Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Abstract

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

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References

  1. 1.

    Jia Z, Huang X, Wu Q, Zhang T, Lui S, Zhang J, Amatya N, Kuang W, Chan RC, Kemp GJ, Mechelli A, Gong Q. High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. Am J Psychiatry 2010; 167(11): 1381–1390

    PubMed  Article  Google Scholar 

  2. 2.

    Sözeri-Varma G. Depression in the elderly: clinical features and risk factors. Aging Dis 2012; 3(6): 465–471

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Nestler EJ, Barrot M, DiLeone RJ, Eisch AJ, Gold SJ, Monteggia LM. Neurobiology of depression. Neuron 2002; 34(1): 13–25

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Ressler KJ, Mayberg HS. Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nat Neurosci 2007; 10(9): 1116–1124

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Martin EI, Ressler KJ, Binder E, Nemeroff CB. The neurobiology of anxiety disorders: brain imaging, genetics, and psychoneur-oendocrinology. Psychiatr Clin North Am 2009; 32(3): 549–575

    PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Suo XS, Lei DL, Li LL, Li WL, Dai JD, Wang SW, He MH, Zhu HZ, Kemp GJK, Gong QG. Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders. J Psychiatry Neurosci 2018; 43(6): 416–427

    PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017; 23(1): 28–38

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Sinyor M, Schaffer A, Levitt A. The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatry 2010; 55(3): 126–135

    PubMed  Article  Google Scholar 

  9. 9.

    Connolly KR, Thase ME. If at first you don’t succeed: a review of the evidence for antidepressant augmentation, combination and switching strategies. Drugs 2011; 71(1): 43–64

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Stimpson N, Agrawal N, Lewis G. Randomised controlled trials investigating pharmacological and psychological interventions for treatment-refractory depression: systematic review. Br J Psychiatry 2002; 181(4): 284–294

    PubMed  Article  Google Scholar 

  11. 11.

    Leuchter AF, Cook IA, Hamilton SP, Narr KL, Toga A, Hunter AM, Faull K, Whitelegge J, Andrews AM, Loo J, Way B, Nelson SF, Horvath S, Lebowitz BD. Biomarkers to predict antidepressant response. Curr Psychiatry Rep 2010; 12(6): 553–562

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Lui S, Zhou XJ, Sweeney JA, Gong Q. Psychoradiology: the frontier of neuroimaging in psychiatry. Radiology 2016; 281(2): 357–372

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, Gold G, Gulani V, Hall-Craggs M, Leiner T, Lim CCT, Pipe JG, Reeder S, Reinhold C, Smits M, Sodickson DK, Tempany C, Vargas HA, Wang M. Value of MRI in medicine: more than just another test? J Magn Reson Imaging 2019; 49(7): e14–e25

    PubMed  Article  Google Scholar 

  14. 14.

    Zhao YJ, Du MY, Huang XQ, Lui S, Chen ZQ, Liu J, Luo Y, Wang XL, Kemp GJ, Gong QY. Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis. Psychol Med 2014; 44(14): 2927–2937

    PubMed  Article  Google Scholar 

  15. 15.

    Gong QY. Psychoradiology. Neuroimaging Clin N Am 2020; 30(1): 1–124

    PubMed  Article  Google Scholar 

  16. 16.

    Chen ZQ, Du MY, Zhao YJ, Huang XQ, Li J, Lui S, Hu JM, Sun HQ, Liu J, Kemp GJ, Gong QY. Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder. J Psychiatry Neurosci 2015; 40(6): 401–411

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Chen Z, Zhang H, Jia Z, Zhong J, Huang X, Du M, Chen L, Kuang W, Sweeney JA, Gong Q. Magnetization transfer imaging of suicidal patients with major depressive disorder. Sci Rep 2015; 5(1): 9670

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Ridgway GR, Henley SM, Rohrer JD, Scahill RI, Warren JD, Fox NC. Ten simple rules for reporting voxel-based morphometry studies. Neuroimage 2008; 40(4): 1429–1435

    PubMed  Article  Google Scholar 

  19. 19.

    Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000; 11(6): 805–821

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Peng W, Chen Z, Yin L, Jia Z, Gong Q. Essential brain structural alterations in major depressive disorder: a voxel-wise meta-analysis on first episode, medication-naive patients. J Affect Disord 2016; 199: 114–123

    PubMed  Article  Google Scholar 

  21. 21.

    Wang W, Zhao Y, Hu X, Huang X, Kuang W, Lui S, Kemp GJ, Gong Q. Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci Rep 2017; 7(1): 10401

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Zhao Y, Chen L, Zhang W, Xiao Y, Shah C, Zhu H, Yuan M, Sun H, Yue Q, Jia Z, Zhang W, Kuang W, Gong Q, Lui S. Gray matter abnormalities in non-comorbid medication-naive patients with major depressive disorder or social anxiety disorder. EBioMedicine 2017; 21: 228–235

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Suh JS, Schneider MA, Minuzzi L, MacQueen GM, Strother SC, Kennedy SH, Frey BN. Cortical thickness in major depressive disorder: a systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2019; 88: 287–302

    PubMed  Article  Google Scholar 

  24. 24.

    Jones DK, Leemans A. Diffusion tensor imaging. Methods Mol Biol 2011; 711: 127–144

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Stieltjes B, Kaufmann WE, van Zijl PC, Fredericksen K, Pearlson GD, Solaiyappan M, Mori S. Diffusion tensor imaging and axonal tracking in the human brainstem. Neuroimage 2001; 14(3): 723–735

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Liao Y, Huang X, Wu Q, Yang C, Kuang W, Du M, Lui S, Yue Q, Chan RC, Kemp GJ, Gong Q. Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci 2013; 38(1): 49–56

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 2001; 1: 211–244

    Google Scholar 

  28. 28.

    Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 2012; 36(4): 1140–1152

    PubMed  Article  Google Scholar 

  29. 29.

    Costafreda SG, Fu CH, Picchioni M, Toulopoulou T, McDonald C, Kravariti E, Walshe M, Prata D, Murray RM, McGuire PK. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 2011; 11(1):18

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Li F, Huang X, Tang W, Yang Y, Li B, Kemp GJ, Mechelli A, Gong Q. Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder. Hum Brain Mapp 2014; 35(6): 2643–2651

    PubMed  Article  Google Scholar 

  31. 31.

    Hu X, Liu Q, Li B, Tang W, Sun H, Li F, Yang Y, Gong Q, Huang X. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy. Eur Neuropsychopharmacol 2016; 26(2): 246–254

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Mwangi B, Ebmeier KP, Matthews K, Steele JD. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012; 135(5): 1508–1521

    PubMed  Article  Google Scholar 

  33. 33.

    Qiu L, Huang X, Zhang J, Wang Y, Kuang W, Li J, Wang X, Wang L, Yang X, Lui S, Mechelli A, Gong Q. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J Psychiatry Neurosci 2014; 39(2): 78–86

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Yang J, Zhang M, Ahn H, Zhang Q, Jin TB, Li I, Nemesure M, Joshi N, Jiang H, Miller JM, Ogden RT, Petkova E, Milak MS, Sublette ME, Sullivan GM, Trivedi MH, Weissman M, McGrath PJ, Fava M, Kurian BT, Pizzagalli DA, Cooper CM, McInnis M, Oquendo MA, Mann JJ, Parsey RV, DeLorenzo C. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018; 39(11): 4420–4439

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Culang-Reinlieb ME, Johnert LC, Brickman AM, Steffens DC, Garcon E, Sneed JR. MRI-defined vascular depression: a review of the construct. Int J Geriatr Psychiatry 2011; 26(11): 1101–1108

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Simpson S, Baldwin RC, Jackson A, Burns A, Thomas P. Is the clinical expression of late-life depression influenced by brain changes? MRI subcortical neuroanatomical correlates of depressive symptoms. Int Psychogeriatr 2000; 12(4): 425–434

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Steffens DC. Establishing diagnostic criteria for vascular depression. J Neurol Sci 2004; 226(1–2): 59–62

    PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Soares JC, Mann JJ. The anatomy of mood disorders—review of structural neuroimaging studies. Biol Psychiatry 1997; 41(1): 86–106

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Salo KI, Scharfen J, Wilden ID, Schubotz RI, Holling H. Confining the concept of vascular depression to late-onset depression: a metaanalysis of MRI-defined hyperintensity burden in major depressive disorder and bipolar disorder. Front Psychol 2019; 10: 1241

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Herrmann LL, Le Masurier M, Ebmeier KP. White matter hyperintensities in late life depression: a systematic review. J Neurol Neurosurg Psychiatry 2008; 79(6): 619–624

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Takahashi K, Oshima A, Ida I, Kumano H, Yuuki N, Fukuda M, Amanuma M, Endo K, Mikuni M. Relationship between age at onset and magnetic resonance image-defined hyperintensities in mood disorders. J Psychiatr Res 2008; 42(6): 443–450

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Park JH, Lee SB, Lee JJ, Yoon JC, Han JW, Kim TH, Jeong HG, Newhouse PA, Taylor WD, Kim JH, Woo JI, Kim KW. Epidemiology of MRI-defined vascular depression: a longitudinal, community-based study in Korean elders. J Affect Disord 2015; 180: 200–206

    PubMed  Article  Google Scholar 

  43. 43.

    Yanai I, Fujikawa T, Horiguchi J, Yamawaki S, Touhouda Y. The 3-year course and outcome of patients with major depression and silent cerebral infarction. J Affect Disord 1998; 47(1–3): 25–30

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Sheline YI, Pieper CF, Barch DM, Welsh-Bohmer K, McKinstry RC, MacFall JR, D’Angelo G, Garcia KS, Gersing K, Wilkins C, Taylor W, Steffens DC, Krishnan RR, Doraiswamy PM. Support for the vascular depression hypothesis in late-life depression: results of a 2-site, prospective, antidepressant treatment trial. Arch Gen Psychiatry 2010; 67(3): 277–285

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Simpson S, Baldwin RC, Jackson A, Burns AS. Is subcortical disease associated with a poor response to antidepressants? Neurological, neuropsychological and neuroradiological findings in late-life depression. Psychol Med 1998; 28(5): 1015–1026

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Sneed JR, Culang-Reinlieb ME. The vascular depression hypothesis: an update. Am J Geriatr Psychiatry 2011; 19(2): 99–103

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Aizenstein HJ, Khalaf A, Walker SE, Andreescu C. Magnetic resonance imaging predictors of treatment response in late-life depression. J Geriatr Psychiatry Neurol 2014; 27(1): 24–32

    PubMed  Article  Google Scholar 

  48. 48.

    Aizenstein HJ, Baskys A, Boldrini M, Butters MA, Diniz BS, Jaiswal MK, Jellinger KA, Kruglov LS, Meshandin IA, Mijajlovic MD, Niklewski G, Pospos S, Raju K, Richter K, Steffens DC, Taylor WD, Tene O. Vascular depression consensus report—a critical update. BMC Med 2016; 14(1): 161

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Foland-Ross LC, Sacchet MD, Prasad G, Gilbert B, Thompson PM, Gotlib IH. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci 2015; 46(1): 125–131

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Frodl TS, Koutsouleris N, Bottlender R, Born C, Jäger M, Scupin I, Reiser M, Möller HJ, Meisenzahl EM. Depression-related variation in brain morphology over 3 years: effects of stress? Arch Gen Psychiatry 2008; 65(10): 1156–1165

    PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Kanai T, Takeuchi H, Furukawa TA, Yoshimura R, Imaizumi T, Kitamura T, Takahashi K. Time to recurrence after recovery from major depressive episodes and its predictors. Psychol Med 2003; 33(5): 839–845

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Soriano-Mas C, Hernández-Ribas R, Pujol J, Urretavizcaya M, Deus J, Harrison BJ, Ortiz H, López-Solà M, Menchón JM, Cardoner N. Cross-sectional and longitudinal assessment of structural brain alterations in melancholic depression. Biol Psychiatry 2011; 69(4): 318–325

    PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Zaremba D, Dohm K, Redlich R, Grotegerd D, Strojny R, Meinert S, Bürger C, Enneking V, Förster K, Repple J, Opel N, Baune BT, Zwitserlood P, Heindel W, Arolt V, Kugel H, Dannlowski U. Association of brain cortical changes with relapse in patients with major depressive disorder. JAMA Psychiatry 2018; 75(5): 484–492

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Sawyer K, Corsentino E, Sachs-Ericsson N, Steffens DC. Depression, hippocampal volume changes, and cognitive decline in a clinical sample of older depressed outpatients and non-depressed controls. Aging Ment Health 2012; 16(6): 753–762

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Malykhin NV, Carter R, Seres P, Coupland NJ. Structural changes in the hippocampus in major depressive disorder: contributions of disease and treatment. J Psychiatry Neurosci 2010; 35(5): 337–343

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Lorenzetti V, Allen NB, Fornito A, Yücel M. Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. J Affect Disord 2009; 117(1–2): 1–17

    PubMed  Article  Google Scholar 

  57. 57.

    Schmaal L, Veltman DJ, van Erp TG, Sämann PG, Frodl T, Jahanshad N, Loehrer E, Tiemeier H, Hofman A, Niessen WJ, Vernooij MW, Ikram MA, Wittfeld K, Grabe HJ, Block A, Hegenscheid K, Völzke H, Hoehn D, Czisch M, Lagopoulos J, Hatton SN, Hickie IB, Goya-Maldonado R, Krämer B, Gruber O, Couvy-Duchesne B, Rentería ME, Strike LT, Mills NT, de Zubicaray GI, McMahon KL, Medland SE, Martin NG, Gillespie NA, Wright MJ, Hall GB, MacQueen GM, Frey EM, Carballedo A, van Velzen LS, van Tol MJ, van der Wee NJ, Veer IM, Walter H, Schnell K, Schramm E, Normann C, Schoepf D, Konrad C, Zurowski B, Nickson T, McIntosh AM, Papmeyer M, Whalley HC, Sussmann JE, Godlewska BR, Cowen PJ, Fischer FH, Rose M, Penninx BW, Thompson PM, Hibar DP. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry 2016; 21(6): 806–812

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Maller JJ, Broadhouse K, Rush AJ, Gordon E, Koslow S, Grieve SM. Increased hippocampal tail volume predicts depression status and remission to anti-depressant medications in major depression. Mol Psychiatry 2018; 23(8): 1737–1744

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Frodl T, Jäger M, Smajstrlova I, Born C, Bottlender R, Palladino T, Reiser M, Möller HJ, Meisenzahl EM. 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(5): 423–430

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Kronmüller KT, Pantel J, Köhler S, Victor D, Giesel F, Magnotta VA, Mundt C, Essig M, Schröder J. Hippocampal volume and 2-year outcome in depression. Br J Psychiatry 2008; 192(6): 472–473

    PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Colle R, Dupong I, Colliot O, Deflesselle E, Hardy P, Falissard B, Ducreux D, Chupin M, Corruble E. Smaller hippocampal volumes predict lower antidepressant response/remission rates in depressed patients: a meta-analysis. World J Biol Psychiatry 2018; 19(5): 360–367

    PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Hu X, Zhang L, Hu X, Lu L, Tang S, Li H, Bu X, Gong Q, Huang X. Abnormal hippocampal subfields may be potential predictors of worse early response to antidepressant treatment in drug-naïve patients with major depressive disorder. J Magn Reson Imaging 2019; 49(6): 1760–1768

    PubMed  Article  PubMed Central  Google Scholar 

  63. 63.

    Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V, Fu CH. Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage 2011; 56(2): 809–813

    PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Ito H, Inoue K, Goto R, Kinomura S, Taki Y, Okada K, Sato K, Sato T, Kanno I, Fukuda H. Database of normal human cerebral blood flow measured by SPECT: I. Comparison between I-123-IMP, Tc-99m-HMPAO, and Tc-99m-ECD as referred with O-15 labeled water PET and voxel-based morphometry. Ann Nucl Med 2006; 20(2): 131–138

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Lameka K, Farwell MD, Ichise M. Positron emission tomography. Handb Clin Neurol 2016; 135: 209–227

    PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Lui S, Parkes LM, Huang X, Zou K, Chan RC, Yang H, Zou L, Li D, Tang H, Zhang T, Li X, Wei Y, Chen L, Sun X, Kemp GJ, Gong QY. Depressive disorders: focally altered cerebral perfusion measured with arterial spin-labeling MR imaging. Radiology 2009; 251(2): 476–484

    PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Su L, Cai Y, Xu Y, Dutt A, Shi S, Bramon E. Cerebral metabolism in major depressive disorder: a voxel-based meta-analysis of positron emission tomography studies. BMC Psychiatry 2014; 14(1): 321

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Smith DF, Jakobsen S. Molecular neurobiology of depression: PET findings on the elusive correlation with symptom severity. Front Psychiatry 2013; 4: 8

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Filippi M, Agosta F. Diffusion tensor imaging and functional MRI. Handb Clin Neurol 2016; 136: 1065–1087

    PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Azeez AK, Biswal BB. A review of resting-state analysis methods. Neuroimaging Clin N Am 2017; 27(4): 581–592

    PubMed  Article  Google Scholar 

  71. 71.

    Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8(9): 700–711

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med 2009; 62(6): 1619–1628

    PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Sundermann B, Feder S, Wersching H, Teuber A, Schwindt W, Kugel H, Heindel W, Arolt V, Berger K, Pfleiderer B. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna) 2017; 124(5): 589–605

    Article  Google Scholar 

  74. 74.

    Zhong X, Shi H, Ming Q, Dong D, Zhang X, Zeng LL, Yao S. Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. J Affect Disord 2017; 218: 346–352

    PubMed  Article  Google Scholar 

  75. 75.

    Bhaumik R, Jenkins LM, Gowins JR, Jacobs RH, Barba A, Bhaumik DK, Langenecker SA. Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. Neuroimage Clin 2017; 16: 390–398

    PubMed  Article  Google Scholar 

  76. 76.

    Zeng LL, Shen H, Liu L, Hu D. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp 2014; 35(4): 1630–1641

    PubMed  Article  Google Scholar 

  77. 77.

    Jing B, Long Z, Liu H, Yan H, Dong J, Mo X, Li D, Liu C, Li H. Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases. Oncotarget 2017; 8(52): 90452–90464

    PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Yoshida K, Shimizu Y, Yoshimoto J, Takamura M, Okada G, Okamoto Y, Yamawaki S, Doya K. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS One 2017; 12(7): e0179638

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  79. 79.

    Wang X, Ren Y, Zhang W. Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features. Comput Math Methods Med 2017; 2017: 3609821

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Ramasubbu R, Brown MR, Cortese F, Gaxiola I, Goodyear B, Greenshaw AJ, Dursun SM, Greiner R. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin 2016; 12: 320–331

    PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Wei M, Qin J, Yan R, Li H, Yao Z, Lu Q. Identifying major depressive disorder using Hurst exponent of resting-state brain networks. Psychiatry Res 2013; 214(3): 306–312

    PubMed  Article  Google Scholar 

  82. 82.

    Cao L, Guo S, Xue Z, Hu Y, Liu H, Mwansisya TE, Pu W, Yang B, Liu C, Feng J, Chen EY, Liu Z. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci 2014; 68(2): 110–119

    PubMed  Article  Google Scholar 

  83. 83.

    Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 2012; 135(5): 1498–1507

    PubMed  Article  Google Scholar 

  84. 84.

    Sundermann B, Feder S, Wersching H, Teuber A, Schwindt W, Kugel H, Heindel W, Arolt V, Berger K, Pfleiderer B. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna) 2017; 124(5): 589–605

    Article  Google Scholar 

  85. 85.

    Lord A, Horn D, Breakspear M, Walter M. Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 2012; 7(8): e41282

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Cáceda R, Bush K, James GA, Stowe ZN, Kilts CD. Modes of resting functional brain organization differentiate suicidal thoughts and actions: a preliminary study. J Clin Psychiatry 2018; 79(4): 17m11901

    Article  Google Scholar 

  87. 87.

    Dinga R, Schmaal L, Penninx BWJH, van Tol MJ, Veltman DJ, van Velzen L, Mennes M, van der Wee NJA, Marquand AF. Evaluating the evidence for biotypes of depression: methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin 2019; 22: 101796

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Pan PM, Sato JR, Salum GA, Rohde LA, Gadelha A, Zugman A, Mari J, Jackowski A, Picon F, Miguel EC, Pine DS, Leibenluft E, Bressan RA, Stringaris A. Ventral striatum functional connectivity as a predictor of adolescent depressive disorder in a longitudinal community-based sample. Am J Psychiatry 2017; 174(11): 1112–1119

    PubMed  Article  Google Scholar 

  89. 89.

    Shapero BG, Chai XJ, Vangel M, Biederman J, Hoover CS, Whitfield-Gabrieli S, Gabrieli JDE, Hirshfeld-Becker DR. Neural markers of depression risk predict the onset of depression. Psychiatry Res Neuroimaging 2019; 285: 31–39

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Hirshfeld-Becker DR, Gabrieli JDE, Shapero BG, Biederman J, Whitfield-Gabrieli S, Chai XJ. Intrinsic functional brain connectivity predicts onset of major depression disorder in adolescence: a pilot study. Brain Connect 2019; 9(5): 388–398

    PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Langenecker SA, Jenkins LM, Stange JP, Chang YS, DelDonno SR, Bessette KL, Passarotti AM, Bhaumik R, Ajilore O, Jacobs RH. Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression. Neuroimage Clin 2018; 20: 1001–1009

    PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Farb NA, Anderson AK, Bloch RT, Segal ZV. Mood-linked responses in medial prefrontal cortex predict relapse in patients with recurrent unipolar depression. Biol Psychiatry 2011; 70(4): 366–372

    PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Lui S, Wu Q, Qiu L, Yang X, Kuang W, Chan RC, Huang X, Kemp GJ, Mechelli A, Gong Q. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry 2011; 168(6): 642–648

    PubMed  Article  Google Scholar 

  94. 94.

    Grimaldi G, Argyropoulos GP, Boehringer A, Celnik P, Edwards MJ, Ferrucci R, Galea JM, Groiss SJ, Hiraoka K, Kassavetis P, Lesage E, Manto M, Miall RC, Priori A, Sadnicka A, Ugawa Y, Ziemann U. Non-invasive cerebellar stimulation—a consensus paper. Cerebellum 2014; 13(1): 121–138

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Paus T, Barrett J. Transcranial magnetic stimulation (TMS) of the human frontal cortex: implications for repetitive TMS treatment of depression. J Psychiatry Neurosci 2004; 29(4): 268–279

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Rossi S, Hallett M, Rossini PM, Pascual-Leone A, Safety of TMS Consensus Group. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 2009; 120(12): 2008–2039

    PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Turriziani P, Smirni D, Zappalà G, Mangano GR, Oliveri M, Cipolotti L. Enhancing memory performance with rTMS in healthy subjects and individuals with mild cognitive impairment: the role of the right dorsolateral prefrontal cortex. Front Hum Neurosci 2012; 6: 62

    PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Herrmann LL, Ebmeier KP. Factors modifying the efficacy of transcranial magnetic stimulation in the treatment of depression: a review. J Clin Psychiatry 2006; 67(12): 1870–1876

    PubMed  Article  Google Scholar 

  99. 99.

    Salomons TV, Dunlop K, Kennedy SH, Flint A, Geraci J, Giacobbe P, Downar J. Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder. Neuropsychopharmacology 2014; 39(2): 488–498

    PubMed  Article  Google Scholar 

  100. 100.

    Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, Voss HU, Casey BJ, Etkin A, Dubin MJ. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry 2014; 76(7): 517–526

    PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Moret C. Combination/augmentation strategies for improving the treatment of depression. Neuropsychiatr Dis Treat 2005; 1(4): 301–309

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Gaynes BN, Dusetzina SB, Ellis AR, Hansen RA, Farley JF, Miller WC, Stürmer T. Treating depression after initial treatment failure: directly comparing switch and augmenting strategies in STAR*D. J Clin Psychopharmacol 2012; 32(1): 114–119

    PubMed  Article  Google Scholar 

  103. 103.

    Craighead WE, Dunlop BW. Combination psychotherapy and antidepressant medication treatment for depression: for whom, when, and how. Annu Rev Psychol 2014; 65(1): 267–300

    PubMed  Article  Google Scholar 

  104. 104.

    McGrath CL, Kelley ME, Dunlop BW, Holtzheimer PE 3rd, Craighead WE, Mayberg HS. Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol Psychiatry 2014; 76(7): 527–535

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Konarski JZ, Kennedy SH, Segal ZV, Lau MA, Bieling PJ, McIntyre RS, Mayberg HS. Predictors of nonresponse to cognitive behavioural therapy or venlafaxine using glucose metabolism in major depressive disorder. J Psychiatry Neurosci 2009; 34(3): 175–180

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Dougherty DD, Weiss AP, Cosgrove GR, Alpert NM, Cassem EH, Nierenberg AA, Price BH, Mayberg HS, Fischman AJ, Rauch SL. Cerebral metabolic correlates as potential predictors of response to anterior cingulotomy for treatment of major depression. J Neurosurg 2003; 99(6): 1010–1017

    PubMed  Article  Google Scholar 

  107. 107.

    Mayberg HS, Lozano AM, Voon V, McNeely HE, Seminowicz D, Hamani C, Schwalb JM, Kennedy SH. Deep brain stimulation for treatment-resistant depression. Neuron 2005; 45(5): 651–660

    CAS  PubMed  Article  Google Scholar 

  108. 108.

    Conway CR, Chibnall JT, Gangwani S, Mintun MA, Price JL, Hershey T, Giuffra LA, Bucholz RD, Christensen JJ, Sheline YI. Pretreatment cerebral metabolic activity correlates with antidepressant efficacy of vagus nerve stimulation in treatment-resistant major depression: a potential marker for response? J Affect Disord 2012; 139(3): 283–290

    PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Siegle GJ, Thompson WK, Collier A, Berman SR, Feldmiller J, Thase ME, Friedman ES. Toward clinically useful neuroimaging in depression treatment: prognostic utility of subgenual cingulate activity for determining depression outcome in cognitive therapy across studies, scanners, and patient characteristics. Arch Gen Psychiatry 2012; 69(9): 913–924

    PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Davidson RJ, Irwin W, Anderle MJ, Kalin NH. The neural substrates of affective processing in depressed patients treated with venlafaxine. Am J Psychiatry 2003; 160(1): 64–75

    PubMed  Article  Google Scholar 

  111. 111.

    Kennedy SH, Evans KR, Krüger S, Mayberg HS, Meyer JH, McCann S, Arifuzzman AI, Houle S, Vaccarino FJ. Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry 2001; 158(6): 899–905

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Kennedy SH, Konarski JZ, Segal ZV, Lau MA, Bieling PJ, McIntyre RS, Mayberg HS. Differences in brain glucose metabolism between responders to CBT and venlafaxine in a 16-week randomized controlled trial. Am J Psychiatry 2007; 164(5): 778–788

    PubMed  Article  Google Scholar 

  113. 113.

    Dunlop BW, Rajendra JK, Craighead WE, Kelley ME, McGrath CL, Choi KS, Kinkead B, Nemeroff CB, Mayberg HS. 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(6): 533–545

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    McGrath CL, Kelley ME, Holtzheimer PE, Dunlop BW, Craighead WE, Franco AR, Craddock RC, Mayberg HS. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 2013; 70(8): 821–829

    PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92(1101): 20181000

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Krueger G, Granziera C, Jack CR Jr, Gunter JL, Littmann A, Mortamet B, Kannengiesser S, Sorensen AG, Ward CP, Reyes DA, Britson PJ, Fischer H, Bernstein MA. Effects of MRI scan acceleration on brain volume measurement consistency. J Magn Reson Imaging 2012; 36(5): 1234–1240

    PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Caramanos Z, Fonov VS, Francis SJ, Narayanan S, Pike GB, Collins DL, Arnold DL. Gradient distortions in MRI: characterizing and correcting for their effects on SIENA-generated measures of brain volume change. Neuroimage 2010; 49(2): 1601–1611

    PubMed  Article  Google Scholar 

  118. 118.

    Preboske GM, Gunter JL, Ward CP, Jack CR Jr. Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 2006; 30(4): 1196–1202

    PubMed  Article  Google Scholar 

  119. 119.

    Lee H, Nakamura K, Narayanan S, Brown RA, Arnold DL, Alzheimer’s Disease Neuroimaging Initiative. Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements. Neuroimage 2019; 184: 555–565

    PubMed  Article  Google Scholar 

  120. 120.

    MR Group of Chinese Society of Radiology, Chinese Medical Association. Chinese guidelines for the standardized application of MRI brain structure imaging technique in schizophrenia. Chin J Radiol (Zhonghua Fang She Xue Za Zhi) 2019; 53: 170–176 (in Chinese)

    Google Scholar 

  121. 121.

    Smieskova R, Allen P, Simon A, Aston J, Bendfeldt K, Drewe J, Gruber K, Gschwandtner U, Klarhoefer M, Lenz C, Scheffler K, Stieglitz RD, Radue EW, McGuire P, Riecher-Rössler A, Borgwardt SJ. Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study. Hum Brain Mapp 2012; 33(10): 2281–2294

    PubMed  Article  Google Scholar 

  122. 122.

    Fusar-Poli P, Howes OD, Allen P, Broome M, Valli I, Asselin MC, Grasby PM, McGuire PK. Abnormal frontostriatal interactions in people with prodromal signs of psychosis: a multimodal imaging study. Arch Gen Psychiatry 2010; 67(7): 683–691

    PubMed  Article  Google Scholar 

  123. 123.

    Kessler RC, Gruber M, Hettema JM, Hwang I, Sampson N, Yonkers KA. Co-morbid major depression and generalized anxiety disorders in the National Comorbidity Survey follow-up. Psychol Med 2008; 38(3): 365–374

    CAS  PubMed  Article  Google Scholar 

  124. 124.

    Coutinho JF, Fernandesl SV, Soares JM, Maia L, Gonçalves OF, Sampaio A. Default mode network dissociation in depressive and anxiety states. Brain Imaging Behav 2016; 10(1): 147–157

    PubMed  Article  Google Scholar 

  125. 125.

    Fonseka TM, MacQueen GM, Kennedy SH. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. J Affect Disord 2018; 233: 21–35

    PubMed  Article  Google Scholar 

  126. 126.

    Enneking V, Leehr EJ, Dannlowski U, Redlich R. Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies. Psychol Med 2020; 50(2): 187–209

    PubMed  Article  Google Scholar 

  127. 127.

    Etkin A. A reckoning and research agenda for neuroimaging in psychiatry. Am J Psychiatry 2019; 176(7): 507–511

    PubMed  Article  Google Scholar 

  128. 128.

    Serpa MH, Ou Y, Schaufelberger MS, Doshi J, Ferreira LK, Machado-Vieira R, Menezes PR, Scazufca M, Davatzikos C, Busatto GF, Zanetti MV. Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability. BioMed Res Int 2014; 2014: 706157

    PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Mwangi B, Ebmeier KP, Matthews K, Steele JD. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012; 135(5): 1508–1521

    PubMed  Article  Google Scholar 

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Acknowledgments

This study was supported by the National Natural Science Foundation (Nos. 81171488, 81671669, 81621003, 81820108018, and 81801681), Program for Scholars and Innovative Research Team in University (PCSIRT, No. IRT16R52) of China.

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Correspondence to Xiaoqi Huang or Qiyong Gong.

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Ziqi Chen, Xiaoqi Huang, Qiyong Gong, and Bharat B. Biswal declare that they have no financial conflicts of interest. This manuscript is a review article and does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

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Chen, Z., Huang, X., Gong, Q. et al. Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front. Med. (2021). https://doi.org/10.1007/s11684-020-0798-1

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Keywords

  • psychoradiology
  • major depressive disorder
  • MRI
  • biomarker