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Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics


Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review “dimensional,” “categorical,” and “hybrid” approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

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Fig. 1: Approaches to parsing heterogeneity in depression.
Fig. 2: Transdiagnostic psychopathology brain connectivity-behavior dimensions.
Fig. 3: Brain connectivity-behavior dimensions of depression define novel depression subtypes that predict treatment response to TMS.
Fig. 4: Integrating neuroimaging and genetic data to uncover intermediate endophenotypes and novel depression subgroups.
Fig. 5: Polygenic risk scores for anhedonia predict psychiatric neuroimaging phenotypes and spatial patterns of gene expression for schizophrenia risk genes predict schizotypy-associated myelination.


  1. 1.

    American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Publishing 2013.

  2. 2.

    Freeman HL. Historical and nosological aspects of dysthymia. Acta Psychiatr Scand Suppl. 1994;383:7–11.

    CAS  Google Scholar 

  3. 3.

    Evans KM. ‘Interrupted by fits of weeping’: Cicero’s major depressive disorder and the death of Tullia. Hist Psychiatry 2007;18:81–102.

    Google Scholar 

  4. 4.

    Burton R. The Anatomy of Melancholy. Thomas Tegg; 1845.

  5. 5.

    Labonté B, Engmann O, Purushothaman I, Menard C, Wang J, Tan C, et al. Sex-specific transcriptional signatures in human depression. Nat Med 2017;23:1102–11.

    Google Scholar 

  6. 6.

    Blazer DG, Kessler RC, McGonagle KA, Swartz MS. The prevalence and distribution of major depression in a national community sample: the National Comorbidity Survey. Am J Psychiatry. 1994;151:979–86.

    CAS  Google Scholar 

  7. 7.

    Kessler RC, Nelson CB, McGonagle KA, Liu J, Swartz M, Blazer DG. Comorbidity of DSM–III–R major depressive disorder in the general population: results from the US national comorbidity survey. Br J Psychiatry. 1996;168:17–30.

    Google Scholar 

  8. 8.

    Post RM, Denicoff KD, Leverich GS, Altshuler LL, Frye MA, Suppes TM, et al. Morbidity in 258 bipolar outpatients followed for 1 year with daily prospective ratings on the NIMH life chart method. J Clin Psychiatry. 2003;64:680–90. quiz 738–739.

    Google Scholar 

  9. 9.

    Casey BJ, Craddock N, Cuthbert BN, Hyman SE, Lee FS, Ressler KJDSM-5. and RDoC: progress in psychiatry research? Nat Rev Neurosci. 2013;14:810–4.

    CAS  Google Scholar 

  10. 10.

    Insel TR, Cuthbert BN. Brain disorders? Precisely. Science 2015;348:499–500.

    CAS  Google Scholar 

  11. 11.

    Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.

    Google Scholar 

  12. 12.

    Wang PS, Berglund PA, Olfson M, Kessler RC. Delays in initial treatment contact after first onset of a mental disorder. Health Serv Res. 2004;39:393–415.

    Google Scholar 

  13. 13.

    Lee S, Fung SC, Tsang A, Zhang MY, Huang YQ, He YL, et al. Delay in initial treatment contact after first onset of mental disorders in metropolitan China. Acta Psychiatr Scand. 2007;116:10–16.

    CAS  Google Scholar 

  14. 14.

    Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, 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.

    Google Scholar 

  15. 15.

    Thase M, Connolly KR. Unipolar depression in adults: Choosing treatment for resistant depression. In: Post TW, editor. Waltham: UpToDate; 2019.

  16. 16.

    Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003;289:3095–105.

    Google Scholar 

  17. 17.

    Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2018 National Survey on Drug Use and Health. Rockville: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2019.

  18. 18.

    Chisholm D, Sweeny K, Sheehan P, Rasmussen B, Smit F, Cuijpers P, et al. Scaling-up treatment of depression and anxiety: a global return on investment analysis. Lancet Psychiatry 2016;3:415–24.

    Google Scholar 

  19. 19.

    James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1789–858.

    Google Scholar 

  20. 20.

    U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. National Survey on Drug Use and Health (NSDUH-2018); 2018.

  21. 21.

    Freedland KE, Carney RM, Rich MW. Effect of depression on prognosis in heart failure. Heart Fail. Clin. 2011;7:11–21.

    Google Scholar 

  22. 22.

    Pan A, Sun Q, Okereke OI, Rexrode KM, Hu FB. Depression and risk of stroke morbidity and mortality: a meta-analysis and systematic review. JAMA 2011;306:1241–9.

    CAS  Google Scholar 

  23. 23.

    Frasure-Smith N, Lespérance F, Talajic M. The impact of negative emotions on prognosis following myocardial infarction: is it more than depression? Health Psychol 1995;14:388–98.

    CAS  Google Scholar 

  24. 24.

    MacMahon KMA, Lip GYH. Psychological factors in heart failure: a review of the literature. Arch Intern Med. 2002;162:509–16.

    Google Scholar 

  25. 25.

    Centers for Disease Control and Prevention. Fatal Injury and Nonfatal Injury. Web-based Injury Statistics Query and Reporting System (WISQARS) [Online]. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention (producer); 2018.

  26. 26.

    Takahashi Y. Depression and suicide. Jpn Med Assoc J 2001;44:359–63.

    Google Scholar 

  27. 27.

    Cavanagh JTO, Carson AJ, Sharpe M, Lawrie SM. Psychological autopsy studies of suicide: a systematic review. Psychol Med 2003;33:395–405.

    CAS  Google Scholar 

  28. 28.

    American Association of Suicidology. Facts about suicide and depression based on 2010 data. 2012.

  29. 29.

    McIntosh AM, Sullivan PF, Lewis CM. Uncovering the Genetic Architecture of Major Depression. Neuron 2019;102:91–103.

    CAS  Google Scholar 

  30. 30.

    Sullivan PF, Daly MJ, Ripke S, Lewis CM, Lin D-Y, Wray NR, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 2013;18:497–511.

    Google Scholar 

  31. 31.

    Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 2018;50:668–81.

    CAS  Google Scholar 

  32. 32.

    Flint J, Kendler KS. The genetics of major depression. Neuron 2014;81:484–503.

    CAS  Google Scholar 

  33. 33.

    Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 2019;22:343–52.

    CAS  Google Scholar 

  34. 34.

    Lynch CJ, Gunning FM, Liston C. Causes and consequences of diagnostic heterogeneity in depression: paths to discovering novel biological depression subtypes. Biol Psychiatry. 2020.

  35. 35.

    Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23:584–601.

    Google Scholar 

  36. 36.

    Beijers L, Wardenaar KJ, van Loo HM, Schoevers RA. Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping. Mol Psychiatry 2019;24:888–900.

    Google Scholar 

  37. 37.

    Telles-Correia D, Marques JG. Melancholia before the twentieth century: fear and sorrow or partial insanity? Front Psychol 2015;6:81.

    Google Scholar 

  38. 38.

    Healy D. From mania to bipolar disorder. In: Yatham LN, Maj M, editors. Bipolar Disorder: Clinical and Neurobiological Foundations. Chichester: John Wiley & Sons, Ltd; 2010. p. 1–7

  39. 39.

    Kraepelin E Manic-Depressive Insanity and Paranoia (1921). Translated by Barclay RM. In: Robertson GM, editor. Salem: Reprinted in facsimile by the Ayer Company; 1987.

  40. 40.

    Pichot P. [DSM-III: the 3d edition of the Diagnostic and Statistical Manual of Mental Disorders from the American Psychiatric Association]. Rev Neurol. 1986;142:489–99.

  41. 41.

    Blashfield RK, Keeley JW, Flanagan EH, Miles SR. The cycle of classification: DSM-I through DSM-5. Annu Rev Clin Psychol. 2014;10:25–51.

    Google Scholar 

  42. 42.

    Caspi A, Houts RM, Ambler A, Danese A, Elliott ML, Hariri A, et al. Longitudinal Assessment of Mental Health Disorders and Comorbidities Across 4 Decades Among Participants in the Dunedin Birth Cohort Study. JAMA Netw Open. 2020;3:e203221.

    Google Scholar 

  43. 43.

    Post RM. Kindling and sensitization as models for affective episode recurrence, cyclicity, and tolerance phenomena. Neurosci Biobehav Rev. 2007;31:858–73.

    Google Scholar 

  44. 44.

    Lebowitz BD, Pearson JL, Schneider LS, Reynolds CF 3rd, Alexopoulos GS, Bruce ML, et al. Diagnosis and treatment of depression in late life. Consensus statement update. JAMA 1997;278:1186–90.

    CAS  Google Scholar 

  45. 45.

    Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D, Charlson M. ‘Vascular depression’ hypothesis. Arch Gen Psychiatry 1997;54:915–22.

    CAS  Google Scholar 

  46. 46.

    Toenders YJ, van Velzen LS, Heideman IZ, Harrison BJ, Davey CG, Schmaal L. Neuroimaging predictors of onset and course of depression in childhood and adolescence: a systematic review of longitudinal studies. Dev Cogn Neurosci. 2019;39:100700.

    Google Scholar 

  47. 47.

    Dohm K, Redlich R, Zwitserlood P, Dannlowski U. Trajectories of major depression disorders: a systematic review of longitudinal neuroimaging findings. Aust N. Z J Psychiatry. 2017;51:441–54.

    Google Scholar 

  48. 48.

    Zisook S, Rush AJ, Lesser I, Wisniewski SR, Trivedi M, Husain MM, et al. Preadult onset vs. adult onset of major depressive disorder: a replication study. Acta Psychiatr Scand. 2007;115:196–205.

    CAS  Google Scholar 

  49. 49.

    Zisook S, Rush AJ, Albala A, Alpert J, Balasubramani GK, Fava M, et al. Factors that differentiate early vs. later onset of major depression disorder. Psychiatry Res 2004;129:127–40.

    Google Scholar 

  50. 50.

    Sung SC, Wisniewski SR, Balasubramani GK, Zisook S, Kurian B, Warden D, et al. Does early-onset chronic or recurrent major depression impact outcomes with antidepressant medications? A CO-MED trial report. Psychol Med 2013;43:945–60.

    CAS  Google Scholar 

  51. 51.

    Kozel FA, Trivedi MH, Wisniewski SR, Miyahara S, Husain MM, Fava M, et al. Treatment Outcomes for Older Depressed Patients With Earlier Versus Late Onset of First Depressive Episode: A Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Report. Am J Geriatr Psychiatry. 2008;16:58–64.

    Google Scholar 

  52. 52.

    Perlis RH, Dennehy EB, Miklowitz DJ, DelBello MP, Ostacher M, Calabrese JR, et al. Retrospective age at onset of bipolar disorder and outcome during two-year follow-up: results from the STEP-BD study. Bipolar Disord 2009;11:391–400.

    Google Scholar 

  53. 53.

    Weissman MM. Cross-National epidemiology of major depression and bipolar disorder. JAMA: J Am Med Assoc. 1996;276:293.

    CAS  Google Scholar 

  54. 54.

    Lewinsohn PM, Clarke GN, Seeley JR, Rohde P. Major depression in community adolescents: age at onset, episode duration, and time to recurrence. J Am Acad Child Adolesc Psychiatry. 1994;33:809–18.

    CAS  Google Scholar 

  55. 55.

    Kobrosly RW, van Wijngaarden E, Seplaki CL, Cory-Slechta DA, Moynihan J. Depressive symptoms are associated with allostatic load among community-dwelling older adults. Physiol Behav 2014;123:223–30.

    CAS  Google Scholar 

  56. 56.

    Strain JJ. The psychobiology of stress, depression, adjustment disorders and resilience. World J Biol Psychiatry. 2018;19:S14–S20.

    Google Scholar 

  57. 57.

    McEwen BS, Rasgon NL. The Brain and Body on Stress: Allostatic Load and Mechanisms for Depression and Dementia. In: Strain JJ, Blumenfield M, editors. Depression as a Systemic Illness. Oxford University Press; 2018.

  58. 58.

    Patel V, Burns JK, Dhingra M, Tarver L, Kohrt BA, Lund C. Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry 2018;17:76–89.

    Google Scholar 

  59. 59.

    Kendler KS, Gardner CO. Sex differences in the pathways to major depression: a study of opposite-sex twin pairs. Am J Psychiatry. 2014;171:426–35.

    Google Scholar 

  60. 60.

    Akincigil A, Olfson M, Siegel M, Zurlo KA, Walkup JT, Crystal S. Racial and ethnic disparities in depression care in community-dwelling elderly in the United States. Am J Public Health. 2012;102:319–28.

    Google Scholar 

  61. 61.

    Alegría M, Chatterji P, Wells K, Cao Z, Chen C-N, Takeuchi D, et al. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv 2008;59:1264–72.

    Google Scholar 

  62. 62.

    Chireh B, D’Arcy C. Shared and unique risk factors for depression and diabetes mellitus in a longitudinal study, implications for prevention: an analysis of a longitudinal population sample aged 45 years. Ther Adv Endocrinol Metab. 2019;10:2042018819865828.

    Google Scholar 

  63. 63.

    Harald B, Gordon P. Meta-review of depressive subtyping models. J Affect Disord. 2012;139:126–40.

    Google Scholar 

  64. 64.

    Lichtenberg P, Belmaker RH. Subtyping major depressive disorder. Psychother Psychosom. 2010;79:131–5.

    Google Scholar 

  65. 65.

    Goodwin FK, Jamison KR. Manic-depressive illness. Oxford University Press; 1990. p. 938.

  66. 66.

    Day CV, Rush AJ, Harris AWF, Boyce PM, Rekshan W, Etkin A, et al. Impairment and distress patterns distinguishing the melancholic depression subtype: an iSPOT-D report. J Affect Disord. 2015;174:493–502.

    Google Scholar 

  67. 67.

    Spanemberg L, Caldieraro MA, Vares EA, Wollenhaupt-Aguiar B, Kauer-Sant’Anna M, Kawamoto SY, et al. Biological differences between melancholic and nonmelancholic depression subtyped by the CORE measure. Neuropsychiatr Dis Treat. 2014;10:1523–31.

    Google Scholar 

  68. 68.

    Stewart JW, McGrath PJ, Rabkin JG, Quitkin FM. Atypical depression. A valid clinical entity? Psychiatr Clin North Am. 1993;16:479–95.

    CAS  Google Scholar 

  69. 69.

    Lewy AJ, Lefler BJ, Emens JS, Bauer VK. The circadian basis of winter depression. Proc Natl Acad Sci USA. 2006;103:7414–9.

    CAS  Google Scholar 

  70. 70.

    Lewy AJ, Sack RL, Miller LS, Hoban TM. Antidepressant and circadian phase-shifting effects of light. Science 1987;235:352–4.

    CAS  Google Scholar 

  71. 71.

    de Bodinat C, Guardiola-Lemaitre B, Mocaër E, Renard P, Muñoz C, Millan MJ. Agomelatine, the first melatonergic antidepressant: discovery, characterization and development. Nat Rev Drug Disco. 2010;9:628–42.

    Google Scholar 

  72. 72.

    Gaudiano BA, Dalrymple KL, Zimmerman M. Prevalence and clinical characteristics of psychotic versus nonpsychotic major depression in a general psychiatric outpatient clinic. Depress Anxiety 2009;26:54–64.

    Google Scholar 

  73. 73.

    Schatzberg AF, Posener JA, DeBattista C, Kalehzan BM, Rothschild AJ, Shear PK. Neuropsychological deficits in psychotic versus nonpsychotic major depression and no mental illness. Am J Psychiatry. 2000;157:1095–1100.

    CAS  Google Scholar 

  74. 74.

    Belanoff JK, Rothschild AJ, Cassidy F, DeBattista C, Baulieu E-E, Schold C, et al. An open label trial of C-1073 (mifepristone) for psychotic major depression. Biol Psychiatry. 2002;52:386–92.

    CAS  Google Scholar 

  75. 75.

    Keller J, Flores B, Gomez RG, Solvason HB, Kenna H, Williams GH, et al. Cortisol circadian rhythm alterations in psychotic major depression. Biol Psychiatry 2006;60:275–81.

    CAS  Google Scholar 

  76. 76.

    Nelson JC, Bickford D, Delucchi K, Fiedorowicz JG, Coryell WH. Risk of psychosis in recurrent episodes of psychotic and nonpsychotic major depressive disorder: a systematic review and meta-analysis. Am J Psychiatry. 2018;175:897–904.

    Google Scholar 

  77. 77.

    Lamers F, Rhebergen D, Merikangas KR, de Jonge P, Beekman ATF, B W J. Stability and transitions of depressive subtypes over a 2-year follow-up. Psychol Med 2012;42:2083–93.

    CAS  Google Scholar 

  78. 78.

    Coryell W, Winokur G, Shea T, Maser JD, Endicott J, Akiskal HS. The long-term stability of depressive subtypes. Am J Psychiatry. 1994;151:199–204

  79. 79.

    Melartin T, Leskelä U, Rytsälä H, Sokero P, Lestelä-Mielonen P, Isometsä E. Co-morbidity and stability of melancholic features in DSM-IV major depressive disorder. Psychol Med 2004;34:1443–52.

    Google Scholar 

  80. 80.

    Lovibond PF. Long-term stability of depression, anxiety, and stress syndromes. J Abnorm Psychol. 1998;107:520–6.

    CAS  Google Scholar 

  81. 81.

    Zuroff DC, Blatt SJ, Sanislow CA III, Bondi CM, Pilkonis PA. Vulnerability to depression: Reexamining state dependence and relative stability. J Abnorm Psychol. 1999;108:76–89.

    CAS  Google Scholar 

  82. 82.

    Musil R, Seemüller F, Meyer S, Spellmann I, Adli M, Bauer M, et al. Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder. Int J Methods Psychiatr Res. 2018;27:e1569.

  83. 83.

    Arnow BA, Blasey C, Williams LM, Palmer DM, Rekshan W, Schatzberg AF, et al. Depression subtypes in predicting antidepressant response: a report from the iSPOT-D trial. Am J Psychiatry. 2015;172:743–50.

    Google Scholar 

  84. 84.

    Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry. 2017;74:370–8.

    Google Scholar 

  85. 85.

    Paul R, Andlauer TFM, Czamara D, Hoehn D, Lucae S, Pütz B, et al. Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models. Transl Psychiatry 2019;9:187.

    CAS  Google Scholar 

  86. 86.

    Ingram WM, Baker AM, Bauer CR, Brown JP, Goes FS, Larson S. et al. Defining major depressive disorder cohorts using the EHR: multiple phenotypes based on ICD-9 codes and medication orders. neurol psychiatry. Neurol Psychiatry Brain Res. 2020;36:18–26.

    Google Scholar 

  87. 87.

    Huang SH, LePendu P, Iyer SV, Tai-Seale M, Carrell D, Shah NH. Toward personalizing treatment for depression: predicting diagnosis and severity. J Am Med Inf Assoc. 2014;21:1069–75.

    Google Scholar 

  88. 88.

    Xia CH, Ma Z, Ciric R, Gu S, Betzel RF, Kaczkurkin AN, et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 2018;9:3003.

    Google Scholar 

  89. 89.

    Mihalik A, Ferreira FS, Rosa MJ, Moutoussis M, Ziegler G, Monteiro JM, et al. Brain-behaviour modes of covariation in healthy and clinically depressed young people. Sci Rep. 2019;9:11536.

    Google Scholar 

  90. 90.

    Zhang B, Lin P, Shi H, Öngür D, Auerbach RP, Wang X, et al. Mapping anhedonia-specific dysfunction in a transdiagnostic approach: an ALE meta-analysis. Brain Imaging Behav. 2016;10:920–39.

    Google Scholar 

  91. 91.

    McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, Etkin A. Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. Am J Psychiatry. 2017;174:676–85.

    Google Scholar 

  92. 92.

    Zilverstand A, Parvaz MA, Goldstein RZ. Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review. Neuroimage 2017;151:105–16.

    Google Scholar 

  93. 93.

    Schäfer JÖ, Naumann E, Holmes EA, Tuschen-Caffier B, Samson AC. Emotion regulation strategies in depressive and anxiety symptoms in youth: a meta-analytic review. J Youth Adolesc. 2017;46:261–76.

    Google Scholar 

  94. 94.

    Sun X, Zhu C, So SHW. Dysfunctional metacognition across psychopathologies: a meta-analytic review. Eur Psychiatry 2017;45:139–53.

    CAS  Google Scholar 

  95. 95.

    Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, et al. Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol Psychiatry 2017;81:347–57.

    Google Scholar 

  96. 96.

    Price RB, Gates K, Kraynak TE, Thase ME, Siegle GJ. Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology 2017;42:2623–32.

    Google Scholar 

  97. 97.

    Feder S, Sundermann B, Wersching H, Teuber A, Kugel H, Teismann H, et al. Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects. J Affect Disord. 2017;222:79–87.

    Google Scholar 

  98. 98.

    Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2016;23:28–38.

    Google Scholar 

  99. 99.

    Dinga R, Schmaal L, Penninx BWJH, van Tol MJ, Veltman DJ, van Velzen L, et al. Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin. 2019:101796.

  100. 100.

    Grosenick L, Shi TC, Gunning FM, Dubin MJ, Downar J, Liston C. Functional and optogenetic approaches to discovering stable subtype-specific circuit mechanisms in depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:554–66.

    Google Scholar 

  101. 101.

    Tokuda T, Yoshimoto J, Shimizu Y, Okada G, Takamura M, Okamoto Y, et al. Identification of depression subtypes and relevant brain regions using a data-driven approach. Sci Rep. 2018;8:14082.

    Google Scholar 

  102. 102.

    Van Dijk KRA, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 2012;59:431–8.

    Google Scholar 

  103. 103.

    Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 2012;60:623–32.

    Google Scholar 

  104. 104.

    Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 2012;59:2142–54.

    Google Scholar 

  105. 105.

    Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 2014;84:320–41.

    Google Scholar 

  106. 106.

    Power JD, Lynch CJ, Dubin MJ, Silver BM, Martin A, Jones RM. Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and ‘missed’ deep breaths. Neuroimage. 2020;204:116234.

    Google Scholar 

  107. 107.

    Power JD, Lynch CJ, Silver BM, Dubin MJ, Martin A, Jones RM. Distinctions among real and apparent respiratory motions in human fMRI data. Neuroimage. 2019;201:116041.

    Google Scholar 

  108. 108.

    Friedman L, Glover GH. Report on a multicenter fMRI quality assurance protocol. J Magn Reson Imaging. 2006;23:827–39.

    Google Scholar 

  109. 109.

    Friedman L, Stern H, Brown GG, Mathalon DH, Turner J, Glover GH, et al. Test-retest and between-site reliability in a multicenter fMRI study. Hum Brain Mapp. 2008;29:958–72.

    Google Scholar 

  110. 110.

    Biswal BB, Mennes M, Zuo X-N, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA. 2010;107:4734–9.

    CAS  Google Scholar 

  111. 111.

    Prathikanti S, Weinberger DR. Psychiatric genetics-the new era: genetic research and some clinical implications. Br Med Bull. 2005;73-74:107–22.

    Google Scholar 

  112. 112.

    Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000;157:1552–62.

    CAS  Google Scholar 

  113. 113.

    Ormel J, Hartman CA, Snieder H. The genetics of depression: successful genome-wide association studies introduce new challenges. Transl Psychiatry 2019;9:114.

    Google Scholar 

  114. 114.

    Smoller JW, Finn CT. Family, twin, and adoption studies of bipolar disorder. Am J Med Genet C Semin Med Genet. 2003;123C:48–58.

    Google Scholar 

  115. 115.

    Halldorsdottir T, Binder EB. Gene × environment interactions: from molecular mechanisms to behavior. Annu Rev Psychol. 2017;68:215–41.

    Google Scholar 

  116. 116.

    Weaver ICG, Cervoni N, Champagne FA, D’Alessio AC, Sharma S, Seckl JR, et al. Epigenetic programming by maternal behavior. Nat Neurosci 2004;7:847–54.

    CAS  Google Scholar 

  117. 117.

    Meaney MJ. Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annu Rev Neurosci. 2001;24:1161–92.

    CAS  Google Scholar 

  118. 118.

    Howard DM, Adams MJ, Shirali M, Clarke T-K, Marioni RE, Davies G, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat Commun 2018;9:1470.

    Google Scholar 

  119. 119.

    Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet 2016;48:1031–6.

    CAS  Google Scholar 

  120. 120.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014;511:421–7.

    Google Scholar 

  121. 121.

    Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003;60:1187–92.

    Google Scholar 

  122. 122.

    Duncan LE, Ostacher M, Ballon J. How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete. Neuropsychopharmacology 2019;44:1518–23.

    Google Scholar 

  123. 123.

    Canli T, Lesch K-P. Long story short: the serotonin transporter in emotion regulation and social cognition. Nat Neurosci 2007;10:1103–9.

    CAS  Google Scholar 

  124. 124.

    Carter CS, Bearden CE, Bullmore ET, Geschwind DH, Glahn DC, Gur RE, et al. Enhancing the informativeness and replicability of imaging genomics studies. Biol Psychiatry 2017;82:157–64.

    CAS  Google Scholar 

  125. 125.

    Pereira LP, Köhler CA, de Sousa RT, Solmi M, de Freitas BP, Fornaro M, et al. The relationship between genetic risk variants with brain structure and function in bipolar disorder: a systematic review of genetic-neuroimaging studies. Neurosci Biobehav Rev. 2017;79:87–109.

    Google Scholar 

  126. 126.

    Pereira LP, Köhler CA, Stubbs B, Miskowiak KW, Morris G, de Freitas BP, et al. Imaging genetics paradigms in depression research: Systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2018;86:102–13.

    CAS  Google Scholar 

  127. 127.

    Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 1996;274:1527–31.

    CAS  Google Scholar 

  128. 128.

    Sen S, Burmeister M, Ghosh D. Meta-analysis of the association between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety-related personality traits. Am J Med Genet B Neuropsychiatr Genet. 2004;127B:85–89.

    Google Scholar 

  129. 129.

    Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science 2003;301:386–9.

    CAS  Google Scholar 

  130. 130.

    Karg K, Burmeister M, Shedden K, Sen S. The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation. Arch Gen Psychiatry. 2011;68:444–54.

    Google Scholar 

  131. 131.

    Risch N, Herrell R, Lehner T, Liang K-Y, Eaves L, Hoh J, et al. Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis. JAMA 2009;301:2462–71.

    CAS  Google Scholar 

  132. 132.

    Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat Neurosci 2005;8:828–34.

    CAS  Google Scholar 

  133. 133.

    Hariri AR, Drabant EM, Weinberger DR. Imaging genetics: perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing. Biol Psychiatry 2006;59:888–97.

    CAS  Google Scholar 

  134. 134.

    Hariri AR, Mattay VS, Tessitore A, Fera F, Weinberger DR. Neocortical modulation of the amygdala response to fearful stimuli. Biol Psychiatry 2003;53:494–501.

    Google Scholar 

  135. 135.

    Alexopoulos GS, Murphy CF, Gunning-Dixon FM, Glatt CE, Latoussakis V, Kelly RE Jr, et al. Serotonin transporter polymorphisms, microstructural white matter abnormalities and remission of geriatric depression. J Affect Disord. 2009;119:132–41.

    CAS  Google Scholar 

  136. 136.

    Taylor WD, Steffens DC, Payne ME, MacFall JR, Marchuk DA, Svenson IK, et al. Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Arch Gen Psychiatry. 2005;62:537–44.

    CAS  Google Scholar 

  137. 137.

    Egan MF, Kojima M, Callicott JH, Goldberg TE, Kolachana BS, Bertolino A, et al. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 2003;112:257–69.

    CAS  Google Scholar 

  138. 138.

    Autry AE, Adachi M, Nosyreva E, Na ES, Los MF, Cheng P-F, et al. NMDA receptor blockade at rest triggers rapid behavioural antidepressant responses. Nature 2011;475:91–95.

    CAS  Google Scholar 

  139. 139.

    Berton O, McClung CA, Dileone RJ, Krishnan V, Renthal W, Russo SJ, et al. Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 2006;311:864–8.

    CAS  Google Scholar 

  140. 140.

    Duman RS, Monteggia LM. A neurotrophic model for stress-related mood disorders. Biol Psychiatry 2006;59:1116–27.

    CAS  Google Scholar 

  141. 141.

    Chen Z-Y, Jing D, Bath KG, Ieraci A, Khan T, Siao C-J, et al. Genetic variant BDNF (Val66Met) polymorphism alters anxiety-related behavior. Science 2006;314:140–3.

    CAS  Google Scholar 

  142. 142.

    Frodl T, Skokauskas N, Frey E-M, Morris D, Gill M, Carballedo A. BDNF Val66Met genotype interacts with childhood adversity and influences the formation of hippocampal subfields. Hum Brain Mapp. 2014;35:5776–83.

    Google Scholar 

  143. 143.

    Cole J, Weinberger DR, Mattay VS, Cheng X, Toga AW, Thompson PM, et al. No effect of 5HTTLPR or BDNF Val66Met polymorphism on hippocampal morphology in major depression. Genes Brain Behav. 2011;10:756–64.

    CAS  Google Scholar 

  144. 144.

    Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatry. 2019;176:376–87.

    Google Scholar 

  145. 145.

    Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97:576–92.

    Google Scholar 

  146. 146.

    Maier RM, Visscher PM, Robinson MR, Wray NR. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med 2018;48:1055–67.

    CAS  Google Scholar 

  147. 147.

    Halldorsdottir T, Piechaczek C, Soares de Matos AP, Czamara D, Pehl V, Wagenbuechler P, et al. Polygenic risk: predicting depression outcomes in clinical and epidemiological cohorts of youths. Am J Psychiatry. 2019;176:615–25.

    Google Scholar 

  148. 148.

    Choi SW, Mak TS-H, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15:2759–72.

    CAS  Google Scholar 

  149. 149.

    Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 2009;41:56–65.

    CAS  Google Scholar 

  150. 150.

    Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl J Med. 2016;375:2349–58.

    CAS  Google Scholar 

  151. 151.

    Khera AV, Chaffin M, Aragam KG. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.

  152. 152.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013;381:1371–9.

    Google Scholar 

  153. 153.

    Yüksel D, Dietsche B, Forstner AJ, Witt SH, Maier R, Rietschel M, et al. Polygenic risk for depression and the neural correlates of working memory in healthy subjects. Prog Neuropsychopharmacol Biol Psychiatry. 2017;79:67–76.

    Google Scholar 

  154. 154.

    Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160:636–45.

    Google Scholar 

  155. 155.

    Ward J, Lyall LM, Bethlehem RAI, Ferguson A, Strawbridge RJ, Lyall DM, et al. Novel genome-wide associations for anhedonia, genetic correlation with psychiatric disorders, and polygenic association with brain structure. Transl Psychiatry. 2019;9:327.

    CAS  Google Scholar 

  156. 156.

    Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015;47:291–5.

  157. 157.

    Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, et al. Canonical genetic signatures of the adult human brain. Nat Neurosci 2015;18:1832–44.

    CAS  Google Scholar 

  158. 158.

    Diez I, Larson AG, Nakhate V, Dunn EC, Fricchione GL, Nicholson TR, et al. Early-life trauma endophenotypes and brain circuit-gene expression relationships in functional neurological (conversion) disorder. Mol Psychiatry. 2020.

  159. 159.

    Morgan SE, Seidlitz J, Whitaker KJ, Romero-Garcia R, Clifton NE, Scarpazza C, et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc Natl Acad Sci USA. 2019;116:9604–9.

    CAS  Google Scholar 

  160. 160.

    Romero-Garcia R, Seidlitz J, Whitaker KJ, Morgan SE, Fonagy P, Dolan RJ, et al. Schizotypy-related magnetization of cortex in healthy adolescence is colocated with expression of schizophrenia-related genes. Biol Psychiatry. 2019.

  161. 161.

    Whitaker KJ, Vértes PE, Romero-Garcia R, Váša F, Moutoussis M, Prabhu G, et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc Natl Acad Sci USA. 2016;113:9105–10.

    CAS  Google Scholar 

  162. 162.

    Vértes PE, Rittman T, Whitaker KJ, Rafael R-G, Váša F, Kitzbichler MG, et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos Trans R Soc Lond B Biol Sci. 2016;371:20150362.

    Google Scholar 

  163. 163.

    Romero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RAI. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol Psychiatry 2019;24:1053–64.

    CAS  Google Scholar 

  164. 164.

    Smith DJ, Nicholl BI, Cullen B, Martin D, Ul-Haq Z, Evans J, et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS One 2013;8:e75362.

    Google Scholar 

  165. 165.

    Cheng W, Rolls ET, Ruan H, Feng J. Functional connectivities in the brain that mediate the association between depressive problems and sleep quality. JAMA Psychiatry. 2018;75:1052–61.

    Google Scholar 

  166. 166.

    Miller JA, Ding S-L, Sunkin SM, Smith KA, Ng L, Szafer A, et al. Transcriptional landscape of the prenatal human brain. Nature 2014;508:199–206.

    CAS  Google Scholar 

  167. 167.

    Sunkin SM, Ng L, Lau C, Dolbeare T, Gilbert TL, Thompson CL, et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 2013;41:D996–D1008.

    CAS  Google Scholar 

  168. 168.

    Shmueli G. To explain or to predict? Stat Sci 2010;25:289–310.

    Google Scholar 

  169. 169.

    Williams NR, Sudheimer KD, Bentzley BS, Pannu J, Stimpson KH, Duvio D, et al. High-dose spaced theta-burst TMS as a rapid-acting antidepressant in highly refractory depression. Brain 2018;141:e18.

    Google Scholar 

  170. 170.

    Chen AC, Oathes DJ, Chang C, Bradley T, Zhou Z-W, Williams LM, et al. Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc Natl Acad Sci USA. 2013;110:19944–9.

    CAS  Google Scholar 

  171. 171.

    Padmanabhan JL, Cooke D, Joutsa J, Siddiqi SH, Ferguson M, Darby RR, et al. A human depression circuit derived from focal brain lesions. Biol Psychiatry 2019;86:749–58.

    Google Scholar 

  172. 172.

    Fox MD. Mapping symptoms to brain networks with the human connectome. N. Engl J Med. 2018;379:2237–45.

    CAS  Google Scholar 

  173. 173.

    Salk RH, Hyde JS, Abramson LY. Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull 2017;143:783–822.

    Google Scholar 

  174. 174.

    Eid RS, Gobinath AR, Galea LAM. Sex differences in depression: Insights from clinical and preclinical studies. Prog Neurobiol 2019;176:86–102.

    Google Scholar 

  175. 175.

    Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM, Gilmore AW, et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 2018;98:439–.e5.

    CAS  Google Scholar 

  176. 176.

    Braga RM, Buckner RL. Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron 2017;95:457–.e5.

    CAS  Google Scholar 

  177. 177.

    Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen M-Y, et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 2015;87:657–70.

    CAS  Google Scholar 

  178. 178.

    Kirkby LA, Luongo FJ, Lee MB, Nahum M, Van Vleet TM, Rao VR, et al. An amygdala-hippocampus subnetwork that encodes variation in human mood. Cell 2018;175:1688–1700.e14.

    CAS  Google Scholar 

  179. 179.

    Krishnan V, Han MH, Graham DL, Berton O, Renthal W, Russo SJ, et al. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 2007;131:391–404.

    CAS  Google Scholar 

  180. 180.

    Hultman R, Ulrich K, Sachs BD, Blount C, Carlson DE, Ndubuizu N, et al. Brain-wide electrical spatiotemporal dynamics encode depression vulnerability. Cell 2018;173:166–180.e14.

    CAS  Google Scholar 

  181. 181.

    Ferenczi E, Zalocusky KA, Liston C, Katovich K, Amatya D, Warden MR, et al. Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science 2016;351:41–53.

    CAS  Google Scholar 

  182. 182.

    Pizzagalli DA, Webb CA, Dillon DG, Tenke CE, Kayser J, Goer F, et al. Pretreatment rostral anterior cingulate cortex theta activity in relation to symptom improvement in depression: a randomized clinical trial. JAMA Psychiatry. 2018;75:547–54.

    Google Scholar 

  183. 183.

    Schatzberg AF, DeBattista C, Lazzeroni LC, Etkin A, Murphy GM Jr, Williams LM. ABCB1 genetic effects on antidepressant outcomes: a report from the iSPOT-D trial. Am J Psychiatry. 2015;172:751–9.

    Google Scholar 

  184. 184.

    Williams LM. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 2016;3:472–80.

    Google Scholar 

  185. 185.

    Dunlop BW, Rajendra JK, Craighead WE, Kelley ME, McGrath CL, Choi KS, et al. Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder. Am J Psychiatry. 2017;174:533–45.

    Google Scholar 

  186. 186.

    Weigand A, Horn A, Caballero R, Cooke D, Stern AP, Taylor SF, et al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol Psychiatry 2018;84:28–37.

    CAS  Google Scholar 

  187. 187.

    Meng X, Brunet A, Turecki G, Liu A, D’Arcy C, Caron J. Risk factor modifications and depression incidence: a 4-year longitudinal Canadian cohort of the Montreal Catchment Area Study. BMJ Open 2017;7:e015156.

    Google Scholar 

  188. 188.

    Rudkjoebing LA, Bungum AB, Flachs EM, Eller NH, Borritz M, Aust B, et al. Work-related exposure to violence or threats and risk of mental disorders and symptoms: a systematic review and meta-analysis. Scand J Work Environ Health. 2020.

  189. 189.

    Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–60.

    Google Scholar 

  190. 190.

    Lewis SJ, Lawlor DA, Davey Smith G, Araya R, Timpson N, Day INM, et al. The thermolabile variant of MTHFR is associated with depression in the British Women’s Heart and Health Study and a meta-analysis. Mol Psychiatry 2006;11:352–60.

    CAS  Google Scholar 

  191. 191.

    Gilbody S, Lewis S, Lightfoot T. Methylenetetrahydrofolate reductase (MTHFR) genetic polymorphisms and psychiatric disorders: a HuGE review. Am J Epidemiol. 2007;165:1–13.

    Google Scholar 

  192. 192.

    López-León S, Janssens ACJW, González-Zuloeta Ladd AM, Del-Favero J, Claes SJ, Oostra BA, et al. Meta-analyses of genetic studies on major depressive disorder. Mol Psychiatry. 2008;13:772–85.

    Google Scholar 

  193. 193.

    Samaan Z, Gaysina D, Cohen-Woods S, Craddock N, Jones L, Korszun A, et al. Methylenetetrahydrofolate reductase gene variant (MTHFR C677T) and migraine: a case control study and meta-analysis. BMC Neurol 2011;11:66.

    Google Scholar 

  194. 194.

    Saha T, Chatterjee M, Sinha S, Rajamma U, Mukhopadhyay K. Components of the folate metabolic pathway and ADHD core traits: an exploration in eastern Indian probands. J Hum Genet. 2017;62:687–95.

    CAS  Google Scholar 

  195. 195.

    Lok A, Bockting CLH, Koeter MWJ, Snieder H, Assies J, Mocking RJT, et al. Interaction between the MTHFR C677T polymorphism and traumatic childhood events predicts depression. Transl Psychiatry 2013;3:e288.

    CAS  Google Scholar 

  196. 196.

    Noh K, Lee H, Choi T-Y, Joo Y, Kim S-J, Kim H, et al. Negr1 controls adult hippocampal neurogenesis and affective behaviors. Mol Psychiatry 2019;24:1189–205.

    CAS  Google Scholar 

  197. 197.

    Wang X, Cheng W, Zhu J, Yin H, Chang S, Yue W, et al. Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorder. J Affect Disord. 2020;265:679–86.

    CAS  Google Scholar 

  198. 198.

    Zandoná MR, Sangalli CN, Campagnolo PDB, Vitolo MR, Almeida S, Mattevi VS. Validation of obesity susceptibility loci identified by genome-wide association studies in early childhood in South Brazilian children. Pediatr Obes. 2017;12:85–92.

    Google Scholar 

  199. 199.

    Kuc K, Bielecki M, Racicka-Pawlukiewicz E, Czerwinski MB, Cybulska-Klosowicz A. The SLC6A3 gene polymorphism is related to the development of attentional functions but not to ADHD. Sci Rep. 2020;10:6176.

    CAS  Google Scholar 

  200. 200.

    Marinho FVC, Pinto GR, Oliveira T, Gomes A, Lima V, Ferreira-Fernandes H, et al. The SLC6A3 3′-UTR VNTR and intron 8 VNTR polymorphisms association in the time estimation. Brain Struct Funct. 2019;224:253–62.

    CAS  Google Scholar 

  201. 201.

    Marinho V, Oliveira T, Bandeira J, Pinto GR, Gomes A, Lima V, et al. Genetic influence alters the brain synchronism in perception and timing. J Biomed Sci. 2018;25:61.

    Google Scholar 

  202. 202.

    Zahavi AY, Sabbagh MA, Washburn D, Mazurka R, Bagby RM, Strauss J, et al. Serotonin and dopamine gene variation and theory of mind decoding accuracy in major depression: a preliminary investigation. PLoS ONE 2016;11:e0150872.

    Google Scholar 

  203. 203.

    Kishi T, Tsunoka T, Ikeda M, Kawashima K, Okochi T, Kitajima T, et al. Serotonin 1A receptor gene and major depressive disorder: an association study and meta-analysis. J Hum Genet. 2009;54:629–33.

    CAS  Google Scholar 

  204. 204.

    Kishi T, Yoshimura R, Fukuo Y, Okochi T, Matsunaga S, Umene-Nakano W, et al. The serotonin 1A receptor gene confer susceptibility to mood disorders: results from an extended meta-analysis of patients with major depression and bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2013;263:105–18.

    Google Scholar 

  205. 205.

    Zhang K, Xu Q, Xu Y, Yang H, Luo J, Sun Y, et al. The combined effects of the 5-HTTLPR and 5-HTR1A genes modulates the relationship between negative life events and major depressive disorder in a Chinese population. J Affect Disord. 2009;114:224–31.

    CAS  Google Scholar 

  206. 206.

    López León S, Croes EA, Sayed-Tabatabaei FA, Claes S, Van Broeckhoven C, van Duijn CM. The dopamine D4 receptor gene 48-base-pair-repeat polymorphism and mood disorders: a meta-analysis. Biol Psychiatry 2005;57:999–1003.

    Google Scholar 

  207. 207.

    Bircher J, Kotyuk E, Fulop M, Vereczkei A, Ronai Z, Varga K, et al. Gene-sex interaction in hypercompetitive attitude suggests beneficial effect of the DRD4 7-repeat allele in adaptation. Neuropsychopharmacol Hung 2019;21:47–58.

    Google Scholar 

  208. 208.

    Ji H, Xu X, Liu G, Liu H, Wang Q, Shen W, et al. Dopamine receptor D4 promoter hypermethylation increases the risk of drug addiction. Exp Ther Med. 2018;15:2128–33.

    CAS  Google Scholar 

  209. 209.

    Green CG, Babineau V, Jolicoeur-Martineau A, Bouvette-Turcot A-A, Minde K, Sassi R, et al. Prenatal maternal depression and child serotonin transporter linked polymorphic region (5-HTTLPR) and dopamine receptor D4 (DRD4) genotype predict negative emotionality from 3 to 36 months. Dev Psychopathol 2017;29:901–17.

    Google Scholar 

  210. 210.

    Badamasi IM, Lye MS, Ibrahim N, Stanslas J. Genetic endophenotypes for insomnia of major depressive disorder and treatment-induced insomnia. J Neural Transm. 2019;126:711–22.

    Google Scholar 

  211. 211.

    Keers R, Bonvicini C, Scassellati C, Uher R, Placentino A, Giovannini C, et al. Variation in GNB3 predicts response and adverse reactions to antidepressants. J Psychopharmacol 2011;25:867–74.

    CAS  Google Scholar 

  212. 212.

    Lin E, Chen PS, Chang HH, Gean P-W, Tsai HC, Yang YK, et al. Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2009;33:1167–72.

    CAS  Google Scholar 

  213. 213.

    Hu Q, Zhang S-Y, Liu F, Zhang XJ, Cui G-C, Yu E-Q, et al. Influence of GNB3 C825T polymorphism on the efficacy of antidepressants in the treatment of major depressive disorder: a meta-analysis. J Affect Disord. 2015;172:103–9.

    CAS  Google Scholar 

  214. 214.

    Krishnan M, Shelling AN, Wall CR, Mitchell EA, Murphy R, McCowan LME, et al. Gene-by-activity interactions on obesity traits of 6-year-old New Zealand European children: a children of SCOPE Study. Pediatr Exerc Sci. 2018;30:69–80.

    Google Scholar 

  215. 215.

    Johnston KJA, Adams MJ, Nicholl BI, Ward J, Strawbridge RJ, McIntosh AM, et al. Identification of novel common variants associated with chronic pain using conditional false discovery rate analysis with major depressive disorder and assessment of pleiotropic effects of LRFN5. Transl Psychiatry 2019;9:310.

    Google Scholar 

  216. 216.

    Nho K, Ramanan VK, Horgusluoglu E, Kim S, Inlow MH, Risacher SL, et al. Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults. J Alzheimers Dis. 2015;45:1197–206.

    CAS  Google Scholar 

  217. 217.

    Choi Y, Nam J, Whitcomb DJ, Song YS, Kim D, Jeon S, et al. SALM5 trans-synaptically interacts with LAR-RPTPs in a splicing-dependent manner to regulate synapse development. Sci Rep. 2016;6:26676.

    CAS  Google Scholar 

  218. 218.

    de Bruijn DRH, van Dijk AHA, Pfundt R, Hoischen A, Merkx GFM, Gradek GA, et al. Severe progressive autism associated with two de novo changes: a 2.6-Mb 2q31.1 deletion and a balanced t(14;21)(q21.1;p11.2) translocation with long-range epigenetic silencing of LRFN5 expression. Mol Syndromol 2010;1:46–57.

    Google Scholar 

  219. 219.

    Cappuccio G, Attanasio S, Alagia M, Mutarelli M, Borzone R, Karali M, et al. Microdeletion of pseudogene chr14.232.a affects LRFN5 expression in cells of a patient with autism spectrum disorder. Eur J Hum Genet. 2019;27:1475–80.

    Google Scholar 

  220. 220.

    Wamsley B, Jaglin XH, Favuzzi E, Quattrocolo G, Nigro MJ, Yusuf N, et al. Rbfox1 mediates cell-type-specific splicing in cortical interneurons. Neuron 2018;100:846–.e7.

    CAS  Google Scholar 

  221. 221.

    Kong L-L, Miao D, Tan L, Liu S-L, Li J-Q, Cao X-P, et al. Genome-wide association study identifies RBFOX1 locus influencing brain glucose metabolism. Ann Transl Med. 2018;6:436.

    CAS  Google Scholar 

  222. 222.

    Fernàndez-Castillo N, Gan G, van Donkelaar MMJ, Vaht M, Weber H, Retz W, et al. RBFOX1, encoding a splicing regulator, is a candidate gene for aggressive behavior. Eur Neuropsychopharmacol 2020;30:44–55.

    Google Scholar 

  223. 223.

    Zhao W-W. Intragenic deletion of RBFOX1 associated with neurodevelopmental/neuropsychiatric disorders and possibly other clinical presentations. Mol Cytogenet 2013;6:26.

    Google Scholar 

  224. 224.

    Clarke H, Flint J, Attwood AS, Munafò MR. Association of the 5- HTTLPR genotype and unipolar depression: a meta-analysis. Psychol Med 2010;40:1767–78.

    CAS  Google Scholar 

  225. 225.

    Furlong RA, Ho L, Walsh C, Rubinsztein JS, Jain S, Paykel ES, et al. Analysis and meta-analysis of two serotonin transporter gene polymorphisms in bipolar and unipolar affective disorders. Am J Med Genet 1998;81:58–63.

    CAS  Google Scholar 

  226. 226.

    Dosenbach NUF, Koller JM, Earl EA, Miranda-Dominguez O, Klein RL, Van AN, et al. Real-time motion analytics during brain MRI improve data quality and reduce costs. Neuroimage 2017;161:80–93.

    Google Scholar 

  227. 227.

    Power JD, Silver BM, Silverman MR, Ajodan EL, Bos DJ, Jones RM. Customized head molds reduce motion during resting state fMRI scans. Neuroimage 2019;189:141–9.

    Google Scholar 

  228. 228.

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118–27.

    Google Scholar 

  229. 229.

    Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B-Stat Methodol. 2005;67:301–20.

    Google Scholar 

  230. 230.

    Sui J, Adali T, Pearlson G, Yang H, Sponheim SR, White T, et al. A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia. Neuroimage 2010;51:123–34.

    Google Scholar 

  231. 231.

    Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 2012;489:391–9.

    CAS  Google Scholar 

  232. 232.

    Arloth J, Bader DM, Röh S, Altmann A. Re-annotator: annotation pipeline for microarray probe sequences. PLoS ONE 2015;10:e0139516.

    Google Scholar 

  233. 233.

    Braschi B, Denny P, Gray K, Jones T, Seal R, Tweedie S, et al. the HGNC and VGNC resources in 2019. Nucleic Acids Res. 2019;47:D786–D792.

    CAS  Google Scholar 

  234. 234.

    Arnatkevic Iūtė A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019.

  235. 235.

    Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.

    Google Scholar 

  236. 236.

    Monteiro JM, Rao A, Shawe-Taylor J, Mourão-Miranda J, Initiative AD. A multiple hold-out framework for sparse partial least squares. J Neurosci Methods. 2016;271:182–94.

    Google Scholar 

  237. 237.

    Ressa A, Fitzpatrick M, van den Toorn H, Heck AJR, Altelaar M. PaDuA: a python library for high-throughput (Phospho)proteomics data analysis. J Proteome Res. 2019;18:576–84.

    CAS  Google Scholar 

  238. 238.

    Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 2011;56:455–75.

    Google Scholar 

  239. 239.

    Abdi H, Williams LJ. Partial least squares methods: partial least squares correlation and partial least square regression. Methods Mol Biol. 2013;930:549–79.

    CAS  Google Scholar 

  240. 240.

    Boulesteix A-L, Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform 2007;8:32–44.

    CAS  Google Scholar 

  241. 241.

    Bennett KP, Embrechts MJ. An optimization perspective on kernel partial least squares regression Nato Science Series sub series III computer and systems sciences. 2003;190:227–50.

  242. 242.

    Wong E, Palande S, Wang B, Zielinski B, Anderson J, Fletcher PT. Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior. In Proc 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague: 2016. p. 1303–6.

  243. 243.

    Alexander-Bloch AF, Shou H, Liu S, Satterthwaite TD, Glahn DC, Shinohara RT, et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 2018;178:540–51.

    Google Scholar 

  244. 244.

    Kock N. Should bootstrapping be used in PLS-SEM? Toward stable P-Value calculation methods. J Appl Struct Equ Modeling. 2018;2:1–12.

    Google Scholar 

  245. 245.

    Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S. Multi-and megavariate data analysis, Vol 1. Sweden: Umetrics Sweden; 2006.

  246. 246.

    Kvalheim OM. Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots. J Chemom 2010;24:496–504.

    CAS  Google Scholar 

  247. 247.

    Rajalahti T, Arneberg R, Kroksveen AC, Berle M, Myhr K-M, Kvalheim OM. Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (Biomarker) selection in complex spectral or chromatographic profiles. Anal Chem 2009;81:2581–90.

    CAS  Google Scholar 

  248. 248.

    Webber W, Moffat A, Zobel J. A similarity measure for indefinite rankings. ACM Trans Inf Syst (TOIS). 2010;28:20.

    Google Scholar 

  249. 249.

    The Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47:D330–D338.

    Google Scholar 

  250. 250.

    Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    CAS  Google Scholar 

  251. 251.

    Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613.

    CAS  Google Scholar 

  252. 252.

    Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234–W241.

    CAS  Google Scholar 

  253. 253.

    Gaspar HA, Hübel C, Breen G. Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes. Bioinformatics 2019;35:2515–7.

    CAS  Google Scholar 

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Both authors contributed to the literature review and writing of this paper. A.B. created Figs. 1 and 4, and adapted the other figures from the references cited in the figure legends. Both authors read and approved the final paper.

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Correspondence to Conor Liston.

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Buch, A.M., Liston, C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacol. 46, 156–175 (2021).

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