorders in adults from the Argentinean study of mental health epidemiology. Soc. Psychiat. Psychiatr. Epidemiol. 53, 341–350 (2018).

    Article  Google Scholar 

  188. Forbes, M. K., Rapee, R. M. & Krueger, R. F. Opportunities for the prevention of mental disorders by reducing general psychopathology in early childhood. Behav. Res. Ther. 119, 103411 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  189. Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiat. 62, 593–602 (2005).

    Article  PubMed  Google Scholar 

  190. McElroy, E., Belsky, J., Carragher, N., Fearon, P. & Patalay, P. Developmental stability of general and specific factors of psychopathology from early childhood to adolescence: dynamic mutualism or p‐differentiation? J. Child. Psychol. Psychiat. 59, 667–675 (2018).

    Article  PubMed  Google Scholar 

  191. van Dijk, I., Krueger, R. F. & Laceulle, O. M. DSM-5 alternative personality disorder model traits as extreme variants of five-factor model traits in adolescents. Pers. Disord. 12, 59–69 (2021).

    Article  Google Scholar 

  192. See, A. Y., Klimstra, T. A., Cramer, A. O. & Denissen, J. J. The network structure of personality pathology in adolescence with the 100-item personality inventory for DSM-5 Short-Form (PID-5-SF). Front. Psychol. 11, 823 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Zhang, W., Wang, M., Yu, M. & Wang, J. The hierarchical structure and predictive validity of the personality inventory for DSM-5 in Chinese nonclinical adolescents. Assessment 29, 1559–1575 (2021).

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  195. Achenbach, T. M. Bottom-up and top-down paradigms for psychopathology: a half-century odyssey. Annu. Rev. Clin. Psychol. 16, 1–24 (2020).

    Article  PubMed  Google Scholar 

  196. Eaton, N. R., Krueger, R. F. & Oltmanns, T. F. Aging and the structure and long-term stability of the internalizing spectrum of personality and psychopathology. Psychol. Aging 26, 987–993 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Greene, A. L. & Eaton, N. R. The temporal stability of the bifactor model of comorbidity: an examination of moderated continuity pathways. Compr. Psychiat. 72, 74–82 (2017).

    Article  PubMed  Google Scholar 

  198. Murray, A. L., Eisner, M. & Ribeaud, D. The development of the general factor of psychopathology ‘p factor’ through childhood and adolescence. J. Abnorm. Child. Psychol. 44, 1573–1586 (2016).

    Article  PubMed  Google Scholar 

  199. Snyder, H. R., Young, J. F. & Hankin, B. L. Strong homotypic continuity in common psychopathology-, internalizing-, and externalizing-specific factors over time in adolescents. Clin. Psychol. Sci. 5, 98–110 (2017).

    Article  PubMed  Google Scholar 

  200. Eaton, N. R. et al. Genes, environments, personality, and successful aging: toward a comprehensive developmental model in later life. J. Gerontol. A 67, 480–488 (2012).

    Article  Google Scholar 

  201. Forbes, M. K., Tackett, J. L., Markon, K. E. & Krueger, R. F. Beyond comorbidity: toward a dimensional and hierarchical approach to understanding psychopathology across the life span. Dev. Psychopathol. 28, 971–986 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  202. Hopwood, C. J. et al. Integrating psychotherapy with the hierarchical taxonomy of psychopathology (HiTOP). J. Psychother. Integr. 30, 477–497 (2020).

    Article  Google Scholar 

  203. Ruggero, C. J. et al. Integrating the hierarchical taxonomy of psychopathology (HiTOP) into clinical practice. J. Consult. Clin. Psychol. 87, 1069–1084 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Mullins‐Sweatt, S. N. et al. Treatment of personality pathology through the lens of the hierarchical taxonomy of psychopathology: developing a research agenda. Pers. Ment. Health 14, 123–141 (2020).

    Article  Google Scholar 

  205. Clark, L. A., Watson, D. & Reynolds, S. Diagnosis and classification of psychopathology: challenges to the current system and future directions. Annu. Rev. Psychol. 46, 121–153 (1995).

    Article  PubMed  Google Scholar 

  206. Markon, K. E., Chmielewski, M. & Miller, C. J. The reliability and validity of discrete and continuous measures of psychopathology: a quantitative review. Psychol. Bull. 137, 856 (2011).

    Article  PubMed  Google Scholar 

  207. Clark, L. A. & Watson, D. in Methodological Issues And Strategies In Clinical Research (ed. Kazdin, A. E.) 187–203 (American Psychological Association, 2016).

  208. Waszczuk, M. A. et al. What do clinicians treat: diagnoses or symptoms? The incremental validity of a symptom-based, dimensional characterization of emotional disorders in predicting medication prescription patterns. Compr. Psychiat. 79, 80–88 (2017).

    Article  PubMed  Google Scholar 

  209. Hansen, S. J. et al. Mental health professionals’ perceived clinical utility of the ICD‐10 vs. ICD‐11 classification of personality disorders. Pers. Ment. Health 13, 84–95 (2019).

    Article  Google Scholar 

  210. Morey, L. C., Skodol, A. E. & Oldham, J. M. Clinician judgments of clinical utility: a comparison of DSM-IV-TR personality disorders and the alternative model for DSM-5 personality disorders. J. Abnorm. Psychol. 123, 398–405 (2014).

    Article  PubMed  Google Scholar 

  211. Rodriguez-Seijas, C., Eaton, N. R., Stohl, M., Mauro, P. M. & Hasin, D. S. Mental disorder comorbidity and treatment utilization. Compr. Psychiat. 79, 89–97 (2017).

    Article  PubMed  Google Scholar 

  212. Barlow, D. H., Harris, B. A., Eustis, E. H. & Farchione, T. J. The unified protocol for transdiagnostic treatment of emotional disorders. World Psychiat. 19, 245 (2020).

    Article  Google Scholar 

  213. Dalgleish, T., Black, M., Johnston, D. & Bevan, A. Transdiagnostic approaches to mental health problems: current status and future directions. J. Consult. Clin. Psychol. 88, 179–195 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  214. Ellard, K. K., Fairholme, C. P., Boisseau, C. L., Farchione, T. J. & Barlow, D. H. Unified protocol for the transdiagnostic treatment of emotional disorders: protocol development and initial outcome data. Cogn. Behav. Pract. 17, 88–101 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  215. Eaton, N. R., Rodriguez-Seijas, C. & Pachankis, J. E. Transdiagnostic approaches to sexual and gender minority mental health. Curr. Dir. Psychol. Sci. 30, 510–518 (2021).

    Article  Google Scholar 

  216. Conway, C. C., Krueger, R. F. & Board, H. C. E. Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training. Curr. Dir. Psychol. Sci. 30, 151–158 (2021).

    Article  Google Scholar 

  217. Rodriguez-Seijas, C., Eaton, N. R. & Krueger, R. F. How transdiagnostic factors of personality and psychopathology can inform clinical assessment and intervention. J. Pers. Assess. 97, 425–435 (2015).

    Article  PubMed  Google Scholar 

  218. von Klipstein, L., Riese, H., Servaas, M. N. & Schoevers, R. A. Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway. BMC Med. 18, 345 (2020).

    Article  Google Scholar 

  219. Krieke, L. V. D. et al. HowNutsAreTheDutch (HoeGekIsNL): a crowdsourcing study of mental symptoms and strengths. Int. J. Meth. Psychiat. Res. 25, 123–144 (2016).

    Article  Google Scholar 

  220. van Roekel, E. et al. Study protocol for a randomized controlled trial to explore the effects of personalized lifestyle advices and tandem skydives on pleasure in anhedonic young adults. BMC Psychiat. 16, 182 (2016).

    Article  Google Scholar 

  221. Bastiaansen, J. A. et al. Self-monitoring and personalized feedback based on the experiencing sampling method as a tool to boost depression treatment: a protocol of a pragmatic randomized controlled trial (ZELF-i). BMC Psychiat. 18, 276 (2018).

    Article  Google Scholar 

  222. Kroeze, R. et al. Personalized feedback on symptom dynamics of psychopathology: a proof-of-principle study. J. Pers. Oriented Res. 3, 1–10 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  223. Frumkin, M. R., Piccirillo, M. L., Beck, E. D., Grossman, J. T. & Rodebaugh, T. L. Feasibility and utility of idiographic models in the clinic: a pilot study. Psychother. Res. 31, 520–534 (2021).

    Article  PubMed  Google Scholar 

  224. Riese, H., Von Klipstein, L., Schoevers, R., van der Veen, D. & Servaas, M. Personalized ESM monitoring and feedback to support psychological treatment for depression: a pragmatic randomized controlled trial (Therap-i). BMC Psychiat. 21, 143 (2021).

    Article  Google Scholar 

  225. Rubel, J. A., Fisher, A. J., Husen, K. & Lutz, W. Translating person-specific network models into personalized treatments: development and demonstration of the dynamic assessment treatment algorithm for individual networks (DATA-IN). Psychother. Psychosom. 87, 249–251 (2018).

    Article  PubMed  Google Scholar 

  226. Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D. & Rubel, J. A. Exploring the idiographic dynamics of mood and anxiety via network analysis. J. Abnorm. Psychol. 126, 1044–1056 (2017).

    Article  PubMed  Google Scholar 

  227. Reeves, J. W. & Fisher, A. J. An examination of idiographic networks of posttraumatic stress disorder symptoms. J. Trauma. Stress. 33, 84–95 (2020).

    Article  PubMed  Google Scholar 

  228. Roefs, A. et al. A new science of mental disorders: using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology. Behav. Res. Ther. 153, 104096 (2022).

    Article  PubMed  Google Scholar 

  229. Bastiaansen, J. A. et al. Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology. J. Psychosom. Res. 137, 110211 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  230. Burger, J. et al. Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis. BMC Med. 18, 99 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  231. Burger, J. et al. A clinical PREMISE for personalized models: towards a formal integration of case formulations and statistical networks. J. Psychopathol. Clin. Sci. 131, 906–916 (2022).

    Article  PubMed  Google Scholar 

  232. Wiciński, M. & Węclewicz, M. M. Clozapine-induced agranulocytosis/granulocytopenia: mechanisms and monitoring. Curr. Opin. Hematol. 25, 22–28 (2018).

    Article  PubMed  Google Scholar 

  233. Hickie, I. B. et al. Right care, first time: a highly personalised and measurement‐based care model to manage youth mental health. Med. J. Aust. 211, S3–S46 (2019).

    Article  PubMed  Google Scholar 

  234. Nelson, B. et al. Staged treatment in early psychosis: a sequential multiple assignment randomised trial of interventions for ultra high risk of psychosis patients. Early Interv. Psychiat. 12, 292–306 (2018).

    Article  Google Scholar 

  235. Eaton, N. R. Measurement and mental health disparities: psychopathology classification and identity assessment. Pers. Ment. Health 14, 76–87 (2020).

    Article  Google Scholar 

  236. Rodriguez-Seijas, C., Eaton, N. R. & Pachankis, J. E. Prevalence of psychiatric disorders at the intersection of race and sexual orientation: results from the national epidemiologic survey of alcohol and related conditions — III. J. Consult. Clin. Psychol. 87, 321–331 (2019).

    Article  PubMed  Google Scholar 

  237. Rodriguez-Seijas, C. et al. Diversity and the hierarchical taxonomy of psychopathology (HiTOP). Nat. Rev. Psychol. https://doi.org/10.1038/s44159-023-00200-0 (2023).

    Article  Google Scholar 

  238. Eaton, N. R. The broad importance of integration: psychopathology research and hierarchy as construct. Eur. J. Pers. 31, 539–540 (2017).

    Google Scholar 

  239. Molenaar, P. C. Latent variable models are network models. Behav. Brain Sci. 33, 166 (2010).

    Article  PubMed  Google Scholar 

  240. Eaton, N. R. Latent variable and network models of comorbidity: toward an empirically derived nosology. Soc. Psychiat. Psychiatr. Epidemiol. 50, 845–849 (2015).

    Article  Google Scholar 

  241. Epskamp, S., Rhemtulla, M. & Borsboom, D. Generalized network psychometrics: combining network and latent variable models. Psychometrika 82, 904–927 (2017).

    Article  PubMed  Google Scholar 

  242. McFarland, D. J. & Malta, L. S. Symptoms as latent variables. Behav. Brain Sci. 33, 165–166 (2010).

    Article  PubMed  Google Scholar 

  243. Rush, A. J. et al. The inventory for depressive symptomatology (IDS): preliminary findings. Psychiat. Res. 18, 65–87 (1986).

    Article  Google Scholar 

  244. Michelini, G., Palumbo, I. M., DeYoung, C. G., Latzman, R. D. & Kotov, R. Linking RDoC and HiTOP: a new interface for advancing psychiatric nosology and neuroscience. Clin. Psychol. Rev. 86, 102025 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  245. Brown, T. A. Confirmatory Factor Analysis For Applied Research (Guilford, 2015).

  246. Kline, R. B. Principles And Practice Of Structural Equation Modeling 4th edn (Guilford, 2015).

  247. Conway, C. C., Forbes, M. K., South, S. C. & the HiTOP Consortium. A hierarchical taxonomy of psychopathology (HiTOP) primer for mental health researchers. Clin. Psychol. Sci. 10, 236–258 (2022).

    Article  PubMed  Google Scholar 

  248. Beltz, A. M. & Gates, K. M. Network mapping with GIMME. Multivar. Behav. Res. 52, 789–804 (2017).

    Article  Google Scholar 

  249. Bringmann, L. F. et al. A network approach to psychopathology: new insights into clinical longitudinal data. PLoS One 8, e60188 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  250. Costantini, G. et al. State of the aRt personality research: a tutorial on network analysis of personality data in R. J. Res. Pers. 54, 13–29 (2015).

    Article  Google Scholar 

  251. Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D. & Borsboom, D. qgraph: network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18 (2012).

    Article  Google Scholar 

  252. Van Borkulo, C. D. et al. A new method for constructing networks from binary data. Sci. Rep. 4, 5918 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, Stony Brook University, Stony Brook, NY, USA

    Nicholas R. Eaton

  2. Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, The Netherlands

    Laura F. Bringmann & Timon Elmer

  3. Department of Clinical Psychology, Leiden University, Leiden, The Netherlands

    Eiko I. Fried

  4. School of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia

    Miriam K. Forbes

  5. VISN 2 Mental Illness Research, Education, and Clinical Center, James J. Peters VA Medical Center, New York, NY, USA

    Ashley L. Greene

  6. Department of Psychology, University of Minnesota, Minneapolis, MN, USA

    Robert F. Krueger

  7. Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA

    Roman Kotov

  8. Orygen, Parkville, Victoria, Australia

    Patrick D. McGorry & Cristina Mei

  9. Center for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia

    Patrick D. McGorry & Cristina Mei

  10. Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA

    Monika A. Waszczuk

Authors
  1. Nicholas R. Eaton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura F. Bringmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Timon Elmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eiko I. Fried
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miriam K. Forbes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ashley L. Greene
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert F. Krueger
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Roman Kotov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Patrick D. McGorry
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Cristina Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Monika A. Waszczuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the writing and editing of this article. N.R.E. was additionally responsible for article structure and integration.

Corresponding author

Correspondence to Nicholas R. Eaton.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Psychology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Glossary

Assessment reliability

The extent to which observed scores on a test are precise and error-free and the degree to which observed scores represent true scores of the construct being assessed.

Betweenness centrality

Assesses the relative number of shortest paths between any two nodes in the network passing through a specific node (for example, if A and B are connected to C but not to each other, the node C lies on the shortest path between A and B).

Confirmatory factor analysis

A largely theory-driven latent variable modelling approach in which the researcher decides the number of latent variables as well as which items or scales load, and do not load, on each factor.

Exploratory factor analysis

A largely atheoretical latent variable modelling approach that generally estimates the number of latent factors underlying the observed items or scales, in which each item or scale is permitted to load on all estimated latent factors.

Inference validity

The extent to which observed scores on a test reflect the construct or constructs that the test is intended to measure, and justifiably supports inferences drawn about the observed test scores’ relations with other variables.

Macrophenotype

Late stage of syndrome development that consists of stable, intense and sustained, or severe syndromes (for example, psychosis, mania, depression, anxiety, alcohol- and substance-use disorders, and borderline personality disorder).

Microphenotype

Early stage of syndrome development that consists of overlapping and fluctuating symptoms.

Model fit

How well a statistical model is congruent with observed data, such as discrepancy between values in an observed correlation matrix and those in a model-implied (estimated) correlation matrix.

Structural validity

The degree to which observed scores (such as those from a measure) adequately reflect the underlying dimensionality of the construct or constructs being assessed.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eaton, N.R., Bringmann, L.F., Elmer, T. et al. A review of approaches and models in psychopathology conceptualization research. Nat Rev Psychol 2, 622–636 (2023). https://doi.org/10.1038/s44159-023-00218-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44159-023-00218-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing