Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Volumn 1, Issue 5, 2016, Pages 433-447

  Marquand, Andre F ^a,b,d   Wolfers, Thomas ^a   Mennes, Maarten ^a   Buitelaar, Jan ^a,b,c   Beckmann, Christian F ^a,b,e  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^c RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^d KING'S COLLEGE LONDON   (United Kingdom)

^e UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

European Roadmap for Mental Health Research; Heterogeneity; Latent cluster analysis; Psychiatry; RDoC; Research Domain Criteria; ROAMER; Subgroup

Indexed keywords

CLUSTER ANALYSIS; DISEASE CLASSIFICATION; FINITE ELEMENT ANALYSIS; HUMAN; MATHEMATICAL ANALYSIS; MENTAL DISEASE; PRIORITY JOURNAL; REVIEW; VALIDATION PROCESS;

EID: 84995543456     PISSN: 24519022     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bpsc.2016.04.002     Document Type: Review

References (113)

1. 1 Kapur, S., Phillips, A.G., Insel, T.R., Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?. Mol Psychiatry 17 (2012), 1174–1179.
2. 2 McKusick, V.A., On lumpers and splitters or nosology of genetic disease. Perspect Biol Med 12 (1969), 298–312.
3. 3 Kraepelin, E., Psychiatrie. 8th ed., 1909, Krieger Publishing, Huntington, NY, 1971.
4. 4 Bleuler, E., Lehrbuch der Psychiatrie. 1920, Springer-Verlag, Berlin.
5. 5 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed., 2013, American Psychiatric Association, Washington, DC.
6. 6 World Health Organization. International Statistical Classification of Diseases and Health Related Problems. 1992, World Health Organization, Geneva, Switzerland.
7. 7 Paykel, E.S., Classification of depressed patients—cluster analysis derived grouping. Br J Psychiatry 118 (1971), 275–288.
8. 8 Farmer, A.E., McGuffin, P., Spitznagel, E.L., Heterogeneity in schizophrenia—a cluster-analytic approach. Psychiatry Res 8 (1983), 1–12.
9. 9 Mirnezami, R., Nicholson, J., Darzi, A., Preparing for precision medicine. N Engl J Med 366 (2012), 489–491.
10. 10 Insel, T.R., Cuthbert, B.N., Brain disorders? Precisely. Science 348 (2015), 499–500.
11. 11 Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D.S., Quinn, K., et al. Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. Am J Psychiatry 167 (2010), 748–751.
12. 12 Schumann, G., Binder, E.B., Holte, A., de Kloet, E.R., Oedegaard, K.J., Robbins, T.W., et al. Stratified medicine for mental disorders. Eur Neuropsychopharmacol 24 (2014), 5–50.
13. 13 Brodersen, K.H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W.D., Buhmann, J.M., et al. Dissecting psychiatric spectrum disorders by generative embedding. Neuroimage Clin 4 (2014), 98–111.
14. 14 Karalunas, S.L., Fair, D., Musser, E.D., Aykes, K., Iyer, S.P., Nigg, J.T., Subtyping attention-deficit/hyperactivity disorder using temperament dimensions toward biologically based nosologic criteria. JAMA Psychiatry 71 (2014), 1015–1024.
15. 15 Fair, D.A., Bathula, D., Nikolas, M.A., Nigg, J.T., Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc Natl Acad Sci U S A 109 (2012), 6769–6774.
16. 16 Gates, K.M., Molenaar, P.C.M., Iyer, S.P., Nigg, J.T., Fair, D.A., Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. Plos One, 9, 2014, e91322.
17. 17 Costa Dias, T.G., Iyer, S.P., Carpenter, S.D., Cary, R.P., Wilson, V.B., Mitchell, S.H., et al. Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Dev Cogn Neurosci 11 (2015), 155–174.
18. 18 Bell, M.D., Corbera, S., Johannesen, J.K., Fiszdon, J.M., Wexler, B.E., Social cognitive impairments and negative symptoms in schizophrenia: Are there subtypes with distinct functional correlates?. Schizophr Bull 39 (2013), 186–196.
19. 19 van Loo, H.M., de Jonge, P., Romeijn, J.-W., Kessler, R.C., Schoevers, R.A., Data-driven subtypes of major depressive disorder: A systematic review. BMC Med, 10, 2012, 156.
20. 20 Pattyn, T., Van Den Eede, F., Lamers, F., Veltman, D., Sabbe, B.G., Penninx, B.W., Identifying panic disorder subtypes using factor mixture modeling. Depression Anxiety 32 (2015), 509–517.
21. 21 Sacco, R., Lenti, C., Saccani, M., Curatolo, P., Manzi, B., Bravaccio, C., et al. Cluster analysis of autistic patients based on principal pathogenetic components. Autism Res 5 (2012), 137–147.
22. 22 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 (2016), 547–556.
23. 23 Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning. 2nd ed., 2009, Springer, New York.
24. 24 Bishop, C., Pattern Recognition and Machine Learning. 2006, Springer, New York.
25. 25 Lazarsfeld, P.F., Henry, N.W., Latent Structure Analysis. 1968, Houghton Mifflin, Boston.
26. 26 Muthen, B., Beyond SEM: General latent variable modeling. Behaviormetrika 29 (2002), 81–117.
27. 27 Klöppel, S., Abdulkadir, A., Jack, C.R. Jr, Koutsouleris, N., Mourão-Miranda, J., Vemuri, P., Diagnostic neuroimaging across diseases. Neuroimage 61 (2012), 457–463.
28. 28 Orru, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A., Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neurosci Biobehav Rev 36 (2012), 1140–1152.
29. 29 Wolfers, T., Buitelaar, J.K., Beckmann, C., Franke, B., Marquand, A.F., From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 57 (2015), 328–349.
30. 30 Xu, R., Wunsch, D. 2nd, Survey of clustering algorithms. IEEE Trans Neural Netw 16 (2005), 645–678.
31. 31 Kriegel, H.-P., Kroeger, P., Zimek, A., Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data 3, 2009, 1–58.
32. 32 Ward, J.H., Hierarchical grouping to optimize an objective function. J Am Statistical Assoc 58 (1963), 236–244.
33. 33 Newman, M.E.J., Modularity and community structure in networks. Proc Natl Acad Sci U S A 103 (2006), 8577–8582.
34. 34 Hagenaars, J.A., McCutcheon, A.L., Applied latent class cluster analysis. 2002, Cambridge University Press, Cambridge.
35. 35 Muthen, B., Shedden, K., Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55 (1999), 463–469.
36. 36 Nagin, D.S., Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods 4 (1999), 139–157.
37. 37 Insel, T.R., Mental disorders in childhood shifting the focus from behavioral symptoms to neurodevelopmental trajectories. JAMA 311 (2014), 1727–1728.
38. 38 Weinberger, D.R., Goldberg, T.E., RDoCs redux. World Psychiatry 13 (2014), 36–38.
39. 39 Rhebergen, D., Lamers, F., Spijker, J., de Graaf, R., Beekman, A.T.F., Penninx BWJH, Course trajectories of unipolar depressive disorders identified by latent class growth analysis. Psychol Med 42 (2012), 1383–1396.
40. 40 Fountain, C., Winter, A.S., Bearman, P.S., Six developmental trajectories characterize children with autism. Pediatrics 129 (2012), E1112–E1120.
41. 41 Titterington, D.M., Smith, A.F.M., Makov, U.E., Statistical analysis of finite mixture distributions. 1985, John Wiley and Sons, New York.
42. 42 Schwarz, G., Estimating dimension of a model. Ann Stat 6 (1978), 461–464.
43. 43 Akaike, H., A new look at the statistical model identification. IEEE Trans Automatic Control 19 (1974), 716–723.
44. 44 Lo, Y.T., Mendell, N.R., Rubin, D.B., Testing the number of components in a normal mixture. Biometrika 88 (2001), 767–778.
45. 45 Nylund, K.L., Asparouhov, T., Muthen, B.O., Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Modeling 14 (2007), 535–569.
46. 46 Rousseeuw, P.J., Silhouettes—a graphical aid to the interpretation and validation of cluster-analysis. J Comput Appl Mathematics 20 (1987), 53–65.
47. 47 C, DJ, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybernet 3 (1973), 32–57.
48. 48 Saha, S., Bandyopadhyay, S., Some connectivity based cluster validity indices. Applied Soft Computing 12 (2012), 1555–1565.
49. 49 Ferguson, T.S., A Bayesian analysis of some nonparametric problems. Annals of Statistics 1 (1973), 209–230.
50. 50 Jablensky, A., Subtyping schizophrenia: Implications for genetic research. Mol Psychiatry 11 (2006), 815–836.
51. 51 Heinrichs, R.W., Meta-analysis, and the science of schizophrenia: Variant evidence or evidence of variants?. Neurosci Biobehav Rev 28 (2004), 379–394.
52. 52 Sun, H., Lui, S., Yao, L., Deng, W., Xiao, Y., Zhang, W., et al. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry 72 (2015), 678–686.
53. 53 Kendler, K.S., Eaves, L.J., Walters, E.E., Neale, M.C., Heath, A.C., Kessler, R.C., The identification and validation of distinct depressive syndromes in a population-based sample of female twins. Arch Gen Psychiatry 53 (1996), 391–399.
54. 54 Sullivan, P.F., Kessler, R.C., Kendler, K.S., Latent class analysis of lifetime depressive symptoms in the National Comorbidity Survey. Am J Psychiatry 155 (1998), 1398–1406.
55. 55 Lamers, F., de Jonge, P., Nolen, W.A., Smit, J.H., Zitman, F.G., Beekman, A.T.F., et al. Identifying depressive subtypes in a large cohort study: Results from the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry 71 (2010), 1582–1589.
56. 56 Lamers, F., Burstein, M., He, J.P., Avenevoli, S., Angst, J., Merikangas, K.R., Structure of major depressive disorder in adolescents and adults in the US general population. Br J Psychiatry 201 (2012), 143–150.
57. 57 Maes, M., Maes, L., Schotte, C., Cosyns, P., A clinical and biological validation of the DSM-III melancholia diagnosis in men—results of pattern-recognition methods. J Psychiatr Res 26 (1992), 183–196.
58. 58 Hybels, C.F., Blazer, D.G., Pieper, C.F., Landerman, L.R., Steffens, D.C., Profiles of depressive symptoms in older adults diagnosed with major depression: Latent cluster analysis. Am J Geriatr Psychiatry 17 (2009), 387–396.
59. 59 van Loo, H.M., Cai, T., Gruber, M.J., Li, J., de Jonge, P., Petukhova, M., et al. Major depressive disorder subtypes to predict long-term course. Depression Anxiety 31 (2014), 765–777.
60. 60 Milaneschi, Y., Lamers, F., Peyrot, W.J., Abdellaoui, A., Willemsen, G., Hottenga, J.J., et al. Polygenic dissection of major depression clinical heterogeneity. Mol Psychiatry 21 (2015), 516–522.
61. 61 Betancur, C., Etiological heterogeneity in autism spectrum disorders: More than 100 genetic and genomic disorders and still counting. Brain Res 1380 (2011), 42–77.
62. 62 Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511 (2014), 421–427.
63. 63 Cannon, T.D., Deciphering the genetic complexity of schizophrenia. JAMA Psychiatry 73 (2016), 5–6.
64. 64 Lahey, B.B., Pelham, W.E., Loney, J., Lee, S.S., Willcutt, E., Instability of the DSM-IV subtypes of ADHD from preschool through elementary school. Arch Gen Psychiatry 62 (2005), 896–902.
65. 65 Slagle, J.L., Chang, C.L., Heller, S.L., A clustering and data-reorganization algorithm. IEEE Transactions on Systems Man and Cybernetics 5 (1975), 121–128.
66. 66 Bouveyron, C., Brunet-Saumard, C., Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis 71 (2014), 52–78.
67. 67 van Hulst, B.M., de Zeeuw, P., Durston, S., Distinct neuropsychological profiles within ADHD: A latent class analysis of cognitive control, reward sensitivity and timing. Psychological Med 45 (2015), 735–745.
68. 68 Veatch, O.J., Veenstra-VanderWeele, J., Potter, M., Pericak-Vance, M.A., Haines, J.L., Genetically meaningful phenotypic subgroups in autism spectrum disorders. Genes Brain Behav 13 (2014), 276–285.
69. 69 Robins, E., Guze, S.B., Establishment of diagnostic validity in psychiatric illness—its application to schizophrenia. Am J Psychiatry 126 (1970), 983–987.
70. 70 Chaste, P., Klei, L., Sanders, S.J., Hus, V., Murtha, M.T., Lowe, J.K., et al. A genome-wide association study of autism using the Simons simplex collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity?. Biol Psychiatry 77 (2015), 775–784.
71. 71 Milaneschi, Y., Lamers, F., Bot, M., Drent, M.L., Penninx BWJH, Leptin dysregulation is specifically associated with major depression with atypical features: Evidence for a mechanism connecting obesity and depression. Biol Psychiatry Nov, 2015, 17, 10.1016/j.biopsych.2015.10.023 [Epub ahead of print].
72. 72 Lamers, F., Beekman, A.T.F., van Hemert, A.M., Schoevers, R.A., Penninx BWJH, Six-year longitudinal course and outcomes of subtypes of depression. Br J Psychiatry 208 (2016), 62–68.
73. 73 Schmaal, L., Marquand, A.F., Rhebergen, D., van Tol, M.J., Ruhe, H.G., van der Wee, N.J.A., et al. Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: A multivariate pattern recognition study. Biol Psychiatry 78 (2015), 278–286.
74. 74 Young, J., Ashburner, J., Ourselin, S., Wrapper methods to correct mislabelled training data. 3rd International Workshop on Pattern Recognition in Neuroimaging. 2013, IEEE, Philadelphia.
75. 75 Filipovych, R., Davatzikos, C., Alzheimer's Disease Neuroimaging Initiative. Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI). Neuroimage 55 (2011), 1109–1119.
76. 76 Filipovych, R., Resnick, S.M., Davatzikos, C., JointMMCC: Joint maximum-margin classification and clustering of imaging data. IEEE Transactions on Medical Imaging 31 (2012), 1124–1140.
77. 77 Varol, E., Sotiras, A., Davatzikos, C., Disentangling disease heterogeneity with max-margin multiple hyperplane classifier. Medical Image Computing and Computer-Assisted Intervention—MICCAI. 2015, Springer, Heidelberg, 702–709.
78. 78 Eavani, H., Hsieh, M.K., An, Y., Erus, G., Beason-Held, L., Resnick, S., et al. Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging. Neuroimage 125 (2016), 498–514.
79. 79 Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P., Image-driven population analysis through mixture modeling. IEEE Transactions on Medical Imaging 28 (2009), 1473–1487.
80. 80 van der Maaten, L., Hinton, G., Visualizing data using t-SNE. J Machine Learn Res 9 (2008), 2579–2605.
81. 81 Ridgway, G.R., Lehmann, M., Barnes, J., Rohrer, J.D., Warren, J.D., Crutch, S.J., et al. Early-onset Alzheimer disease clinical variants Multivariate analyses of cortical thickness. Neurology 79 (2012), 80–84.
82. 82 Mwangi, B., Soares, J.C., Hasan, K.M., Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data. J Neurosci Methods 236 (2014), 19–25.
83. 83 Scholkopf, B., Platt, J.C., Taylor, J.S., Smola, A.J., Williamson, R.C., Estimating the support of a high-dimensional distribution. Neural Computation 13 (2001), 1443–1471.
84. 84 Mourao-Miranda, J., Hardoon, D.R., Hahn, T., Marquand, A.F., Williams, S.C.R., Shawe-Taylor, J., et al. Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine. Neuroimage 58 (2011), 793–804.
85. 85 Gur, R.C., Calkins, M.E., Satterthwaite, T.D., Ruparel, K., Bilker, W.B., Moore, T.M., et al. Neurocognitive growth charting in psychosis spectrum youths. JAMA Psychiatry 71 (2014), 366–374.
86. 86 Erus, G., Battapady, H., Satterthwaite, T.D., Hakonarson, H., Gur, R.E., Davatzikos, C., et al. Imaging patterns of brain development and their relationship to cognition. Cerebral Cortex 25 (2015), 1676–1684.
87. 87 Cuthbert, B.N., The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 13 (2014), 28–35.
88. 88 Arnedo, J., Svrakic, D.M., del Val, C., Romero-Zaliz, R., Hernandez-Cuervo, H., Fanous, A.H., et al. Uncovering the hidden risk architecture of the schizophrenias: Confirmation in three independent genome-wide association studies. Am J Psychiatry 172 (2015), 139–153.
89. 89 Simpson, A., Tan, V.Y.F., Winn, J., Svensen, M., Bishop, C.M., Heckerman, D.E., et al. Beyond atopy multiple patterns of sensitization in relation to asthma in a birth cohort study. Am J Respir Crit Care Med 181 (2010), 1200–1206.
90. 90 Ruiz, F.J.R., Valera, I., Blanco, C., Perez-Cruz, F., Bayesian nonparametric comorbidity analysis of psychiatric disorders. J Machine Learn Res 15 (2014), 1215–1247.
91. 91 LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
92. 92 Kim, J., Calhoun, V.D., Shim, E., Lee, J.H., Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124 (2016), 127–146.
93. 93 Castle, D.J., Sham, P.C., Wessely, S., Murray, R.M., The subtyping of schizophrenia in men and women—a latent class analysis. Psychological Med 24 (1994), 41–51.
94. 94 Sham, P.C., Castle, D.J., Wessely, S., Farmer, A.E., Murray, R.M., Further exploration of a latent class typology of schizophrenia. Schizophrenia Res 20 (1996), 105–115.
95. 95 Dollfus, S., Everitt, B., Ribeyre, J.M., AssoulyBesse, F., Sharp, C., Petit, M., Identifying subtypes of schizophrenia by cluster analyses. Schizophrenia Bull 22 (1996), 545–555.
96. 96 Kendler, K.S., Karkowski, L.M., Walsh, D., The structure of psychosis—Latent class analysis of probands from the Roscommon family study. Arch Gen Psychiatry 55 (1998), 492–499.
97. 97 Murray, V., McKee, I., Miller, P.M., Young, D., Muir, W.J., Pelosi, A.J., et al. Dimensions and classes of psychosis in a population cohort: a four-class, four-dimension model of schizophrenia and affective psychoses. Psychological Med 35 (2005), 499–510.
98. 98 Dawes, S.E., Jeste, D.V., Palmer, B.W., Cognitive profiles in persons with chronic schizophrenia. J Clin Exp Neuropsychol 33 (2011), 929–936.
99. 99 Cole, V.T., Apud, J.A., Weinberger, D.R., Dickinson, D., Using latent class growth analysis to form trajectories of premorbid adjustment in schizophrenia. J Abnormal Psychol 121 (2012), 388–395.
100. 100 Friston, K.J., Harrison, L., Penny, W., Dynamic causal modelling. Neuroimage 19 (2003), 1273–1302.
101. 101 Kass, R.E., Raftery, A.E., Bayes factors. J Am Statistical Assoc 90 (1995), 773–795.
102. 102 Geisler, D., Walton, E., Naylor, M., Roessner, V., Lim, K.O., Schulz, S.C., et al. Brain structure and function correlates of cognitive subtypes in schizophrenia. Psychiatry Res Neuroimaging 234 (2015), 74–83.
103. 103 Friedman, H.P., Rubin, J., On some invariant criteria for grouping data. J Am Statistical Assoc, 62, 1967 1159–1178.
104. 104 Lamers, F., Rhebergen, D., Merikangas, K.R., de Jonge, P., Beekman, A.T.F., Penninx BWJH, Stability and transitions of depressive subtypes over a 2-year follow-up. Psychological Med 42 (2012), 2083–2093.
105. 105 Lamers, F., Vogelzangs, N., Merikangas, K.R., de Jonge, P., Beekman, A.T.F., Penninx BWJH, Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 18 (2013), 692–699.
106. 106 Mostert, J.C., Hoogman, M., Onnink, A.M.H., van Rooij, D., von Rhein, D., van Hulzen, K.J.E., et al. Similar subgroups based on cognitive performance parse heterogeneity in adults with ADHD and healthy controls. J Atten Disord Sep, 2015, 14 pii: 1087054715602332. [Epub ahead of print].
107. 107 Munson, J., Dawson, G., Sterling, L., Beauchaine, T., Zhou, A., Koehler, E., et al. Evidence for latent classes of IQ in young children with autism spectrum disorder. Am J Ment Retard 113 (2008), 439–452.
108. 108 Georgiades, S., Szatmari, P., Boyle, M., Hanna, S., Duku, E., Zwaigenbaum, L., et al. Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach. J Child Psychol Psychiatry 54 (2013), 206–215.
109. 109 Doshi-Velez, F., Ge, Y., Kohane, I., Comorbidity clusters in autism spectrum disorders: An electronic health record time-series analysis. Pediatrics 133 (2014), E54–E63.
110. 110 Hubert, L., Arabie, P., Comparing partitions. J Classification 2 (1985), 193–218.
111. 111 Lewandowski, K.E., Sperry, S.H., Cohen, B.M., Oenguer, D., Cognitive variability in psychotic disorders: A cross-diagnostic cluster analysis. Psychological Med 44 (2014), 3239–3248.
112. 112 Kleinman, A., Caetano, S.C., Brentani, H., de Almeida Rocca, C.C., dos Santos, B., Andrade, E.R., et al. Attention-based classification pattern, a research domain criteria framework, in youths with bipolar disorder and attention-deficit/hyperactivity disorder. Aust N Z J Psychiatry 49 (2015), 255–265.
113. 113 Olino, T.M., Klein, D.N., Lewinsohn, P.M., Rohde, P., Seeley, J.R., Latent trajectory classes of depressive and anxiety disorders from adolescence to adulthood: Description of classes and associations with risk factors. Compr Psychiatry 51 (2010), 224–235.