Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 9:65
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 172:743–750
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 189:224–232
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 16:76–95
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:e553
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 19:1392–1396
Carvalho AF, Berk M, Hyphantis TN, Mcintyre RS (2014) The integrative management of treatment-resistant depression: a comprehensive review and perspectives. Psychother Psychosom 83:70–88
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 46:277–284
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 3:243–250
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 74:370–378
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 8:1580–1591
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 391:1357–1366
Cohen ZD, Derubeis RJ (2018) Treatment selection in depression. Annu Rev Clin Psychol 14:209–236
Costafreda SG, Chu C, Ashburner J, Fu CH (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4:e6353
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:e83875
Dold M, Kasper S (2016) Evidence-based pharmacotherapy of treatment-resistant unipolar depression. Int J Psychiatry Clin Pract 21:1–11
Fried EI (2017) The 52 symptoms of major depression: lack of content overlap among seven common depression scales. J Affect Disord 208:191–197
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 28:1354–1367
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 75:128–134
Gratten J, Wray NR, Keller MC, Visscher PM (2014) Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat Neurosci 17:782–790
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 75:201–209
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 28:478–482
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 78:94–102
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 8:5530
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 254:8–11
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 25:441–453
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 78:215–222
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. https://doi.org/10.4088/JCP.16m11385
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 18:5976–5989
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:e40968
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):406–412
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 99:62–68
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 26:1320–1326
Marquand AF, Mourao-Miranda J, Brammer MJ, Cleare AJ, Fu CH (2008) Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport 19:1507–1511
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. https://doi.org/10.1002/mpr.1569
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 56:809–813
Passos IC, Mwangi B, Kapczinski F (2016) Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry 3:13–15
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 30:1056–1067
Perlis RH (2013) A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry 74:7–14
Perlis RH (2016) Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry 15:228–235
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 65:785–791
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 67:1110–1113
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 133:137–149
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:pyv042
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 78:278–286
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 22:453–468
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 152:223–231
Shafer AB (2006) Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol 62:123–146
Sinyor M, Schaffer A, Levitt A (2010) The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatr 55:126–135
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 68:1062–1070
Sullivan PF, Neale MC, Kendler KS (2000) Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 157:1552–1562
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:e1001326
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 73:679–682
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 190:395–406
Thase ME (2008) Management of patients with treatment-resistant depression. J Clin Psychiatry 69:e8
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 167:555–564
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 188:270–277
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 19:769–778
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 12:5–13
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 10:156
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 31:765–777
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 81:470–477
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 46:3371–3382
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 44:3289–3302
WHO (2001) World health report 2001. Mental health—new understanding, new hope. WHO, Geneva
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kautzky, A., Lanzenberger, R., Kasper, S. (2019). Big Data Guided Interventions: Predicting Treatment Response. In: Passos, I., Mwangi, B., Kapczinski, F. (eds) Personalized Psychiatry. Springer, Cham. https://doi.org/10.1007/978-3-030-03553-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-03553-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03552-5
Online ISBN: 978-3-030-03553-2
eBook Packages: MedicineMedicine (R0)