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Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

Molecular Psychiatry volume 30, pages 825–837 (2025)Cite this article

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Abstract

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = −2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

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Fig. 1: Overall random effects model forest plot of the logarithm of diagnostic odds ratios in clinical and MRI studies.
Fig. 2: Summary receiver operator characteristic (SROC) curve within clinical and MRI studies.
Fig. 3: Results of multiple regression model in MRI group by the elastic net algorithm predicting log(DOR) of individual studies.
Fig. 4: Summary schematic representation of brain MRI features predicting treatment outcomes of major depressive disorder.

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Data availability

The data that support the findings of this study, along with the code utilized in the Methods section, are available from the corresponding authors upon reasonable request.

References

  1. World Health Organization. Depression fact sheet. World Health Organization. Published December 2019. Accessed November 20, 2022. https://www.who.int/mediacentre/factsheets/fs369/en/.

  2. Chen X, Lu B, Li HX, Li XY, Wang YW, Castellanos FX, et al. The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. Psychoradiology. 2022;2:32–42.

    PubMed  PubMed Central  Google Scholar 

  3. Jain FA, Connolly CG, Reus VI, Meyerhoff DJ, Yang TT, Mellon SH, et al. Cortisol, moderated by age, is associated with antidepressant treatment outcome and memory improvement in Major Depressive Disorder: A retrospective analysis. Psychoneuroendocrinology. 2019;109:104386.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Sramek JJ, Murphy MF, Cutler NR. Sex differences in the psychopharmacological treatment of depression. Dialogues Clin Neurosci. 2016;18:447–57.

    PubMed  PubMed Central  Google Scholar 

  5. Riedel M, Möller HJ, Obermeier M, Adli M, Bauer M, Kronmüller K, et al. Clinical predictors of response and remission in inpatients with depressive syndromes. J Affect Disord. 2011;133:137–49.

    PubMed  Google Scholar 

  6. Davis AK, Barrett FS, May DG, Cosimano MP, Sepeda ND, Johnson MW, et al. Effects of Psilocybin-Assisted Therapy on Major Depressive Disorder: A Randomized Clinical Trial. JAMA Psychiatry. 2021;78:481–89.

    PubMed  Google Scholar 

  7. Zhou Y, Zhang Z, Wang C, Lan X, Li W, Zhang M, et al. Predictors of 4-week antidepressant outcome in patients with first-episode major depressive disorder: An ROC curve analysis. J Affect Disord. 2022;304:59–65.

    CAS  PubMed  Google Scholar 

  8. Lee Y, Ragguett RM, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. J Affect Disord. 2018;241:519–32.

    PubMed  Google Scholar 

  9. Carstens L, Hartling C, Stippl A, Domke A-K, Herrera-Mendelez AL, Aust S, et al. A symptom-based approach in predicting ECT outcome in depressed patients employing MADRS single items. Eur Arc Psychiatry Clin Neurosci. 2021;271:1275–84.

    Google Scholar 

  10. Krepel N, Rush AJ, Iseger TA, Sack AT, Arns M. Can psychological features predict antidepressant response to rTMS? A Discovery-Replication approach. Psychol Med. 2020;50:264–72.

    PubMed  Google Scholar 

  11. Kautzky A, Dold M, Bartova L, Spies M, Vanicek T, Souery D, et al. Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample. J Clin Psychiatry. 2018;79:16m11385.

  12. Li F, Sun H, Biswal BB, Sweeney JA, Gong Q. Artificial intelligence applications in psychoradiology. Psychoradiology. 2021;1:94–107.

    PubMed  PubMed Central  Google Scholar 

  13. Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol. 2022;67:23TR01.

  14. You W, Luo L, Yao L, Zhao Y, Li Q, Wang Y, et al. Impaired dynamic functional brain properties and their relationship to symptoms in never treated first-episode patients with schizophrenia. Schizophrenia (Heidelb). 2022;8:90.

    PubMed  Google Scholar 

  15. Luo L, Li Q, Wang Y, He N, Wang Y, You W, et al. Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia. Schizophr Bull. 2023;49:1387–98.

    PubMed  PubMed Central  Google Scholar 

  16. Moreno-Ortega M, Prudic J, Rowny S, Patel GH, Kangarlu A, Lee S, et al. Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression. Sci Rep. 2019;9:5071.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Godlewska BR, Browning M, Norbury R, Igoumenou A, Cowen PJ, Harmer CJ. Predicting Treatment Response in Depression: The Role of Anterior Cingulate Cortex. Int J Neuropsychopharmacol. 2018;21:988–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hu X, Zhang L, Hu X, Lu L, Tang S, Li H, et al. 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;49:1760–68.

    PubMed  Google Scholar 

  19. Wu P, Zhang A, Sun N, Lei L, Liu P, Wang Y, et al. Cortical Thickness Predicts Response Following 2 Weeks of SSRI Regimen in First-Episode, Drug-Naive Major Depressive Disorder: An MRI Study. Front Psychiatry. 2021;12:751756.

    PubMed  Google Scholar 

  20. Cohen SE, Zantvoord JB, Wezenberg BN, Bockting CLH, van Wingen GA. Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry. 2021;11:168.

    PubMed  PubMed Central  Google Scholar 

  21. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med. 2009;151:W65–94.

    PubMed  Google Scholar 

  22. Doebler P. mada: Meta-Analysis of Diagnostic Accuracy. R package version 0.5.11. https://r-forge.r-project.org/projects/mada/. 2022.

  23. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33:1–22.

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  25. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56:1129–35.

    PubMed  Google Scholar 

  26. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58:982–90.

    PubMed  Google Scholar 

  27. Devillé WL, Buntinx F, Bouter LM, Montori VM, de Vet HC, van der Windt DA, et al. Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol. 2002;2:9.

    PubMed  PubMed Central  Google Scholar 

  28. Jones CM, Athanasiou T. Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. Ann Thorac Surg. 2005;79:16–20.

    PubMed  Google Scholar 

  29. Gowin JL, Manza P, Ramchandani VA, Volkow ND. Neuropsychosocial markers of binge drinking in young adults. Mol Psychiatry. 2021;26:4931–43.

    PubMed  Google Scholar 

  30. Gosnell SN, Curtis KN, Velasquez K, Fowler JC, Madan A, Goodman W, et al. Habenular connectivity may predict treatment response in depressed psychiatric inpatients. J Affect Disord. 2019;242:211–19.

    PubMed  Google Scholar 

  31. Jackson D, Turner R. Power analysis for random-effects meta-analysis. Res Synth Methods. 2017;8:290–302.

    PubMed  PubMed Central  Google Scholar 

  32. Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58:882–93.

    PubMed  Google Scholar 

  33. Macaskill PTY, Deeks JJ, Gatsonis C. Chapter 9: Understanding meta-analysis. Draft version (4 October 2022) for inclusion in: Deeks JJ, Bossuyt PM, Leeflang MM, Takwoingi Y, editor(s). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 2. London: Cochrane.

  34. Leucht S, Fennema H, Engel RR, Kaspers-Janssen M, Szegedi A. Translating the HAM-D into the MADRS and vice versa with equipercentile linking. J Affect Disord. 2018;226:326–31.

    PubMed  Google Scholar 

  35. Furukawa TA, Reijnders M, Kishimoto S, Sakata M, DeRubeis RJ, Dimidjian S, et al. Translating the BDI and BDI-II into the HAMD and vice versa with equipercentile linking. Epidemiol Psychiatr Sci. 2019 ;29:e24.

    PubMed  PubMed Central  Google Scholar 

  36. Takwoingi YDN, Schiller I, Rücker G, Jones HE, Partlett C, Macaskill P. Chapter 10: Undertaking meta-analysis. Draft version (4 October 2022) for inclusion in: Deeks JJ, Bossuyt PM, Leeflang MM, Takwoingi Y, editor(s). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 2. London: Cochrane.

  37. Hopman HJ, Chan SMS, Chu WCW, Lu H, Tse CY, Chau SWH, et al. Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning. J Affect Disord. 2021;290:261–71.

    CAS  PubMed  Google Scholar 

  38. Nakamura T, Tomita M, Horikawa N, Ishibashi M, Uematsu K, Hiraki T, et al. Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression. Neuropsychopharmacol Rep. 2021;41:168–78.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. van Waarde JA, Scholte HS, van Oudheusden LJ, Verwey B, Denys D, van Wingen GA. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry. 2015;20:609–14.

    PubMed  Google Scholar 

  40. Goldstein-Piekarski AN, Staveland BR, Ball TM, Yesavage J, Korgaonkar MS, Williams LM. Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers. Transl Psychiatry. 2018;8:57.

    PubMed  PubMed Central  Google Scholar 

  41. Braund TA, Breukelaar IA, Griffiths K, Tillman G, Palmer DM, Bryant R, et al. Intrinsic Functional Connectomes Characterize Neuroticism in Major Depressive Disorder and Predict Antidepressant Treatment Outcomes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;7:276–84.

  42. Korgaonkar MS, Goldstein-Piekarski AN, Fornito A, Williams LM. Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder. Mol Psychiatry. 2020;25:1537–49.

    CAS  PubMed  Google Scholar 

  43. Siegle GJ, Thompson WK, Collier A, Berman SR, Feldmiller J, Thase ME, et al. 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;69:913–24.

    PubMed  PubMed Central  Google Scholar 

  44. Crane NA, Jenkins LM, Bhaumik R, Dion C, Gowins JR, Mickey BJ, et al. Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI. Brain. 2017;140:472–86.

    PubMed  PubMed Central  Google Scholar 

  45. Marquand AF, Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CH. Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport. 2008;19:1507–11.

    PubMed  Google Scholar 

  46. Costafreda SG, Khanna A, Mourao-Miranda J, Fu CH. Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. Neuroreport. 2009;20:637–41.

    PubMed  Google Scholar 

  47. Cao B, Luo Q, Fu Y, Du L, Qiu T, Yang X, et al. Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder. Sci Rep. 2018;8:5434.

    PubMed  PubMed Central  Google Scholar 

  48. Costafreda SG, Chu C, Ashburner J, Fu CH. Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One. 2009;4:e6353.

    PubMed  PubMed Central  Google Scholar 

  49. Wade BSC, Sui J, Njau S, Leaver AM, Vasvada M, Gutman BA, et al. DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS. Proc IEEE Int Symp Biomed Imaging 2017;2017:502–06.

    PubMed  PubMed Central  Google Scholar 

  50. Zhang F, Wang C, Lan X, Li W, Ye Y, Liu H, et al. Ketamine-induced hippocampal functional connectivity alterations associated with clinical remission in major depression. J Affect Disord. 2023;325:534–41.

    CAS  PubMed  Google Scholar 

  51. Zhang F, Wang C, Lan X, Li W, Fu L, Ye Y, et al. The functional connectivity of the middle frontal cortex predicts ketamine’s outcome in major depressive disorder. Front Neurosci. 2022;16:956056.

  52. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. JAMA Psychiatry. 2016;73:557–64.

    PubMed  Google Scholar 

  53. Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI, Deckersbach T, et al. Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis. Am J Psychiatry. 2019;176:44–56.

    PubMed  Google Scholar 

  54. Wagner S, Tadic A, Roll SC, Engel A, Dreimueller N, Engelmann J, et al. A combined marker of early non-improvement and the occurrence of melancholic features improve the treatment prediction in patients with Major Depressive Disorders. J Affect Disord. 2017;221:184–91.

    PubMed  Google Scholar 

  55. Lin E, Kuo P-H, Liu Y-L, Yu YWY, Yang AC, Tsai S-J. A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers. Front Psychiatry. 2018;9:290.

  56. Joyce JB, Grant CW, Liu D, MahmoudianDehkordi S, Kaddurah-Daouk R, Skime M, et al. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021;11:513.

  57. Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol. 2012;8:49–76.

    PubMed  Google Scholar 

  58. Rolls ET. The cingulate cortex and limbic systems for emotion, action, and memory. Brain Struct Funct. 2019;224:3001–18.

    PubMed  PubMed Central  Google Scholar 

  59. Höflich A, Michenthaler P, Kasper S, Lanzenberger R. Circuit Mechanisms of Reward, Anhedonia, and Depression. Int J Neuropsychopharmacol. 2019;22:105–18.

    PubMed  Google Scholar 

  60. Wu QZ, Li DM, Kuang WH, Zhang TJ, Lui S, Huang XQ, et al. Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI. Hum Brain Mapp. 2011;32:1290–9.

    PubMed  Google Scholar 

  61. Guo W, Liu F, Xue Z, Gao K, Liu Z, Xiao C, et al. Abnormal resting-state cerebellar-cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression. Prog Neuropsychopharmacol Biol Psychiatry. 2013;44:51–7.

    PubMed  Google Scholar 

  62. Ye Y, Wang C, Lan X, Li W, Fu L, Zhang F, et al. Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder. Neuroimage Clin. 2022;36:103230.

    PubMed  PubMed Central  Google Scholar 

  63. Xiao H, Yuan M, Li H, Li S, Du Y, Wang M, et al. Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder. J Affect Disord. 2021;291:315–21.

    PubMed  Google Scholar 

  64. Yamamura T, Okamoto Y, Okada G, Takaishi Y, Takamura M, Mantani A, et al. Association of thalamic hyperactivity with treatment-resistant depression and poor response in early treatment for major depression: a resting-state fMRI study using fractional amplitude of low-frequency fluctuations. Transl Psychiatry. 2016;6:e754.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Hou Z, Gong L, Zhi M, Yin Y, Zhang Y, Xie C, et al. Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. Brain Imaging Behav. 2018;12:1042–52.

    PubMed  Google Scholar 

  66. Lemke H, Romankiewicz L, Förster K, Meinert S, Waltemate L, Fingas SM, et al. Association of disease course and brain structural alterations in major depressive disorder. Depress Anxiety. 2022;39:441–51.

    PubMed  Google Scholar 

  67. Sheng W, Cui Q, Jiang K, Chen Y, Tang Q, Wang C, et al. Individual variation in brain network topology is linked to course of illness in major depressive disorder. Cereb Cortex. 2022;32:5301–10.

    PubMed  Google Scholar 

  68. Fried EI. Moving forward: how depression heterogeneity hinders progress in treatment and research. Expert Rev Neurother. 2017;17:423–25.

    CAS  PubMed  Google Scholar 

  69. Runge VM, Heverhagen JT. The Clinical Utility of Magnetic Resonance Imaging According to Field Strength, Specifically Addressing the Breadth of Current State-of-the-Art Systems, Which Include 0.55 T, 1.5 T, 3 T, and 7 T. Invest Radio. 2022;57:1–12.

    Google Scholar 

  70. Li Q, Zhao Y, Hu Y, Liu Y, Wang Y, Zhang Q, et al. Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder. EBioMedicine. 2024;106:105255.

    PubMed  PubMed Central  Google Scholar 

  71. Ai H, Opmeer EM, Marsman JC, Veltman DJ, van der Wee NJA, Aleman A, et al. Longitudinal brain changes in MDD during emotional encoding: effects of presence and persistence of symptomatology. Psychol Med. 2020;50:1316–26.

    PubMed  Google Scholar 

  72. Li Q, Yao L, You W, Liu J, Deng S, Li B, et al. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia. Schizophr Bull. 2023;49:659–68.

    PubMed  Google Scholar 

  73. Ai Y, Li F, Hou Y, Li X, Li W, Qin K, et al. Differential cortical gray matter changes in early- and late-onset patients with amyotrophic lateral sclerosis. Cereb Cortex. 2024;34:bhad426.

  74. Feng Y, Murphy MC, Hojo E, Li F, Roberts N. Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases. J Magn Reson Imaging. 2024;59:82–96.

    PubMed  Google Scholar 

  75. Wang Y, Li Q, Yao L, He N, Tang Y, Chen L, et al. Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer’s disease. Cereb Cortex. 2024;34:bhae094.

  76. You W, Li Q, Chen L, He N, Li Y, Long F, et al. Common and distinct cortical thickness alterations in youth with autism spectrum disorder and attention-deficit/hyperactivity disorder. BMC Med. 2024;22:92.

    PubMed  PubMed Central  Google Scholar 

  77. Hahn A, Wadsak W, Windischberger C, Baldinger P, Höflich AS, Losak J, et al. Differential modulation of the default mode network via serotonin-1A receptors. Proc Natl Acad Sci USA. 2012;109:2619–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Chen F, Madsen TM, Wegener G, Nyengaard JR. Repeated electroconvulsive seizures increase the total number of synapses in adult male rat hippocampus. Eur Neuropsychopharmacol. 2009;19:329–38.

    CAS  PubMed  Google Scholar 

  79. Rose D, Fleischmann P, Wykes T, Leese M, Bindman J. Patients’ perspectives on electroconvulsive therapy: systematic review. Bmj. 2003;326:1363.

    PubMed  PubMed Central  Google Scholar 

  80. Wade BS, Joshi SH, Njau S, Leaver AM, Vasavada M, Woods RP, et al. Effect of Electroconvulsive Therapy on Striatal Morphometry in Major Depressive Disorder. Neuropsychopharmacology. 2016;41:2481–91.

    PubMed  PubMed Central  Google Scholar 

  81. Fisher PM, Ozenne B, Ganz M, Frokjaer VG, Dam VN, Penninx BW, et al. Emotional faces processing in major depressive disorder and prediction of antidepressant treatment response: A NeuroPharm study. J Psychopharmacol. 2022;36:626–36.

    CAS  PubMed  Google Scholar 

  82. Brakemeier E-L, Luborzewski A, Danker-Hopfe H, Kathmann N, Bajbouj M. Positive predictors for antidepressive response to prefrontal repetitive transcranial magnetic stimulation (rTMS). J Psychiatr Res. 2007;41:395–403.

    PubMed  Google Scholar 

  83. Brakemeier E-L, Wilbertz G, Rodax S, Danker-Hopfe H, Zinka B, Zwanzger P, et al. Patterns of response to repetitive transcranial magnetic stimulation (rTMS) in major depression: Replication study in drug-free patients. J Affect Disord. 2008;108:59–70.

    PubMed  Google Scholar 

  84. Lin H-S, Lin C-H. Early improvement in HAMD-17 and HAMD-6 scores predicts ultimate response and remission for depressed patients treated with fluoxetine or ECT. J Affect Disord. 2019;245:91–97.

    CAS  PubMed  Google Scholar 

  85. Mohamed AK, Croarkin PE, Jha MK, Voort JLV. Early reduction in irritability is associated with improved outcomes among youth with depression: Findings from the AMOD study. J Affect Disord. 2023;324:77–81.

    PubMed  Google Scholar 

  86. Leaver AM, Wade B, Vasavada M, Hellemann G, Joshi SH, Espinoza R, et al. Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression. Front Psychiatry. 2018;9:92.

    PubMed  PubMed Central  Google Scholar 

  87. 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. 2017;23:28–38.

    CAS  PubMed  Google Scholar 

  88. Kautzky A, Baldinger-Melich P, Kranz GS, Vanicek T, Souery D, Montgomery S, et al. A New Prediction Model for Evaluating Treatment-Resistant Depression. J Clin Psychiatry. 2017 Feb;78:215–+.

    PubMed  Google Scholar 

  89. Perlis RH. A Clinical Risk Stratification Tool for Predicting Treatment Resistance in Major Depressive Disorder. Biol Psychiatry. 2013;74:7–14.

    PubMed  PubMed Central  Google Scholar 

  90. Su L, Zhang Y, Jia Y, Sun J, Mellor D, Yuan T-F, et al. Predictors of Electroconvulsive Therapy Outcome in Major Depressive Disorder. Int J Neuropsychopharmacol. 2023;26:53–60.

    CAS  PubMed  Google Scholar 

  91. Rezaei M, Bagheri MMS, Ahmadi M. Clinical and demographic predictors of response to anodal tDCS treatment in major depression disorder (MDD). J Psychiatr Res. 2021;138:68–74.

    PubMed  Google Scholar 

  92. Donse L, Padberg F, Sack AT, Rush AJ, Arns M. Simultaneous rTMS and psychotherapy in major depressive disorder: Clinical outcomes and predictors from a large naturalistic study. Brain Stimul. 2018;11:337–45.

    PubMed  Google Scholar 

  93. Xue SW, Kuai C, Xiao Y, Zhao L, Lan Z. Abnormal Dynamic Functional Connectivity of the Left RostrHippocampus in Predicting Antidepressant Efficacy in Major Depressive Disorder. Psychiatry Investig. 2022;19:562–69.

    PubMed  PubMed Central  Google Scholar 

  94. Wu H, Liu R, Zhou J, Feng L, Wang Y, Chen X, et al. Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks. Transl Psychiatry. 2022;12:391.

  95. Pei C, Sun Y, Zhu J, Wang X, Zhang Y, Zhang S, et al. Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder. J Magn Reson Imaging. 2020;52:161–71.

    PubMed  Google Scholar 

  96. Tian S, Sun Y, Shao J, Zhang S, Mo Z, Liu X, et al. Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex. Hum Brain Mapp. 2020;41:1249–60.

    PubMed  Google Scholar 

  97. Zhu J, Cai H, Yuan Y, Yue Y, Jiang D, Chen C, et al. Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder. Brain Imaging Behav. 2018;12:1768–74.

    PubMed  PubMed Central  Google Scholar 

  98. Cash RFH, Cocchi L, Anderson R, Rogachov A, Kucyi A, Barnett AJ, et al. A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression. Hum Brain Mapp. 2019;40:4618–29.

    PubMed  PubMed Central  Google Scholar 

  99. Ge R, Downar J, Blumberger DM, Daskalakis ZJ, Vila-Rodriguez F. Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up. Brain Stimul. 2020;13:206–14.

    PubMed  Google Scholar 

  100. Sun H, Jiang R, Qi S, Narr KL, Wade BS, Upston J, et al. Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data. Neuroimage Clin. 2020;26:102080.

    PubMed  Google Scholar 

  101. Meyer BM, Rabl U, Huemer J, Bartova L, Kalcher K, Provenzano J, et al. Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study. Transl Psychiatry. 2019;9:64.

    PubMed  PubMed Central  Google Scholar 

  102. Goldstein-Piekarski AN, Korgaonkar MS, Green E, Suppes T, Schatzberg AF, Hastie T, et al. Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants. Proc Natl Acad Sci USA. 2016;113:11955–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Williams LM, Korgaonkar MS, Song YC, Paton R, Eagles S, Goldstein-Piekarski A, et al. Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial. Neuropsychopharmacology. 2015;40:2398–408.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Korgaonkar MS, Williams LM, Song YJ, Usherwood T, Grieve SM. Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder. Br J Psychiatry. 2014;205:321–8.

    PubMed  Google Scholar 

  105. Queirazza F, Fouragnan E, Steele JD, Cavanagh J, Philiastides MG. Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression. Sci Adv. 2019;5:eaav4962.

    PubMed  PubMed Central  Google Scholar 

  106. Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, et al. Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage. 2011;55:1497–503.

    PubMed  Google Scholar 

  107. Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V, et al. Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage. 2011;56:809–13.

    PubMed  Google Scholar 

  108. Xu J, Li W, Bai T, Li J, Zhang J, Hu Q, et al. Volume of hippocampus-amygdala transition area predicts outcomes of electroconvulsive therapy in major depressive disorder: high accuracy validated in two independent cohorts. Psychol Med. 2022;53:4464–73.

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Funding

This study was supported by the National Key R&D Program (2022YFC2009900) and National Natural Science Foundation (Grant Nos. 82001795 to Youjin Zhao, and 82027808 to Qiyong Gong) of China, and Sichuan Science and Technology Program (2024NSFSC0653 to Fei Li).

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  1. These authors contributed equally: Fenghua Long, Yufei Chen, Qian Zhang.

Authors and Affiliations

  1. Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China

    Fenghua Long, Yufei Chen, Qian Zhang, Qian Li, Yaxuan Wang, Yitian Wang, Haoran Li, Youjin Zhao, John A. Sweeney, Qiyong Gong & Fei Li

  2. Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China

    Fenghua Long, Yufei Chen, Qian Zhang, Qian Li, Yaxuan Wang, Yitian Wang, Haoran Li, Youjin Zhao, Qiyong Gong & Fei Li

  3. Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA

    Robert K. McNamara, Melissa P. DelBello & John A. Sweeney

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  1. Fenghua Long
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  2. Yufei Chen
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  3. Qian Zhang
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  13. Fei Li
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Contributions

Fei Li conceptualized the study. Fenghua Long, Yufei Chen, Qian Zhang, John A. Sweeney, and Fei Li designed and drafted the manuscript. Fenghua Long, Yufei Chen, Qian Zhang, Youjin Zhao, Qian Li, Yitian Wang, Haoran Li, and Yaxuan Wang contributed to the literature search, data collection and analysis. Fenghua Long, Yufei Chen, Qian Zhang, Robert K. McNamara, Melissa P. DelBello, John A. Sweeney, Qiyong Gong, and Fei Li critically revised the manuscript, and contributed to the data analysis strategy and interpretation. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Fei Li.

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Competing interests

Melissa P. DelBello receives research support from national institutes of health (NIH), PCORI, Acadia, Allergan, Janssen, Johnson and Johnson, Lundbeck, Otsuka, Pfizer, and Sunovion. She is also a consultant, on the advisory board, or has received honoraria for speaking for Alkermes, Allergan, Assurex, CMEology, Janssen, Johnson and Johnson, Lundbeck, Myriad, Neuronetics, Otsuka, Pfizer, Sunovion, and Supernus. Robert K. McNamara has received research support from Martek Biosciences Inc, Royal DSM Nutritional Products, LLC, Inflammation Research Foundation, Ortho-McNeil Janssen, AstraZeneca, Eli Lilly, NARSAD, and NIH, and previously served on the scientific advisory board of the Inflammation Research Foundation. The remaining authors declare no potential conflicts of interest with regard to this manuscript.

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Long, F., Chen, Y., Zhang, Q. et al. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis. Mol Psychiatry 30, 825–837 (2025). https://doi.org/10.1038/s41380-024-02710-6

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