Phenomapping: Methods and Measures for Deconstructing Diagnosis in Psychiatry

  • Andre F. MarquandEmail author
  • Thomas Wolfers
  • Richard Dinga


In most areas of medicine, biological tests are routinely used to assist diagnosis and treatment allocation. However, this is not the case in psychiatry, which is now one of the last areas of medicine where diseases are still diagnosed based on symptoms and biological tests to assist treatment allocation remain to be developed. Heterogeneity is widely recognized as a major challenge toward achieving these objectives and many approaches to tackle such heterogeneity have been proposed over the years, largely aiming to partition psychiatric disorders into more consistent subtypes. However, none of these stratifications have translated toward clinical practice. Here, we review the different approaches employed, focusing on methods that use biological measures to stratify psychiatric disorders. We highlight several recent prominent studies and identify key challenges for the field. Specifically, we argue that a lack of validation or replication of prospective stratifications coupled with a widespread fixation on finding sharply defined subtypes has impeded progress. We outline recently proposed methodological innovations that may be useful to move forward. Many of these innovations provide inferences at the level of individual participants and do not rest on the assumption that the biological fingerprints underlying psychiatric disorders can be cleanly separated into subtypes.


Psychiatric disorders Subtype Biotype Stratification Clustering Research Domain Criteria 


  1. Barch DM (2017) Biotypes: promise and pitfalls. Biol Psychiatry 82:2–3CrossRefGoogle Scholar
  2. Bedi G, Carillo F, Cecchi G, Sezak GF, Sigman M, Mota N, Ribeiro S, Javitt DC, Copelli M, Corcoran CM (2015) Automated analysis of free speech predicts psychosis onset in high-risk youths. Schizophrenia 1:15030CrossRefGoogle Scholar
  3. Beirlant J, Goegebeur Y, Teugels J, Segers J (2004) Statistics of extremes: theory and applications. Wiley, SussexCrossRefGoogle Scholar
  4. Betancur C (2011) Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 1380:42–77CrossRefGoogle Scholar
  5. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022Google Scholar
  6. Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on computational learning theory, vol 5, pp 144–152Google Scholar
  7. Brodersen KH, Deserno L, Schlagenhauf F, Lin Z, Penny WD, Buhmann JM, Stephan KE (2014) Dissecting psychiatric spectrum disorders by generative embedding. Neuroimage Clin 4:98–111CrossRefGoogle Scholar
  8. Cannon TD (2016) Deciphering the genetic complexity of schizophrenia. JAMA Psychiat 73:5–6CrossRefGoogle Scholar
  9. Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Ethridge LE, Pearlson GD, Keshavan MS, Tamminga CA (2016) Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry 173:373–384CrossRefGoogle Scholar
  10. Costa Dias TG, Iyer SP, Carpenter SD, Cary RP, Wilson VB, Mitchell SH, Nigg JT, Fair DA (2015) Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Dev Cogn Neurosci 11:155–174CrossRefGoogle Scholar
  11. Dong AY, Honnorat N, Gaonkar B, Davatzikos C (2016) CHIMERA: clustering of heterogeneous disease effects via distribution matching of imaging patterns. IEEE Trans Med Imaging 35:612–621CrossRefGoogle Scholar
  12. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28–38CrossRefGoogle Scholar
  13. Gates KM, Molenaar PCM, Iyer SP, Nigg JT, Fair DA (2014) Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. PLoS One 9(3):e91322CrossRefGoogle Scholar
  14. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New YorkCrossRefGoogle Scholar
  15. Honnorat J, Dong A, Meizenzahl-Lechner E, Koutsoleris N, Davatzikos C (2018) Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods. In pressGoogle Scholar
  16. Insel TR (2014) Mental disorders in childhood shifting the focus from behavioral symptoms to neurodevelopmental trajectories. JAMA 311:1727–1728CrossRefGoogle Scholar
  17. Insel TR, Cuthbert BN (2015) Brain disorders? Precisely. Science 348:499–500CrossRefGoogle Scholar
  18. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 167:748–751CrossRefGoogle Scholar
  19. Ivleva EI, Clementz BA, Dutcher AM, Arnold SJM, Jeon-Slaughter H, Aslan S, Witte B, Poudyal G, Lu H, Meda SA, Pearlson GD, Sweeney JA, Keshavan MS, Tamminga CA (2017) Brain structure biomarkers in the psychosis biotypes: findings from the bipolar-schizophrenia network for intermediate phenotypes. Biol Psychiatry 82:26–39CrossRefGoogle Scholar
  20. Kalia M (2015) Biomarkers for personalized oncology: recent advances and future challenges. Metabolism 64:S16–S21CrossRefGoogle Scholar
  21. Kapur S, Phillips AG, Insel TR (2012) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry 17:1174–1179CrossRefGoogle Scholar
  22. Kriegel H-P, Kroeger P, Zimek A (2009) Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data 3:1–58CrossRefGoogle Scholar
  23. Lamers F, Vogelzangs N, Merikangas KR, De Jonge P, Beekman ATF, Penninx BWJH (2013) Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 18:692–699CrossRefGoogle Scholar
  24. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  25. Liu Y, Hayes DN, Nobel A, Marron JS (2008) Statistical significance of clustering for high-dimension, low-sample size data. J Am Stat Assoc 103:1281–1293CrossRefGoogle Scholar
  26. Marquand AF, Rezek I, Buitelaar J, Beckmann CF (2016a) Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol Psychiatry 80:552–561CrossRefGoogle Scholar
  27. Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF (2016b) Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 1:433–447CrossRefGoogle Scholar
  28. Miettunen J, Nordstrom T, Kaakinen M, Ahmed AO (2016) Latent variable mixture modeling in psychiatric research—a review and application. Psychol Med 46:457–467CrossRefGoogle Scholar
  29. Milaneschi Y, Lamers F, Peyrot WJ, Abdellaoui A, Willemsen G, Hottenga J-J, Jansen R, Mbarek H, Dehghan A, Lu C, CHARGE Inflammation Working Group, Boomsma DI, Penninx BWHJ (2015) Polygenic dissection of major depression clinical heterogeneity. In pressGoogle Scholar
  30. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu JQ, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu J, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19:1523–1536CrossRefGoogle Scholar
  31. Mirnezami R, Nicholson J, Darzi A (2012) Preparing for precision medicine. N Engl J Med 366:489–491CrossRefGoogle Scholar
  32. Mourao-Miranda J, Hardoon DR, Hahn T, Marquand AF, Williams SCR, Shawe-Taylor J, Brammer M (2011) Patient classification as an outlier detection problem: an application of the one-class support vector machine. Neuroimage 58:793–804CrossRefGoogle Scholar
  33. Mwangi B, Matthews K, Steele JD (2012) Prediction of illness severity in patients with major depression using structural MR brain scans. J Magn Reson Imaging 35:64–71CrossRefGoogle Scholar
  34. Rao A, Monteiro JM, Mourao-Miranda J, Alzheimers Dis I (2017) Predictive modelling using neuroimaging data in the presence of confounds. Neuroimage 150:23–49CrossRefGoogle Scholar
  35. Rasmussen CE, Williams C (2006) Gaussian processes for machine learning. MIT Press, CambridgeGoogle Scholar
  36. Ripke S, Neale BM, Corvin A, Walters JTR, Farh K-H, Holmans PA, Lee P, Bulik-Sullivan B, Collier DA, Huang H, Pers TH, Agartz I, Agerbo E, Albus M, Alexander M, Amin F, Bacanu SA, Begemann M, Belliveau RA Jr, Bene J, Bergen SE, Bevilacqua E, Bigdeli TB, Black DW, Bruggeman R, Buccola NG, Buckner RL, Byerley W, Cahn W, Cai G, Campion D, Cantor RM, Carr VJ, Carrera N, Catts SV, Chambert KD, Chan RCK, Chen RYL, Chen EYH, Cheng W, Cheung EFC, Chong SA, Cloninger CR, Cohen D, Cohen N, Cormican P, Craddock N, Crowley JJ, Curtis D, Davidson M, Davis KL, Degenhardt F, Del Favero J, Demontis D, Dikeos D, Dinan T, Djurovic S, Donohoe G, Drapeau E, Duan J, Dudbridge F, Durmishi N, Eichhammer P, Eriksson J, Escott-Price V, Essioux L, Fanous AH, Farrell MS, Frank J, Franke L, Freedman R, Freimer NB, Friedl M, Friedman JI, Fromer M, Genovese G, Georgieva L, Giegling I, Giusti-Rodriguez P, Godard S, Goldstein JI, Golimbet V, Gopal S, Gratten J, De Haan L, Hammer C, Hamshere ML, Hansen M, Hansen T, Haroutunian V, Hartmann AM, Henskens FA, Herms S, Hirschhorn JN, Hoffmann P, Hofman A, Hollegaard MV, Hougaard DM, Ikeda M, Joa I et al (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427CrossRefGoogle Scholar
  37. Ruiz FJR, Valera I, Blanco C, Perez-Cruz F (2014) Bayesian nonparametric comorbidity analysis of psychiatric disorders. J Mach Learn Res 15:1215–1247Google Scholar
  38. Sato JR, Rondina JM, Mourao-Miranda J (2012) Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines. Front Neurosci 6:178CrossRefGoogle Scholar
  39. Schnack H (2018) Improving individual predictions: machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric disorders). Schizophr Res. In pressGoogle Scholar
  40. Schumann G, Binder EB, Holte A, De Kloet ER, Oedegaard KJ, Robbins TW, Walker-Tilley TR, Bitter I, Brown VJ, Buitelaar J, Ciccocioppo R, Cools R, Escera C, Fleischhacker W, Flor H, Frith CD, Heinz A, Johnsen E, Kirschbaum C, Klingberg T, Lesch K-P, Lewis S, Maier W, Mann K, Martinot J-L, Meyer-Lindenberg A, Mueller CP, Mueller WE, Nutt DJ, Persico A, Perugi G, Pessiglione M, Preuss UW, Roiser JP, Rossini PM, Rybakowski JK, Sandi C, Stephan KE, Undurraga J, Vieta E, Van Der Wee N, Wykes T, Maria Haro J, Wittchen HU (2014) Stratified medicine for mental disorders. Eur Neuropsychopharmacol 24:5–50CrossRefGoogle Scholar
  41. Sun H, Lui S, Yao L, Deng W, Xiao Y, Zhang W, Huang X, Hu J, Bi F, Li T, Sweeney JA, Gong Q (2015) Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiat 72:678–686CrossRefGoogle Scholar
  42. Torous J, Onnela JP, Keshavan M (2017) New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry 7(3):e1053CrossRefGoogle Scholar
  43. Van Dijk KRA, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59:431–438CrossRefGoogle Scholar
  44. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K, Consortium WU-MH (2013) The WU-minn human connectome project: an overview. Neuroimage 80:62–79CrossRefGoogle Scholar
  45. Varol E, Sotiras A, Davatzikos C, Alzheimer’s Disease Neuroimaging Initiative (2017) HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage 145:346–364CrossRefGoogle Scholar
  46. Weinberger DR, Goldberg TE (2014) RDoCs redux. World Psychiatry 13:36–38CrossRefGoogle Scholar
  47. Wolfers T, Buitelaar JK, Beckmann C, Franke B, Marquand AF (2015) From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev. In pressGoogle Scholar
  48. Wolfers T, Arenas AL, Onnink AMH, Dammers J, Hoogman M, Zwiers MP, Buitelaar JK, Franke B, Marquand AF, Beckmann CF (2017) Refinement by integration: aggregated effects of multimodal imaging markers on adult ADHD. J Psychiatry Neurosci 42:386–394CrossRefGoogle Scholar
  49. Young J, Ashburner J, Ourselin S (2013) Wrapper methods to correct mislabelled training data. 3rd international workshop on pattern recognition in neuroimaging. IEEE, PhiladelphiaGoogle Scholar
  50. Zhang XM, Mormino EC, Sun NB, Sperling RA, Sabuncu MR, Yeo BT, Alzheimer’s Disease Neuroimaging Initiative (2016) Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Proc Natl Acad Sci U S A 113:E6535–E6544CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andre F. Marquand
    • 1
    • 2
    • 3
    Email author
  • Thomas Wolfers
    • 1
    • 4
  • Richard Dinga
    • 5
  1. 1.Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
  2. 2.Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
  3. 3.Department of Neuroimaging, Centre for Neuroimaging SciencesInstitute of Psychiatry, King’s College LondonLondonUK
  4. 4.Department of Human GeneticsRadboud University Medical CentreNijmegenThe Netherlands
  5. 5.Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research InstituteVU University Medical CenterAmsterdamThe Netherlands

Personalised recommendations