Big Data Guided Interventions: Predicting Treatment Response

  • Alexander Kautzky
  • Rupert Lanzenberger
  • Siegfried KasperEmail author


Big data analytics and advanced statistical learning methods held an auspicious entry into neuropsychiatric research over the last decade. Especially for common multifactorial diseases as major depressive disorder (MDD), decisive advantages for diagnostics and prediction of treatment outcome phenotypes were both promised and expected. While a substantial amount of research was brought forward over the last years that already acknowledged the high potential of big data analytics for precision medicine in psychiatry, these expectations have so far been curbed by data management and methodological issues as well as difficulties inherent to the heterogeneous nature of neuropsychiatric disorders.

Based on the example of MDD and treatment resistance in depression, this chapter will first give an overview of unsupervised machine learning algorithms targeting heterogeneity by surfacing subtypes of depression in a data driven manor. Supervised learning algorithms discussed next in this chapter are focused on predicting treatment outcome for antidepressant trials, based on clinical, genetic and imaging predictors. Finally, state-of-the-art machine learning design with prerequisites for successful and clinically meaningful application are discussed and prospects of their future in neuropsychiatric research are presented.


Treatment response Major depressive disorder Multimodal data Data driven Big data 


  1. Amare AT, Schubert KO, Tekola-Ayele F, Hsu YH, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A, Altman RB, Arolt V, Brockmoller J, Chen CH, Domschke K, Hall-Flavin DK, Hong CJ, Illi A, Ji Y, Kampman O, Kinoshita T, Leinonen E, Liou YJ, Mushiroda T, Nonen S, Skime MK, Wang L, Kato M, Liu YL, Praphanphoj V, Stingl JC, Bobo WV, Tsai SJ, Kubo M, Klein TE, Weinshilboum RM, Biernacka JM, Baune BT (2018) Association of the polygenic scores for personality traits and response to selective serotonin reuptake inhibitors in patients with major depressive disorder. Front Psych 9:65CrossRefGoogle Scholar
  2. Arnow BA, Blasey C, Williams LM, Palmer DM, Rekshan W, Schatzberg AF, Etkin A, Kulkarni J, Luther JF, Rush AJ (2015) Depression subtypes in predicting antidepressant response: a report from the iSPOT-D trial. Am J Psychiatry 172:743–750PubMedCrossRefGoogle Scholar
  3. Balestri M, Calati R, Souery D, Kautzky A, Kasper S, Montgomery S, Zohar J, Mendlewicz J, Serretti A (2016) Socio-demographic and clinical predictors of treatment resistant depression: a prospective European multicenter study. J Affect Disord 189:224–232PubMedCrossRefGoogle Scholar
  4. Bauer M, Severus E, Kohler S, Whybrow PC, Angst J, Moller HJ, WFSBP Task Force on Treatment Guidelines for Unipolar Depressive Disorders (2015) World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of unipolar depressive disorders. Part 2: maintenance treatment of major depressive disorder-update 2015. World J Biol Psychiatry 16:76–95PubMedCrossRefGoogle Scholar
  5. Biernacka JM, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A, Altman RB, Arolt V, Brockmoller J, Chen CH, Domschke K, Hall-Flavin DK, Hong CJ, Illi A, Ji Y, Kampman O, Kinoshita T, Leinonen E, Liou YJ, Mushiroda T, Nonen S, Skime MK, Wang L, Baune BT, Kato M, Liu YL, Praphanphoj V, Stingl JC, Tsai SJ, Kubo M, Klein TE, Weinshilboum R (2015) The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl Psychiatry 5:e553PubMedPubMedCentralCrossRefGoogle Scholar
  6. Breen G, Li Q, Roth BL, O’Donnell P, Didriksen M, Dolmetsch R, O’Reilly PF, Gaspar HA, Manji H, Huebel C, Kelsoe JR, Malhotra D, Bertolino A, Posthuma D, Sklar P, Kapur S, Sullivan PF, Collier DA, Edenberg HJ (2016) Translating genome-wide association findings into new therapeutics for psychiatry. Nat Neurosci 19:1392–1396PubMedPubMedCentralCrossRefGoogle Scholar
  7. Carvalho AF, Berk M, Hyphantis TN, Mcintyre RS (2014) The integrative management of treatment-resistant depression: a comprehensive review and perspectives. Psychother Psychosom 83:70–88PubMedCrossRefGoogle Scholar
  8. Caudill MM, Hunter AM, Cook IA, Leuchter AF (2015) The antidepressant treatment response index as a predictor of Reboxetine treatment outcome in major depressive disorder. Clin EEG Neurosci 46:277–284PubMedCrossRefGoogle Scholar
  9. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR (2016) Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:243–250PubMedPubMedCentralCrossRefGoogle Scholar
  10. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, Mccarthy G (2017) Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiat 74:370–378CrossRefGoogle Scholar
  11. Chen CC, Schwender H, Keith J, Nunkesser R, Mengersen K, Macrossan P (2011) Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression. IEEE/ACM Trans Comput Biol Bioinform 8:1580–1591PubMedCrossRefGoogle Scholar
  12. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, Leucht S, Ruhe HG, Turner EH, Higgins JPT, Egger M, Takeshima N, Hayasaka Y, Imai H, Shinohara K, Tajika A, Ioannidis JPA, Geddes JR (2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391:1357–1366PubMedPubMedCentralCrossRefGoogle Scholar
  13. Cohen ZD, Derubeis RJ (2018) Treatment selection in depression. Annu Rev Clin Psychol 14:209–236PubMedCrossRefGoogle Scholar
  14. Costafreda SG, Chu C, Ashburner J, Fu CH (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4:e6353PubMedPubMedCentralCrossRefGoogle Scholar
  15. Derubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L (2014) The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One 9:e83875PubMedPubMedCentralCrossRefGoogle Scholar
  16. Dold M, Kasper S (2016) Evidence-based pharmacotherapy of treatment-resistant unipolar depression. Int J Psychiatry Clin Pract 21:1–11Google Scholar
  17. Fried EI (2017) The 52 symptoms of major depression: lack of content overlap among seven common depression scales. J Affect Disord 208:191–197PubMedCrossRefGoogle Scholar
  18. Fried EI, Van Borkulo CD, Epskamp S, Schoevers RA, Tuerlinckx F, Borsboom D (2016) Measuring depression over time. Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychol Assess 28:1354–1367PubMedCrossRefGoogle Scholar
  19. Garcia-Gonzalez J, Tansey KE, Hauser J, Henigsberg N, Maier W, Mors O, Placentino A, Rietschel M, Souery D, Zagar T, Czerski PM, Jerman B, Buttenschon HN, Schulze TG, Zobel A, Farmer A, Aitchison KJ, Craig I, Mcguffin P, Giupponi M, Perroud N, Bondolfi G, Evans D, O’Donovan M, Peters TJ, Wendland JR, Lewis G, Kapur S, Perlis R, Arolt V, Domschke K, Breen G, Curtis C, Sang-Hyuk L, Kan C, Newhouse S, Patel H, Baune BT, Uher R, Lewis CM, Fabbri C, Major Depressive Disorder Working Group of the Psychiatric Genomic Consortium (2017) Pharmacogenetics of antidepressant response: a polygenic approach. Prog Neuropsychopharmacol Biol Psychiatry 75:128–134PubMedCrossRefGoogle Scholar
  20. Gratten J, Wray NR, Keller MC, Visscher PM (2014) Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat Neurosci 17:782–790PubMedPubMedCentralCrossRefGoogle Scholar
  21. Grisanzio KA, Goldstein-Piekarski AN, Wang MY, Rashed Ahmed AP, Samara Z, Williams LM (2018) Transdiagnostic symptom clusters and associations with brain, behavior, and daily function in mood, anxiety, and trauma disorders. JAMA Psychiat 75:201–209CrossRefGoogle Scholar
  22. Hunter AM, Cook IA, Greenwald SD, Tran ML, Miyamoto KN, Leuchter AF (2011) The antidepressant treatment response index and treatment outcomes in a placebo-controlled trial of fluoxetine. J Clin Neurophysiol 28:478–482PubMedPubMedCentralGoogle Scholar
  23. Iniesta R, Malki K, Maier W, Rietschel M, Mors O, Hauser J, Henigsberg N, Dernovsek MZ, Souery D, Stahl D, Dobson R, Aitchison KJ, Farmer A, Lewis CM, Mcguffin P, Uher R (2016) Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J Psychiatr Res 78:94–102PubMedCrossRefGoogle Scholar
  24. Iniesta R, Hodgson K, Stahl D, Malki K, Maier W, Rietschel M, Mors O, Hauser J, Henigsberg N, Dernovsek MZ, Souery D, Dobson R, Aitchison KJ, Farmer A, Mcguffin P, Lewis CM, Uher R (2018) Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Sci Rep 8:5530PubMedPubMedCentralCrossRefGoogle Scholar
  25. Jung J, Tawa EA, Muench C, Rosen AD, Rickels K, Lohoff FW (2017) Genome-wide association study of treatment response to venlafaxine XR in generalized anxiety disorder. Psychiatry Res 254:8–11PubMedPubMedCentralCrossRefGoogle Scholar
  26. Kautzky A, Baldinger P, Souery D, Montgomery S, Mendlewicz J, Zohar J, Serretti A, Lanzenberger R, Kasper S (2015) The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression. Eur Neuropsychopharmacol 25:441–453PubMedCrossRefGoogle Scholar
  27. Kautzky A, Baldinger-Melich P, Kranz GS, Vanicek T, Souery D, Montgomery S, Mendlewicz J, Zohar J, Serretti A, Lanzenberger R, Kasper S (2017a) A new prediction model for evaluating treatment-resistant depression. J Clin Psychiatry 78:215–222PubMedCrossRefGoogle Scholar
  28. Kautzky A, Dold M, Bartova L, Spies M, Vanicek T, Souery D, Montgomery S, Mendlewicz J, Zohar J, Fabbri C, Serretti A, Lanzenberger R, Kasper S (2017b) Refining prediction in treatment-resistant depression: results of machine learning analyses in the TRD III sample. J Clin Psychiatry 79. PubMedCrossRefGoogle Scholar
  29. Kennedy SH, Downar J, Evans KR, Feilotter H, Lam RW, Macqueen GM, Milev R, Parikh SV, Rotzinger S, Soares C (2012) The Canadian biomarker integration network in depression (CAN-BIND): advances in response prediction. Curr Pharm Des 18:5976–5989PubMedCrossRefGoogle Scholar
  30. Liu F, Guo W, Yu D, Gao Q, Gao K, Xue Z, Du H, Zhang J, Tan C, Liu Z, Zhao J, Chen H (2012) Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS One 7:e40968PubMedPubMedCentralCrossRefGoogle Scholar
  31. Maciukiewicz M, Marshe VS, Tiwari AK, Fonseka TM, Freeman N, Kennedy JL, Rotzinger S, Foster JA, Kennedy SH, Muller DJ (2017) Genome-wide association studies of placebo and duloxetine response in major depressive disorder. Pharmacogenomics J 18(3):406–412PubMedCrossRefGoogle Scholar
  32. Maciukiewicz M, Marshe VS, Hauschild AC, Foster JA, Rotzinger S, Kennedy JL, Kennedy SH, Muller DJ, Geraci J (2018) GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. J Psychiatr Res 99:62–68PubMedCrossRefGoogle Scholar
  33. Mandelli L, Serretti A, Souery D, Mendlewicz J, Kasper S, Montgomery S, Zohar J (2016) High occupational level is associated with poor response to treatment of depression. Eur Neuropsychopharmacol 26:1320–1326PubMedCrossRefGoogle Scholar
  34. Marquand AF, Mourao-Miranda J, Brammer MJ, Cleare AJ, Fu CH (2008) Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport 19:1507–1511PubMedCrossRefGoogle Scholar
  35. Musil R, Seemuller F, Meyer S, Spellmann I, Adli M, Bauer M, Kronmuller KT, Brieger P, Laux G, Bender W, Heuser I, Fisher R, Gaebel W, Schennach R, Moller HJ, Riedel M (2018) Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder. Int J Methods Psychiatr Res 27. CrossRefGoogle Scholar
  36. Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V, Fu CH (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56:809–813PubMedCrossRefGoogle Scholar
  37. Passos IC, Mwangi B, Kapczinski F (2016) Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry 3:13–15PubMedCrossRefGoogle Scholar
  38. Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF 3rd, Aizenstein HJ (2015) Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 30:1056–1067PubMedPubMedCentralCrossRefGoogle Scholar
  39. Perlis RH (2013) A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry 74:7–14PubMedPubMedCentralCrossRefGoogle Scholar
  40. Perlis RH (2016) Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry 15:228–235PubMedPubMedCentralCrossRefGoogle Scholar
  41. Perlis RH, Fijal B, Adams DH, Sutton VK, Trivedi MH, Houston JP (2009) Variation in catechol-O-methyltransferase is associated with duloxetine response in a clinical trial for major depressive disorder. Biol Psychiatry 65:785–791PubMedCrossRefGoogle Scholar
  42. Perlis RH, Fijal B, Dharia S, Heinloth AN, Houston JP (2010) Failure to replicate genetic associations with antidepressant treatment response in duloxetine-treated patients. Biol Psychiatry 67:1110–1113PubMedCrossRefGoogle Scholar
  43. Riedel M, Moller HJ, Obermeier M, Adli M, Bauer M, Kronmuller K, Brieger P, Laux G, Bender W, Heuser I, Zeiler J, Gaebel W, Schennach-Wolff R, Henkel V, Seemuller F (2011) Clinical predictors of response and remission in inpatients with depressive syndromes. J Affect Disord 133:137–149PubMedCrossRefGoogle Scholar
  44. Scarr E, Millan MJ, Bahn S, Bertolino A, Turck CW, Kapur S, Moller HJ, Dean B (2015) Biomarkers for psychiatry: the journey from fantasy to fact, a report of the 2013 CINP think tank. Int J Neuropsychopharmacol 18:pyv042PubMedPubMedCentralCrossRefGoogle Scholar
  45. Schmaal L, Marquand AF, Rhebergen D, Van Tol MJ, Ruhe HG, Van Der Wee NJ, Veltman DJ, Penninx BW (2015) Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol Psychiatry 78:278–286PubMedPubMedCentralCrossRefGoogle Scholar
  46. Schosser A, Serretti A, Souery D, Mendlewicz J, Zohar J, Montgomery S, Kasper S (2012) European Group for the Study of Resistant Depression (GSRD)—where have we gone so far: review of clinical and genetic findings. Eur Neuropsychopharmacol 22:453–468PubMedCrossRefGoogle Scholar
  47. Serretti A, Olgiati P, Liebman MN, Hu H, Zhang Y, Zanardi R, Colombo C, Smeraldi E (2007) Clinical prediction of antidepressant response in mood disorders: linear multivariate vs. neural network models. Psychiatry Res 152:223–231PubMedCrossRefGoogle Scholar
  48. Shafer AB (2006) Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol 62:123–146PubMedCrossRefGoogle Scholar
  49. Sinyor M, Schaffer A, Levitt A (2010) The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatr 55:126–135CrossRefGoogle Scholar
  50. Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere K, Kasper S, Lecrubier Y, Montgomery S, Serretti A, Zohar J, Mendlewicz J, Group for the Study of Resistant Depression (2007) Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry 68:1062–1070PubMedCrossRefGoogle Scholar
  51. Sullivan PF, Neale MC, Kendler KS (2000) Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 157:1552–1562CrossRefGoogle Scholar
  52. Tansey KE, Guipponi M, Perroud N, Bondolfi G, Domenici E, Evans D, Hall SK, Hauser J, Henigsberg N, Hu X, Jerman B, Maier W, Mors O, O’Donovan M, Peters TJ, Placentino A, Rietschel M, Souery D, Aitchison KJ, Craig I, Farmer A, Wendland JR, Malafosse A, Holmans P, Lewis G, Lewis CM, Stensbol TB, Kapur S, Mcguffin P, Uher R (2012) Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLoS Med 9:e1001326PubMedPubMedCentralCrossRefGoogle Scholar
  53. Tansey KE, Guipponi M, Hu X, Domenici E, Lewis G, Malafosse A, Wendland JR, Lewis CM, Mcguffin P, Uher R (2013) Contribution of common genetic variants to antidepressant response. Biol Psychiatry 73:679–682PubMedCrossRefGoogle Scholar
  54. Ten Have M, Lamers F, Wardenaar K, Beekman A, De Jonge P, Van Dorsselaer S, Tuithof M, Kleinjan M, De Graaf R (2016) The identification of symptom-based subtypes of depression: a nationally representative cohort study. J Affect Disord 190:395–406PubMedCrossRefGoogle Scholar
  55. Thase ME (2008) Management of patients with treatment-resistant depression. J Clin Psychiatry 69:e8PubMedCrossRefGoogle Scholar
  56. Uher R, Perroud N, Ng MY, Hauser J, Henigsberg N, Maier W, Mors O, Placentino A, Rietschel M, Souery D, Zagar T, Czerski PM, Jerman B, Larsen ER, Schulze TG, Zobel A, Cohen-Woods S, Pirlo K, Butler AW, Muglia P, Barnes MR, Lathrop M, Farmer A, Breen G, Aitchison KJ, Craig I, Lewis CM, Mcguffin P (2010) Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry 167:555–564PubMedCrossRefGoogle Scholar
  57. Ulbricht CM, Rothschild AJ, Lapane KL (2015) The association between latent depression subtypes and remission after treatment with citalopram: a latent class analysis with distal outcome. J Affect Disord 188:270–277PubMedCrossRefGoogle Scholar
  58. Ulbricht CM, Dumenci L, Rothschild AJ, Lapane KL (2016) Changes in depression subtypes for women during treatment with citalopram: a latent transition analysis. Arch Womens Ment Health 19:769–778PubMedCrossRefGoogle Scholar
  59. Ulbricht CM, Dumenci L, Rothschild AJ, Lapane KL (2018) Changes in depression subtypes among men in STAR*D: a latent transition analysis. Am J Mens Health 12:5–13PubMedCrossRefGoogle Scholar
  60. Van Loo HM, De Jonge P, Romeijn JW, Kessler RC, Schoevers RA (2012) Data-driven subtypes of major depressive disorder: a systematic review. BMC Med 10:156PubMedPubMedCentralCrossRefGoogle Scholar
  61. Van Loo HM, Cai T, Gruber MJ, Li J, De Jonge P, Petukhova M, Rose S, Sampson NA, Schoevers RA, Wardenaar KJ, Wilcox MA, Al-Hamzawi AO, Andrade LH, Bromet EJ, Bunting B, Fayyad J, Florescu SE, Gureje O, Hu C, Huang Y, Levinson D, Medina-Mora ME, Nakane Y, Posada-Villa J, Scott KM, Xavier M, Zarkov Z, Kessler RC (2014) Major depressive disorder subtypes to predict long-term course. Depress Anxiety 31:765–777PubMedPubMedCentralCrossRefGoogle Scholar
  62. Vassos E, Di Forti M, Coleman J, Iyegbe C, Prata D, Euesden J, O’Reilly P, Curtis C, Kolliakou A, Patel H, Newhouse S, Traylor M, Ajnakina O, Mondelli V, Marques TR, Gardner-Sood P, Aitchison KJ, Powell J, Atakan Z, Greenwood KE, Smith S, Ismail K, Pariante C, Gaughran F, Dazzan P, Markus HS, David AS, Lewis CM, Murray RM, Breen G (2017) An examination of polygenic score risk prediction in individuals with first-episode psychosis. Biol Psychiatry 81:470–477PubMedCrossRefGoogle Scholar
  63. Wanders RB, Van Loo HM, Vermunt JK, Meijer RR, Hartman CA, Schoevers RA, Wardenaar KJ, De Jonge P (2016) Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort. Psychol Med 46:3371–3382PubMedCrossRefGoogle Scholar
  64. Wardenaar KJ, Van Loo HM, Cai T, Fava M, Gruber MJ, Li J, De Jonge P, Nierenberg AA, Petukhova MV, Rose S, Sampson NA, Schoevers RA, Wilcox MA, Alonso J, Bromet EJ, Bunting B, Florescu SE, Fukao A, Gureje O, Hu C, Huang YQ, Karam AN, Levinson D, Medina Mora ME, Posada-Villa J, Scott KM, Taib NI, Viana MC, Xavier M, Zarkov Z, Kessler RC (2014) The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity. Psychol Med 44:3289–3302PubMedPubMedCentralCrossRefGoogle Scholar
  65. WHO (2001) World health report 2001. Mental health—new understanding, new hope. WHO, GenevaGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Kautzky
    • 1
  • Rupert Lanzenberger
    • 1
  • Siegfried Kasper
    • 1
    Email author
  1. 1.Department for Psychiatry and PsychotherapyMedical University of ViennaViennaAustria

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