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Chapter 23 - Progress in Biomarkers to Improve Treatment Outcomes in Major Depressive Disorder

Published online by Cambridge University Press:  16 May 2024

Allan Young
Affiliation:
Institute of Psychiatry, King's College London
Marsal Sanches
Affiliation:
Baylor College of Medicine, Texas
Jair C. Soares
Affiliation:
McGovern Medical School, The University of Texas
Mario Juruena
Affiliation:
King's College London
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Summary

Progress in developing new treatments for people with Major Depressive Disorder (MDD) and other mental disorders is hampered by the inability to apply standardized diagnostic tools to supplement clinical findings from DSM-5 or other recognized diagnostic systems. In the absence of tissue biopsies as a source of ‘solid’ biomarkers, mental health researchers have access to ‘liquid’ biopsies as well as neuroimaging, electroencephalography (EEG), and other techniques. Integration of clinical and biomarker features derived from large integrated datasets using machine-learning techniques provides a future for better classification and treatment selection to improve outcomes.

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Publisher: Cambridge University Press
Print publication year: 2024

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References

Committee on a Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy Of Disease. Washington, DC: National Academies Press, 2011. doi: 10.17226/13284.Google Scholar
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:8995.CrossRefGoogle Scholar
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed. Washington, DC: American Psychiatric Association, 2013.Google Scholar
Zimmerman, M, Ellison, W, Young, D, et al. How many different ways do patients meet the diagnostic criteria for major depressive disorder? Compr Psychiatry 2015;56:2934.CrossRefGoogle ScholarPubMed
Califf, RM. Biomarker definitions and their applications. Exp Biol Med 2018;243:213–21.CrossRefGoogle ScholarPubMed
Mayeux, R. Biomarkers: potential uses and limitations. NeuroRX 2004;1:182–8.CrossRefGoogle ScholarPubMed
McAllister-Williams, RH, Arango, C, Blier, P, et al. The identification, assessment and management of difficult-to-treat depression: an international consensus statement. J Affect Disord 2020;267:264–82.CrossRefGoogle ScholarPubMed
Kraus, C, Kadriu, B, Lanzenberger, R, et al. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry 2019;9:127.CrossRefGoogle ScholarPubMed
Rush, AJ, Trivedi, MH, Wisniewski, SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 2006;163:1905–17.CrossRefGoogle ScholarPubMed
Souery, D, Oswald, P, Massat, I, et al. Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry 2007;68:1062–70.CrossRefGoogle ScholarPubMed
Spijker, J, , Bijl R V, de Graaf, R, et al. Determinants of poor 1-year outcome of DSM-III-R major depression in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Acta Psychiatr Scand 2001;103:122–30.CrossRefGoogle ScholarPubMed
Nanni, V, Uher, R, Danese, A. Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatry 2012;169:141–51.CrossRefGoogle ScholarPubMed
Harkness, KL, Bagby, RM, Kennedy, SH. Childhood maltreatment and differential treatment response and recurrence in adult major depressive disorder. J Consult Clin Psychol 2012;80:342–53.CrossRefGoogle ScholarPubMed
Nelson, J, Klumparendt, A, Doebler, P, et al. Childhood maltreatment and characteristics of adult depression: meta-analysis. Br J Psychiatry 2017;210:96104.CrossRefGoogle ScholarPubMed
Quilty, LC, Marshe, V, Lobo, DSS, et al. Childhood abuse history in depression predicts better response to antidepressants with higher serotonin transporter affinity: a pilot investigation. Neuropsychobiology 2016;74:7883.CrossRefGoogle ScholarPubMed
Allen, TA, Lam, RW, Milev, R, et al. Early change in reward and punishment sensitivity as a predictor of response to antidepressant treatment for major depressive disorder: a CAN-BIND-1 report. Psychol Med 2019;49:1629–38.CrossRefGoogle ScholarPubMed
Uher, R, Frey, BN, Quilty, LC, et al. Symptom dimension of interest-activity indicates need for aripiprazole augmentation of escitalopram in major depressive disorder. J Clin Psychiatry 2020;81:20m13229.CrossRefGoogle ScholarPubMed
Uher, R, Perlis, RH, Henigsberg, N, et al. Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms. Psychol Med 2012;42:967–80.CrossRefGoogle ScholarPubMed
Dunlop, K, Rizvi, SJ, Kennedy, SH, et al. Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report. Neuropsychopharmacology 2020;45:1390–7.CrossRefGoogle ScholarPubMed
Hakulinen, C, Elovainio, M, Pulkki-Råback, L, et al. Personality and depressive symptoms: individual participant meta-analysis of 10 cohort studies. Depress Anxiety 2015;32:461–70.CrossRefGoogle ScholarPubMed
Takahashi, M, Shirayama, Y, Muneoka, K, et al. Low openness on the revised NEO personality inventory as a risk factor for treatment-resistant depression. PLoS One 2013;8:e71964.CrossRefGoogle ScholarPubMed
Takahashi, M, Shirayama, Y, Muneoka, K, et al. Personality traits as risk factors for treatment-resistant depression. PLoS One 2013;8:e63756.CrossRefGoogle ScholarPubMed
Allen, TA, Harkness, KL, Lam, RW, et al. Interactions between neuroticism and stressful life events predict response to pharmacotherapy for major depression: a CAN‐BIND 1 report. Personal Ment Health 2021;15(4):273–82.CrossRefGoogle ScholarPubMed
Bagby, RM, Quilty, LC, Segal, ZV, et al. Personality and differential treatment response in major depression: a randomized controlled trial comparing cognitive-behavioural therapy and pharmacotherapy. Can J Psychiatry 2008;53:361370.CrossRefGoogle ScholarPubMed
Chakrabarty, T, McInerney, SJ, Torres, IJ, et al. Cognitive outcomes with sequential escitalopram monotherapy and adjunctive aripiprazole treatment in major depressive disorder: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) report. CNS Drugs 2021;35(3):291304.CrossRefGoogle ScholarPubMed
Etkin, A, Patenaude, B, Song, YJC, et al. A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology 2015;40:1332–42.CrossRefGoogle ScholarPubMed
Ang, YS, Bruder, GE, Keilp, JG, et al. Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial. Psychol Med 2022;52(12):2441–9.CrossRefGoogle ScholarPubMed
Gururajan, A, Clarke, G, Dinan, TG, et al. Molecular biomarkers of depression. Neurosci Biobehav Rev 2016;64:101–33.CrossRefGoogle ScholarPubMed
Gururajan, A, Cryan, JF, Dinan, TG. Molecular biomarkers in depression: toward personalized psychiatric treatment. In Baune, BT, editor. Personalized Psychiatry. Cambridge, MA: Academic Press, 2020;319–38.Google Scholar
Hayashi-Takagi, A, Vawter, MP, Iwamoto, K. Peripheral biomarkers revisited: integrative profiling of peripheral samples for psychiatric research. Biol Psychiatry 2014;75:920–8.CrossRefGoogle ScholarPubMed
Solomon, H V., Cates, KW, Li, KJ. Does obtaining CYP2D6 and CYP2C19 pharmacogenetic testing predict antidepressant response or adverse drug reactions? Psychiatry Res 2019;271:604–13.CrossRefGoogle ScholarPubMed
O’Connell, CP, Goldstein-Piekarski, AN, Nemeroff, CB, et al. Antidepressant outcomes predicted by genetic variation in corticotropin-releasing hormone binding protein. Am J Psychiatry 2018;175:251–61.CrossRefGoogle ScholarPubMed
Fabbri, C, Tansey, KE, Perlis, RH, et al. Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies. Eur Neuropsychopharmacol 2018;28:945–54.CrossRefGoogle ScholarPubMed
Ising, M, Maccarrone, G, Brückl, T, et al. FKBP5 gene expression predicts antidepressant treatment outcome in depression. Int J Mol Sci 2019;20:485.CrossRefGoogle ScholarPubMed
Sarginson, JE, Lazzeroni, LC, Ryan, HS, et al. FKBP5 polymorphisms and antidepressant response in geriatric depression. Am J Med Genet Part B Neuropsychiatr Genet 2010;153B:554–60.CrossRefGoogle ScholarPubMed
Yu, YW-Y, Chen, T-J, Hong, C-J, et al. Association study of the interleukin-1beta (C-511T) genetic polymorphism with major depressive disorder, associated symptomatology, and antidepressant response. Neuropsychopharmacology 2003;28:1182–5.CrossRefGoogle ScholarPubMed
Tadic, A. Association analysis between variants of the interleukin-1beta and the interleukin-1 receptor antagonist gene and antidepressant treatment response in major depression. Neuropsychiatr Dis Treat 2008;4(1):269–76.Google ScholarPubMed
Baune, BT, Dannlowski, U, Domschke, K, et al. The Interleukin 1 beta (IL1B) gene is associated with failure to achieve remission and impaired emotion processing in major depression. Biol Psychiatry 2010;67:543–9.CrossRefGoogle ScholarPubMed
Uher, R, Perroud, N, Ng, MYM, et al. Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry 2010;167:555–64.CrossRefGoogle ScholarPubMed
Powell, TR, Smith, RG, Hackinger, S, et al. DNA methylation in interleukin-11 predicts clinical response to antidepressants in GENDEP. Transl Psychiatry 2013;3:e300.CrossRefGoogle ScholarPubMed
Jha, MK, Minhajuddin, A, Gadad, BS, et al. Interleukin 17 selectively predicts better outcomes with bupropion-SSRI combination: novel T cell biomarker for antidepressant medication selection. Brain Behav Immun 2017;66:103–10.CrossRefGoogle ScholarPubMed
Laterza, OF, Hendrickson, RC, Wagner, JA. Molecular biomarkers. Drug Inf J 2007;41:573–85.CrossRefGoogle Scholar
Belzeaux, R, Lin, R, Ju, C, et al. Transcriptomic and epigenomic biomarkers of antidepressant response. J Affect Disord 2018;233:3644.CrossRefGoogle ScholarPubMed
Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, et al. Omics-based clinical discovery: science, technology, and applications. In Micheel, CM, Nass, SJ, Omenn, GS, editors. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington, DC: National Academies Press, 2012; chapter 2.CrossRefGoogle Scholar
U.S. Food and Drug Administration. Table of Pharmacogenomic Biomarkers in Drug Labeling. www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling.Google Scholar
Meyer, UA. Pharmacogenetics and adverse drug reactions. Lancet 2000;356:1667–71.CrossRefGoogle ScholarPubMed
Maciukiewicz, M, Marshe, VS, Hauschild, A-C, et al. GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. J Psychiatr Res 2018;99:62–8.CrossRefGoogle ScholarPubMed
Lin, E, Kuo, P-H, Liu, Y-L, et al. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front Psychiatry 2018;9:290.CrossRefGoogle ScholarPubMed
Chekroud, AM, Zotti, RJ, Shehzad, Z, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2016;3:243–50.CrossRefGoogle ScholarPubMed
Heinzel, A, Mühlberger, I, Fechete, R, et al. Functional molecular units for guiding biomarker panel design. In Kumar, VD, Tipney, HJ editors. Biomedical Literature Mining: Methods in Molecular Biology (Methods and Protocols). New York: Humana Press, 2014;109–33.Google Scholar
Tam, V, Patel, N, Turcotte, M, et al. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019;20:467–84.CrossRefGoogle ScholarPubMed
Goldstein, JM, Seidman, LJ, Makris, N, et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry 2007;61:935–45.CrossRefGoogle ScholarPubMed
Boyle, EA, Li, YI, Pritchard, JK. An expanded view of complex traits: from polygenic to omnigenic. Cell 2017;169:1177–86.CrossRefGoogle ScholarPubMed
Paulus, MP. Pragmatism instead of mechanism. JAMA Psychiatry 2015;72:631–2.CrossRefGoogle ScholarPubMed
Li, QS, Tian, C, Hinds, D, et al. Genome-wide association studies of antidepressant class response and treatment-resistant depression. Transl Psychiatry 2020;10:360.CrossRefGoogle ScholarPubMed
Lopez, JP, Kos, A, Turecki, G. Major depression and its treatment: microRNAs as peripheral biomarkers of diagnosis and treatment response. Curr Opin Psychiatry 2018;31:716.CrossRefGoogle ScholarPubMed
Belzeaux, R, Lin, R, Turecki, G. Potential use of microRNA for monitoring therapeutic response to antidepressants. CNS Drugs 2017;31:253–62.CrossRefGoogle ScholarPubMed
Yrondi, A, Fiori, LM, Frey, BN, et al. Association between side effects and blood microRNA expression levels and their targeted pathways in patients with major depressive disorder treated by a selective serotonin reuptake inhibitor, escitalopram: a CAN-BIND-1 Report. Int J Neuropsychopharmacol 2021;23:8895.CrossRefGoogle Scholar
Lopez, JP, Fiori, LM, Cruceanu, C, et al. MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes. Nat Commun 2017;8:15497.CrossRefGoogle ScholarPubMed
Saeedi, S, Israel, S, Nagy, C, et al. The emerging role of exosomes in mental disorders. Transl Psychiatry 2019;9:111.CrossRefGoogle ScholarPubMed
Jha, M, Trivedi, M. Personalized antidepressant selection and pathway to novel treatments: clinical utility of targeting inflammation. Int J Mol Sci 2018;19:233.CrossRefGoogle ScholarPubMed
Sproston, NR, Ashworth, JJ. Role of C-reactive protein at sites of inflammation and infection. Front Immunol 2018;9:754.CrossRefGoogle ScholarPubMed
Haapakoski, R, Mathieu, J, Ebmeier, KP, et al. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain Behav Immun 2015;49:206–15.CrossRefGoogle ScholarPubMed
Miller, AH, Raison, CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 2016;16:2234.CrossRefGoogle Scholar
Uher, R, Tansey, KE, Dew, T, et al. An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. Am J Psychiatry 2014;171:1278–86.CrossRefGoogle ScholarPubMed
Jha, MK, Minhajuddin, A, Gadad, BS, et al. Can C-reactive protein inform antidepressant medication selection in depressed outpatients? Findings from the CO-MED trial. Psychoneuroendocrinology 2017;78:105–13.CrossRefGoogle ScholarPubMed
Vogelzangs, N, Duivis, HE, Beekman, ATF, et al. Association of depressive disorders, depression characteristics and antidepressant medication with inflammation. Transl Psychiatry 2012;2:e79.CrossRefGoogle ScholarPubMed
Mocking, RJT, Nap, TS, Westerink, AM, et al. Biological profiling of prospective antidepressant response in major depressive disorder: associations with (neuro)inflammation, fatty acid metabolism, and amygdala-reactivity. Psychoneuroendocrinology 2017;79:8492.CrossRefGoogle ScholarPubMed
Zhang, J, Yue, Y, Thapa, A, et al. Baseline serum C-reactive protein levels may predict antidepressant treatment responses in patients with major depressive disorder. J Affect Disord 2019;250:432–8.CrossRefGoogle ScholarPubMed
Li, X, Sun, N, Yang, C, et al. C-Reactive protein gene variants in depressive symptoms & antidepressants efficacy. Psychiatry Investig 2019;16:940–7.CrossRefGoogle ScholarPubMed
Zwicker, A, Fabbri, C, Rietschel, M, et al. Genetic disposition to inflammation and response to antidepressants in major depressive disorder. J Psychiatr Res 2018;105:1722.CrossRefGoogle ScholarPubMed
Köhler, CA, Freitas, TH, Maes, M, et al. Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr Scand 2017;135:373–87.CrossRefGoogle ScholarPubMed
Osimo, EF, Pillinger, T, Rodriguez, IM, et al. Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain Behav Immun 2020;87:901–9.CrossRefGoogle ScholarPubMed
Köhler, CA, Freitas, TH, Stubbs, B, et al. Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and meta-analysis. Mol Neurobiol 2018;55:41954206.Google ScholarPubMed
Fonseka, TM, MacQueen, GM, Kennedy, SH. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. J Affect Disord 2018;233:2135.CrossRefGoogle ScholarPubMed
Nogovitsyn, N, Muller, M, Souza, R, et al. Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report. Neuropsychopharmacology 2020;45:283–91.CrossRefGoogle ScholarPubMed
MacQueen, GM, Yucel, K, Taylor, VH, et al. Posterior hippocampal volumes are associated with remission rates in patients with major depressive disorder. Biol Psychiatry 2008;64:880–3.CrossRefGoogle ScholarPubMed
Suh, JS, Schneider, MA, Minuzzi, L, et al. Cortical thickness in major depressive disorder: a systematic review and meta-analysis. Prog Neuro-Psychopharmacology Biol Psychiatry 2019;88:287302.CrossRefGoogle ScholarPubMed
Suh, JS, Minuzzi, L, Raamana, PR, et al. An investigation of cortical thickness and antidepressant response in major depressive disorder: a CAN-BIND study report. NeuroImage Clin 2020;25:102178.CrossRefGoogle ScholarPubMed
Davis, AD, Hassel, S, Arnott, SR, et al. White matter indices of medication response in major depression: a diffusion tensor imaging study. Biol Psychiatry Cogn Neurosci Neuroimaging 2019;4:913–24.Google ScholarPubMed
Seminowicz, DA, Mayberg, HS, McIntosh, AR, et al. Limbic-frontal circuitry in major depression: a path modeling metanalysis. Neuroimage 2004;22:409–18.CrossRefGoogle ScholarPubMed
Furtado, CP, Hoy, KE, Maller, JJ, et al. An investigation of medial temporal lobe changes and cognition following antidepressant response: a prospective rTMS study. Brain Stimul 2013;6:346–54.CrossRefGoogle ScholarPubMed
Straub, J, Metzger, CD, Plener, PL, et al. Successful group psychotherapy of depression in adolescents alters fronto-limbic resting-state connectivity. J Affect Disord 2017;209:135–9.CrossRefGoogle ScholarPubMed
Fu, CHY, Williams, SCR, Cleare, AJ, et al. Neural responses to sad facial expressions in major depression following cognitive behavioral therapy. Biol Psychiatry 2008;64:505–12.CrossRefGoogle ScholarPubMed
Langenecker, SA, Kennedy, SE, Guidotti, LM, et al. Frontal and limbic activation during inhibitory control predicts treatment response in major depressive disorder. Biol Psychiatry 2007;62:1272–80.CrossRefGoogle ScholarPubMed
Ruhé, HG, Booij, J, Veltman, DJ, et al. Successful pharmacologic treatment of major depressive disorder attenuates amygdala activation to negative facial expressions: a functional magnetic resonance imaging study. J Clin Psychiatry 2012;73:451–9.CrossRefGoogle ScholarPubMed
Szczepanik, J, Nugent, AC, Drevets, WC, et al. Amygdala response to explicit sad face stimuli at baseline predicts antidepressant treatment response to scopolamine in major depressive disorder. Psychiatry Res Neuroimaging 2016;254:6773.CrossRefGoogle ScholarPubMed
Cullen, KR, Klimes-Dougan, B, Vu, DP, et al. Neural correlates of antidepressant treatment response in adolescents with major depressive disorder. J Child Adolesc Psychopharmacol 2016;26:705–12.CrossRefGoogle ScholarPubMed
Drysdale, AT, Grosenick, L, Downar, J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23:2838.CrossRefGoogle ScholarPubMed
Wang, L, Xia, M, Li, K, et al. The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 2015;36:768–78.CrossRefGoogle ScholarPubMed
Miller, JM, Schneck, N, Siegle, GJ, et al. fMRI response to negative words and SSRI treatment outcome in major depressive disorder: a preliminary study. Psychiatry Res Neuroimaging 2013;214:296305.CrossRefGoogle ScholarPubMed
Gyurak, A, Patenaude, B, Korgaonkar, MS, et al. Frontoparietal activation during response inhibition predicts remission to antidepressants in patients with major depression. Biol Psychiatry 2016;79:274–81.CrossRefGoogle ScholarPubMed
Guo, W, Liu, F, Xue, Z, et al. Alterations of the amplitude of low-frequency fluctuations in treatment-resistant and treatment-response depression: a resting-state fMRI study. Prog Neuro-Psychopharmacology Biol Psychiatry 2012;37:153–60.CrossRefGoogle ScholarPubMed
Keedwell, PA, Drapier, D, Surguladze, S, et al. Subgenual cingulate and visual cortex responses to sad faces predict clinical outcome during antidepressant treatment for depression. J Affect Disord 2010;120:120–5.CrossRefGoogle ScholarPubMed
Furey, ML, Drevets, WC, Szczepanik, J, et al. Pretreatment differences in BOLD response to emotional faces correlate with antidepressant response to scopolamine. Int J Neuropsychopharmacol 2015;18:pyv028.CrossRefGoogle ScholarPubMed
Costafreda, SG, Chu, C, Ashburner, J, et al. Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 2009;4:e6353.CrossRefGoogle ScholarPubMed
Baeken, C, De Raedt, R, Van Hove, C, et al. HF-rTMS treatment in medication-resistant melancholic depression: results from 18 FDG-PET brain imaging. CNS Spectr 2009;14:439–48.CrossRefGoogle Scholar
Li, C-T, Wang, S-J, Hirvonen, J, et al. Antidepressant mechanism of add-on repetitive transcranial magnetic stimulation in medication-resistant depression using cerebral glucose metabolism. J Affect Disord 2010;127:219–29.CrossRefGoogle ScholarPubMed
Baeken, C, Marinazzo, D, Everaert, H, et al. The impact of accelerated HF-rTMS on the subgenual anterior cingulate cortex in refractory unipolar major depression: insights from 18FDG PET brain imaging. Brain Stimul 2015;8:808–15.Google ScholarPubMed
Rizvi, SJ, Salomons, TV, Konarski, JZ, et al. Neural response to emotional stimuli associated with successful antidepressant treatment and behavioral activation. J Affect Disord 2013;151:573–81.CrossRefGoogle ScholarPubMed
Mayberg, HS, Brannan, SK, Tekell, JL, et al. Regional metabolic effects of fluoxetine in major depression: serial changes and relationship to clinical response. Biol Psychiatry 2000;48:830–43.CrossRefGoogle ScholarPubMed
Conway, CR, Chibnall, JT, Gebara, MA, et al. Association of cerebral metabolic activity changes with vagus nerve stimulation antidepressant response in treatment-resistant depression. Brain Stimul 2013;6:788–97.CrossRefGoogle ScholarPubMed
McGrath, CL, Kelley, ME, Holtzheimer, PE III, et al. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 2013;70:821–9.CrossRefGoogle Scholar
Widge, AS, Taha Bilge, M, Montana, R, et al. electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis. Am J Psychiatry 2019;176:4456.CrossRefGoogle ScholarPubMed
Wu, W, Zhang, Y, Jiang, J, et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol 2020;38:439–47.CrossRefGoogle ScholarPubMed
Rolle, CE, Fonzo, GA, Wu, W, et al. Cortical connectivity moderators of antidepressant vs placebo treatment response in major depressive disorder: secondary analysis of a randomized clinical trial. JAMA Psychiatry 2020;77:397408.CrossRefGoogle ScholarPubMed
Zhdanov, A, Atluri, S, Wong, W, et al. Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression. JAMA Netw Open 2020;3:e1918377.CrossRefGoogle ScholarPubMed
Farzan, F, Atluri, S, Frehlich, M, et al. Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the Canadian Biomarker Integration Network in Depression. Sci Rep 2017;7:111.CrossRefGoogle ScholarPubMed
MacQueen, GM, Hassel, S, Arnott, SR, et al. The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols. J Psychiatry Neurosci 2019;44:223–36.CrossRefGoogle ScholarPubMed
Thompson, PM, Stein, JL, Medland, SE, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 2014;8:153–82.CrossRefGoogle ScholarPubMed
Strawbridge, R, Young, AH, Cleare, AJ. Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr Dis Treat 2017;13:1245–62.CrossRefGoogle ScholarPubMed
Lenze, EJ, Nicol, GE, Barbour, DL, et al. Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders. J Psychiatry Neurosci 2021;46:E97E110.CrossRefGoogle Scholar
Andreazza, AC, Laksono, I, Fernandes, BS, et al. Guidelines for the standardized collection of blood-based biomarkers in psychiatry: steps for laboratory validity – a consensus of the Biomarkers Task Force from the WFSBP. World J Biol Psychiatry 2019;20:340–51.CrossRefGoogle ScholarPubMed
Longo, DL, Drazen, JM. Data sharing. N Engl J Med 2016;374:276–7.CrossRefGoogle ScholarPubMed
Lefaivre, S, Behan, B, Vaccarino, A, et al. Big data needs big governance: best practices from Brain-CODE, the Ontario-Brain Institute’s neuroinformatics platform. Front Genet 2019; 10:191.CrossRefGoogle ScholarPubMed
Lam, RW, Milev, R, Rotzinger, S, et al. Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort. BMC Psychiatry 2016;16:105.CrossRefGoogle ScholarPubMed
Kennedy, SH, Lam, RW, Rotzinger, S, et al. Symptomatic and functional outcomes and early prediction of response to escitalopram monotherapy and sequential adjunctive aripiprazole therapy in patients with major depressive disorder: a CAN-BIND-1 report. J Clin Psychiatry 2019;80:18m12202.CrossRefGoogle ScholarPubMed
Habert, J, Katzman, MA, Oluboka, OJ, et al. Functional recovery in major depressive disorder. Prim Care Companion CNS Disord 2016;18(5). doi: 10.4088/PCC.15r01926.Google ScholarPubMed
Dudek, D, Rybakowski, JK, Siwek, M, et al. Risk factors of treatment resistance in major depression: association with bipolarity. J Affect Disord 2010;126:268–71.CrossRefGoogle ScholarPubMed
Souery, D, Oswald, P, Massat, I, et al. Clinical factors associated with treatment resistance in major depressive disorder. J Clin Psychiatry 2007;68:1062–70.CrossRefGoogle ScholarPubMed
Spijker, J, , Bijl R V., de Graaf, R, et al. Determinants of poor 1-year outcome of DSM-III-R major depression in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Acta Psychiatr Scand 2001;103:122–30.CrossRefGoogle ScholarPubMed
Trivedi, MH, Rush, AJ, Wisniewski, SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006;163:2840.CrossRefGoogle ScholarPubMed
Katon, W, Ũntzer J, Russo J. Major depression: The importance of clinical characteristics and treatment response to prognosis. Depress Anxiety 2010;27:1926.CrossRefGoogle ScholarPubMed
Jha, MK, Minhajuddin, A, Gadad, BS, et al. Interleukin 17 selectively predicts better outcomes with bupropion-SSRI combination: novel T cell biomarker for antidepressant medication selection. Brain Behav Immun 2017;66:103–10.CrossRefGoogle ScholarPubMed
Cattaneo, A, Gennarelli, M, Uher, R, et al. Candidate genes expression profile associated with antidepressants response in the GENDEP study: differentiating between baseline ‘predictors’ and longitudinal ‘targets’. Neuropsychopharmacology 2013;38:377–85.Google ScholarPubMed
Mrazek, DA, Rush, AJ, Biernacka, JM, et al. SLC6A4 variation and citalopram response. Am J Med Genet Part B Neuropsychiatr Genet 2009;150B:341–51.CrossRefGoogle ScholarPubMed
Porcelli, S, Fabbri, C, Serretti, A. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with antidepressant efficacy. Eur Neuropsychopharmacol 2012;22:239–58.CrossRefGoogle ScholarPubMed
Maron, E, Tammiste, A, Kallassalu, K, et al. Serotonin transporter promoter region polymorphisms do not influence treatment response to escitalopram in patients with major depression. Eur Neuropsychopharmacol 2009;19:451–6.CrossRefGoogle Scholar
Perlis, RH, Fijal, B, Dharia, S, et al. Failure to replicate genetic associations with antidepressant treatment response in duloxetine-treated patients. Biol Psychiatry 2010;67:1110–13.CrossRefGoogle ScholarPubMed
McMahon, FJ, Buervenich, S, Charney, D, et al. Variation in the gene encoding the serotonin 2 A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet 2006;78:804–14.CrossRefGoogle Scholar
Peters, EJ, Slager, SL, Jenkins, GD, et al. Resequencing of serotonin-related genes and association of tagging SNPs to citalopram response. Pharmacogenet Genomics 2009;19:110.CrossRefGoogle ScholarPubMed
Lucae, S, Ising, M, Horstmann, S, et al. HTR2A gene variation is involved in antidepressant treatment response. Eur Neuropsychopharmacol 2010;20:65–8.CrossRefGoogle ScholarPubMed
Uher, R, Huezo-Diaz, P, Perroud, N, et al. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenomics J 2009;9:225–33.CrossRefGoogle ScholarPubMed
Horstmann, S, Lucae, S, Menke, A, et al. Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment. Neuropsychopharmacology 2010;35:727–40.CrossRefGoogle ScholarPubMed
Baune, BT, Hohoff, C, Berger, K, et al. Association of the COMT val158 met variant with antidepressant treatment response in major depression. Neuropsychopharmacology 2008;33:924–32.CrossRefGoogle Scholar
Anttila, S, Huuhka, K, Huuhka, M, et al. Catechol-O-methyltransferase (COMT) polymorphisms predict treatment response in electroconvulsive therapy. Pharmacogenomics J 2008;8:113–16.CrossRefGoogle ScholarPubMed
Paddock, S, Laje, G, Charney, D, et al. Association of GRIK4 with outcome of antidepressant treatment in the STAR*D cohort. Am J Psychiatry 2007;164:1181–8.CrossRefGoogle ScholarPubMed
Pu, M, Zhang, Z, Xu, Z, et al. Influence of genetic polymorphisms in the glutamatergic and GABAergic systems and their interactions with environmental stressors on antidepressant response. Pharmacogenomics 2013;14:277–88.CrossRefGoogle ScholarPubMed
Serretti, A, Chiesa, A, Crisafulli, C, et al. Failure to replicate influence of GRIK4 and GNB3 polymorphisms on treatment outcome in major depression. Neuropsychobiology 2012;65:70–5.CrossRefGoogle ScholarPubMed
Tadić, A, Müller, MJ, Rujescu, D, et al. The MAOA T941G polymorphism and short-term treatment response to mirtazapine and paroxetine in major depression. Am J Med Genet Part B Neuropsychiatr Genet 2007;144B:325–31.CrossRefGoogle ScholarPubMed
Ortiz, R, Niciu, MJ, Lukkahati, N, et al. Shank3 as a potential biomarker of antidepressant response to ketamine and its neural correlates in bipolar depression. J Affect Disord 2015;172:307–11.CrossRefGoogle ScholarPubMed
Tadić, A, Müller-Engling, L, Schlicht, KF, et al. Methylation of the promoter of brain-derived neurotrophic factor exon IV and antidepressant response in major depression. Mol Psychiatry 2014;19:281–3.CrossRefGoogle ScholarPubMed
Colle, R, Deflesselle, E, Martin, S, et al. BDNF/TRKB/P75NTR polymorphisms and their consequences on antidepressant efficacy in depressed patients. Pharmacogenomics 2015;16:9971013.CrossRefGoogle ScholarPubMed
Schatzberg, AF, DeBattista, C, Lazzeroni, LC, et al. ABCB1 genetic effects on antidepressant outcomes: a report from the iSPOT-D trial. Am J Psychiatry 2015;172(8);751–9.CrossRefGoogle ScholarPubMed
Uhr, M, Tontsch, A, Namendorf, C, et al. Polymorphisms in the drug transporter gene ABCB1 predict antidepressant treatment response in depression. Neuron 2008;57:203–9.CrossRefGoogle ScholarPubMed
Jha, MK, Minhajuddin, A, Gadad, BS, et al. Platelet-derived growth factor as an antidepressant treatment selection biomarker: higher levels selectively predict better outcomes with bupropion-SSRI combination. Int J Neuropsychopharmacol 2017;20:919–27.CrossRefGoogle ScholarPubMed
Jha, M, Minhajuddin, A, Gadad, B, et al. Blood brain barrier dysfunction selectively predicts poorer outcomes with SSRI monotherapy vs. antidepressant combinations: clinical utility of novel astrocytic marker. Biol Psychiatry 2018;83:S28.CrossRefGoogle Scholar
Lopez, JP, Mamdani, F, Labonte, B, et al. Epigenetic regulation of BDNF expression according to antidepressant response. Mol Psychiatry 2013;18:398–9.CrossRefGoogle ScholarPubMed
Lopez, JP, Lim, R, Cruceanu C, et al. miR-1202 is a primate-specific and brain-enriched microRNA involved in major depression and antidepressant treatment. Nat Med 2014;20:764–8.CrossRefGoogle ScholarPubMed
Fiori, LM, Lopez, JP, Richard-Devantoy, S, et al. Investigation of miR-1202, miR-135a, and miR-16 in major depressive disorder and antidepressant response. Int J Neuropsychopharmacol 2017;20:619–23.CrossRefGoogle ScholarPubMed
He, S, Liu, X, Jiang, K, et al. Alterations of microRNA-124 expression in peripheral blood mononuclear cells in pre- and post-treatment patients with major depressive disorder. J Psychiatr Res 2016;78:6571.CrossRefGoogle ScholarPubMed
Marshe, VS, Islam, F, Maciukiewicz, M, et al. Validation study of microRNAs previously associated with antidepressant response in older adults treated for late-life depression with venlafaxine. Prog Neuro-Psychopharmacology Biol Psychiatry 2020;100:109867.CrossRefGoogle ScholarPubMed
Ju, C, Fiori, LM, Belzeaux, R, et al. Integrated genome-wide methylation and expression analyses reveal functional predictors of response to antidepressants. Trsansl Psychiatry 2019;9:254.CrossRefGoogle ScholarPubMed
Belzeaux, R, Gorgievski, V, Fiori, LM, et al. GPR56/ADGRG1 is associated with response to antidepressant treatment. Nat Commun 2020;11:1635.CrossRefGoogle ScholarPubMed
Vakili, K, Pillay, SS, Lafer, B, et al. Hippocampal volume in primary unipolar major depression: a magnetic resonance imaging study. Biol Psychiatry 2000;47:1087–90.CrossRefGoogle ScholarPubMed
Hsieh, M-H, McQuoid, DR, Levy, RM, et al. Hippocampal volume and antidepressant response in geriatric depression. Int J Geriatr Psychiatry 2002;17:519525.CrossRefGoogle ScholarPubMed
Frodl, T, Meisenzahl, EM, Zetzsche, T, et al. Hippocampal and amygdala changes in patients with major depressive disorder and healthy controls during a 1-year follow-up. J Clin Psychiatry 2004;65:492–9.CrossRefGoogle ScholarPubMed
Frodl, T, Jäger, M, Smajstrlova, I, et al. Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: a 3-year prospective magnetic resonance imaging study. J Psychiatry Neurosci 2008;33:423–30.Google ScholarPubMed
Abbott, CC, Jones, T, Lemke, NT, et al. Hippocampal structural and functional changes associated with electroconvulsive therapy response. Transl Psychiatry 2014;4:e483.CrossRefGoogle ScholarPubMed
Li, C-T, Li, C-P, Chou, K-H, et al. Structural and cognitive deficits in remitting and non-remitting recurrent depression: a voxel-based morphometric study. Neuroimage 2010;50:347–56.CrossRefGoogle ScholarPubMed
ten Doesschate, F, van Eijndhoven, P, Tendolkar, I, et al. Pre-treatment amygdala volume predicts electroconvulsive therapy response. Front Psychiatry 2014;5:169.CrossRefGoogle ScholarPubMed
Baldwin, R, Jeffiries, S, Jackson, A, et al. Treatment response in late-onset depression: Relationship to neuropsychological, neuroradiological and vascular risk factors. Psychol Med 2004;34:125–36.CrossRefGoogle ScholarPubMed
, Iosifescu D V., Renshaw, PF, Lyoo, IK, et al. Brain white-matter hyperintensities and treatment outcome in major depressive disorder. Br J Psychiatry 2006;188:180–5.Google Scholar
Toki, S, Okamoto, Y, Onoda, K, et al. Hippocampal activation during associative encoding of word pairs and its relation to symptomatic improvement in depression: a functional and volumetric MRI study. J Affect Disord 2014;152154:462–7.Google ScholarPubMed
Mayberg, HS, Brannan, SK, Tekell, JL, et al. Regional metabolic effects of fluoxetine in major depression: serial changes and relationship to clinical response. Biol Psychiatry 2000;48:830–43.CrossRefGoogle ScholarPubMed
Goldapple, K, Segal, Z, Garson, C, et al. Modulation of cortical-limbic pathways in major depression. Arch Gen Psychiatry 2004;61:34.CrossRefGoogle ScholarPubMed
Baskaran, A, Farzan, F, Milev, R, et al. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: a pilot study. J Affect Disord 2018;227:542–9.CrossRefGoogle ScholarPubMed

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