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Mood Disorders: Predictors of tDCS Response

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Transcranial Direct Current Stimulation in Neuropsychiatric Disorders

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Abstract

Detecting reliable predictors of response to tDCS treatment for mood disorders could potentially facilitate treatment stratification and guide personalized treatment decisions, thereby enhancing treatment efficiency and reducing redundant treatments. Furthermore, hypothesis generation based on predictive information could streamline future research by narrowing the choice of multiple treatment parameters to the most promising targets. While attempts to identify such predictors have been made across multiple domains, these have mostly applied post-hoc exploratory statistics, that likely suffer from poor generalizability. As a result, objective response prediction is currently not feasible. New research strategies relying on artificial intelligence and statistical learning methodology represent promising means to achieve more reliable, clinically translatable predictive models. To be successful, these rely on multisite collaboration to enhance dataset sizes, should include data from naturalistic clinical settings, and embrace an open science framework. At the moment, heuristic clinical decision-making based on RCT results and clinical reasoning should guide tDCS treatment.

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References

  1. Moffa AH, Martin D, Alonzo A, Bennabi D, Blumberger DM, Benseñor IM, et al. Efficacy and acceptability of transcranial direct current stimulation (tDCS) for major depressive disorder: an individual patient data meta-analysis. Prog Neuro Psychopharmacol Biol Psychiatry [Internet]. 2020;99:109836. Available from: https://doi.org/10.1016/j.pnpbp.2019.109836.

  2. Razza LB, Palumbo P, Moffa AH, Carvalho AF, Solmi M, Loo CK, et al. A systematic review and meta-analysis on the effects of transcranial direct current stimulation in depressive episodes. Depress Anxiety. 2020;37(7):594–608.

    Article  Google Scholar 

  3. Brunoni AR, Moffa AH, Sampaio B, Borrione L, Moreno ML, Fernandes RA, et al. Trial of electrical direct-current therapy versus escitalopram for depression. N Engl J Med. 2017;376(26):2523–33.

    Article  CAS  Google Scholar 

  4. Brunoni AR, Valiengo L, Baccaro A, Zanão TA, De Oliveira JF, Goulart A, et al. The sertraline vs electrical current therapy for treating depression clinical study. JAMA Psychiat. 2013;70(4):383–91.

    Article  CAS  Google Scholar 

  5. Padberg F, Kumpf U, Mansmann U, Palm U, Plewnia C, Langguth B, et al. Prefrontal transcranial direct current stimulation (tDCS) as treatment for major depression: study design and methodology of a multicenter triple blind randomized placebo controlled trial (DepressionDC). Eur Arch Psychiatry Clin Neurosci. 2017;

    Google Scholar 

  6. Bajbouj M, Aust S, Spies J, Herrera-Melendez AL, Mayer SV, Peters M, et al. PsychotherapyPlus: augmentation of cognitive behavioral therapy (CBT) with prefrontal transcranial direct current stimulation (tDCS) in major depressive disorder—study design and methodology of a multicenter double-blind randomized placebo-controlled tria. Eur Arch Psychiatry Clin Neurosci. 2018;

    Google Scholar 

  7. Sathappan A V., Luber BM, Lisanby SH. The dynamic duo: combining noninvasive brain stimulation with cognitive interventions. Prog Neuro Psychopharmacol Biol Psychiatry [Internet]. 2019;89:347–60. Available from: https://doi.org/10.1016/j.pnpbp.2018.10.006.

  8. Weller S, Nitsche MA, Plewnia C. Enhancing cognitive control training with transcranial direct current stimulation: a systematic parameter study. Brain Stimul. 2020;

    Google Scholar 

  9. Woods AJ, Antal A, Bikson M, Boggio PS, Brunoni AR, Celnik P, et al. A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin Neurophysiol. 2016;

    Google Scholar 

  10. Boggio PS, Rigonatti SP, Ribeiro RB, Myczkowski ML, Nitsche MA, Pascual-Leone A, et al. A randomized, double-blind clinical trial on the efficacy of cortical direct current stimulation for the treatment of major depression. Int J Neuropsychopharmacol [Internet]. 2008 [cited 2020 Aug 6];11(2):249–54. Available from: https://pubmed.ncbi.nlm.nih.gov/17559710/

  11. Jamil A, Batsikadze G, Kuo HI, Meesen RLJ, Dechent P, Paulus W, et al. Current intensity- and polarity-specific online and aftereffects of transcranial direct current stimulation: an fMRI study. Hum Brain Mapp. 2020;

    Google Scholar 

  12. Chew T, Ho KA, Loo CK. Inter- and intra-individual variability in response to transcranial direct current stimulation (tDCS) at varying current intensities. Brain Stimul [Internet] 2015;8(6):1130–7. Available from: https://doi.org/10.1016/j.brs.2015.07.031

  13. Wiethoff S, Hamada M. Rothwell JC. Brain Stimul: Variability in response to transcranial direct current stimulation of the motor cortex; 2014.

    Google Scholar 

  14. Jablensky A. Psychiatric classifications: validity and utility. World Psychiatry. 2016;15(1):26–31.

    Article  Google Scholar 

  15. Williams LM. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress Anxiety. 2017;

    Google Scholar 

  16. Bulubas L, Padberg F, Bueno PV, Duran F, Busatto G, Amaro E, et al. Antidepressant effects of tDCS are associated with prefrontal gray matter volumes at baseline: evidence from the ELECT-TDCS trial. Brain Stimul. 2019;12(5):1197–204.

    Article  Google Scholar 

  17. Martin DM, McClintock SM, Aaronson ST, Alonzo A, Husain MM, Lisanby SH, et al. Pre-treatment attentional processing speed and antidepressant response to transcranial direct current stimulation: results from an international randomized controlled trial. Brain Stimul [Internet]. 2018;11(6):1282–90. Available from: https://doi.org/10.1016/j.brs.2018.08.011.

  18. Kalu UG, Sexton CE, Loo CK, Ebmeier KP. Transcranial direct current stimulation in the treatment of major depression: a meta-analysis. Psychol Med. 2012;42(9):1791–800.

    Article  CAS  Google Scholar 

  19. Kambeitz J, Goerigk S, Gattaz W, Falkai P, Benseñor IM, Lotufo PA, et al. Clinical patterns differentially predict response to transcranial direct current stimulation (tDCS) and escitalopram in major depression: a machine learning analysis of the ELECT-TDCS study. J Affect Disord [Internet]. 2020;265(December 2019):460–7. Available from: https://doi.org/10.1016/j.jad.2020.01.118.

  20. Sampaio B, Tortella G, Borrione L, Moffa AH, Machado-Vieira R, Cretaz E, et al. Efficacy and safety of transcranial direct current stimulation as an add-on treatment for bipolar depression: a randomized clinical trial. JAMA Psychiat. 2018;75(2):158–66.

    Article  Google Scholar 

  21. D’Urso G, Dell’Osso B, Rossi R, Brunoni AR, Bortolomasi M, Ferrucci R, et al. Clinical predictors of acute response to transcranial direct current stimulation (tDCS) in major depression. J Affect Disord [Internet]. 2017;219(May):25–30. Available from: https://doi.org/10.1016/j.jad.2017.05.019

  22. Goerigk SA, Padberg F, Bühner M, Sarubin N, Kaster TS, Daskalakis ZJ, et al. Distinct trajectories of response to prefrontal tDCS in major depression: results from a 3-arm randomized controlled trial. Neuropsychopharmacology. 2020;

    Google Scholar 

  23. Sackeim HA, Roose SP, Burt T. Optimal length of antidepressant trials in late-life depression. J Clin Psychopharmacol. 2005;

    Google Scholar 

  24. Loo CK, Sachdev P, Martin D, Pigot M, Alonzo A, Malhi GS, et al. A double-blind, sham-controlled trial of transcranial direct current stimulation for the treatment of depression. Int J Neuropsychopharmacol. 2010;13(1):61–9.

    Article  Google Scholar 

  25. Bennabi D, Nicolier M, Monnin J, Tio G, Pazart L, Vandel P, et al. Pilot study of feasibility of the effect of treatment with tDCS in patients suffering from treatment-resistant depression treated with escitalopram. Clin Neurophysiol [Internet]. 2015;126(6):1185–9. Available from: https://doi.org/10.1016/j.clinph.2014.09.026

  26. Blumberger DM, Tran LC, Fitzgerald PB, Hoy KE, Daskalakis ZJ. A randomized double-blind sham-controlled study of transcranial direct current stimulation for treatment-resistant major depression. Front Psych. 2012;3(August):1–8.

    Google Scholar 

  27. Palm U, Hasan A, Strube W, Padberg F. tDCS for the treatment of depression: a comprehensive review. Eur Arch Psychiatry Clin Neurosci. 2016;266(8):681–94.

    Article  Google Scholar 

  28. Brunoni AR, Moffa AH, Fregni F, Palm U, Padberg F, Blumberger DM, et al. Transcranial direct current stimulation for acute major depressive episodes: meta-analysis of individual patient data. Br J Psychiatry. 2016;208(6):522–31.

    Article  Google Scholar 

  29. Heller AS, Johnstone T, Peterson MJ, Kolden GG, Kalin NH, Davidson RJ. Increased prefrontal cortex activity during negative emotion regulation as a predictor of depression symptom severity trajectory over 6 months. JAMA Psychiat. 2013;70(11):1181–9.

    Article  CAS  Google Scholar 

  30. Barlow DH, Sauer-Zavala S, Carl JR, Bullis JR, Ellard KK. The nature, diagnosis, and treatment of neuroticism: Back to the future. Clin Psychol Sci. 2014;2(3):344–65.

    Article  Google Scholar 

  31. Caspi A, Moffitt TE. All for one and one for all: mental disorders in one dimension. Am J Psychiatr. 2018;

    Google Scholar 

  32. Kaster TS, Downar J, Vila-Rodriguez F, Thorpe KE, Feffer K, Noda Y, et al. Trajectories of response to dorsolateral prefrontal rTMS in major depression: a three-D study. Am J Psychiatry. 2019;176(5):367–75.

    Article  Google Scholar 

  33. Hunter AM, Minzenberg MJ, Cook IA, Krantz DE, Levitt JG, Rotstein NM, et al. Concomitant medication use and clinical outcome of repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder. Brain Behav. 2019;

    Google Scholar 

  34. Turco CV, El-Sayes J, Locke MB, Chen R, Baker S, Nelson AJ. Effects of lorazepam and baclofen on short- and long-latency afferent inhibition. J Physiol. 2018;

    Google Scholar 

  35. Martin DM, Yeung K, Loo CK. Pre-treatment letter fluency performance predicts antidepressant response to transcranial direct current stimulation. J Affect Disord [Internet]. 2016;203:130–5. Available from: https://doi.org/10.1016/j.jad.2016.05.072

  36. Grimm S, Beck J, Schuepbach D, Hell D, Boesiger P, Bermpohl F, et al. Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: an fMRI study in severe major depressive disorder. Biol Psychiatry. 2008;63(4):369–76.

    Article  Google Scholar 

  37. Brunoni AR, Kemp AH, Shiozawa P, Cordeiro Q, Valiengo LCL, Goulart AC, et al. Impact of 5-HTTLPR and BDNF polymorphisms on response to sertraline versus transcranial direct current stimulation: implications for the serotonergic system. Eur Neuropsychopharmacol [Internet]. 2013;23(11):1530–40. Available from: https://doi.org/10.1016/j.euroneuro.2013.03.009

  38. Brunoni AR, Carracedo A, Amigo OM, Pellicer AL, Talib L, Carvalho AF, et al. Association of BDNF, HTR2A, TPH1, SLC6A4, and comt polymorphisms with tdcs and escitalopram efficacy: ancillary analysis of a double-blind, placebo-controlled trial. Brazilian J Psychiatry. 2020;42(2):128–35.

    Article  Google Scholar 

  39. Loo CK, Husain MM, McDonald WM, Aaronson S, O’Reardon JP, Alonzo A, et al. International randomized-controlled trial of transcranial direct current stimulation in depression. Brain Stimul. 2018;11(1):125–33.

    Article  Google Scholar 

  40. Fritsch B, Reis J, Martinowich K, Schambra HM, Ji Y, Cohen LG, et al. Direct current stimulation promotes BDNF-dependent synaptic plasticity : potential implications for motor learning. Neuron [Internet]. 2011;66(2):198–204. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2864780&tool=pmcentrez&rendertype=abstract

    Article  Google Scholar 

  41. Kishi T, Yoshimura R, Ikuta T, Iwata N. Brain-derived neurotrophic factor and major depressive disorder: Evidence from meta-analyses [Internet]. Vol. 8, Frontiers in Psychiatry. Frontiers Media S.A.; 2018 [cited 2020 Oct 27]. p. 17. Available from: /pmc/articles/PMC5776079/?report=abstract.

    Google Scholar 

  42. Nieratschker V, Kiefer C, Giel K, Krüger R, Plewnia C. The COMT Val/Met polymorphism modulates effects of tDCS on response inhibition. Brain Stimul. 2015;

    Google Scholar 

  43. Plewnia C, Zwissler B, Längst I, Maurer B, Giel K, Krüger R. Effects of transcranial direct current stimulation (tDCS) on executive functions: influence of COMT Val/met polymorphism. Cortex. 2013;

    Google Scholar 

  44. Hayek D, Antonenko D, Witte AV, Lehnerer SM, Meinzer M, Külzow N, et al. Impact of COMT val158met on tDCS-induced cognitive enhancement in older adults. Behav Brain Res. 2021;

    Google Scholar 

  45. Filmer HL, Ehrhardt SE, Shaw TB, Mattingley JB, Dux PE. The efficacy of transcranial direct current stimulation to prefrontal areas is related to underlying cortical morphology. NeuroImage. 2019;196:41–8.

    Article  Google Scholar 

  46. van der Meer D, Frei O, Kaufmann T, Chen C-H, Thompson WK, O’Connell KS, et al. Quantifying the polygenic architecture of the human cerebral cortex: extensive genetic overlap between cortical thickness and surface area. bioRxiv [Internet]. 2019;868307. Available from: http://biorxiv.org/content/early/2019/12/06/868307.abstract

  47. Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, et al. The genetic architecture of the human cerebral cortex. Science (80- ). 2020;

    Google Scholar 

  48. Nord CL, Halahakoon DC, Limbachya T, Charpentier C, Lally N, Walsh V, et al. Neural predictors of treatment response to brain stimulation and psychological therapy in depression: a double-blind randomized controlled trial. Neuropsychopharmacology. 2019;

    Google Scholar 

  49. 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;

    Google Scholar 

  50. Siddiqi SH, Taylor SF, Cooke D, Pascual-Leone A, George MS, Fox MD. Distinct symptom-specific treatment targets for circuit-based neuromodulation. Am J Psychiatry. 2020;177(5):435–46.

    Article  Google Scholar 

  51. Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol. 2000;

    Google Scholar 

  52. Fregni F, Boggio PS, Nitsche M, Marcolin MA, Rigonatti SP, Pascual-Leone A. Letters to the Editor Treatment of major depression with transcranial direct current stimulation Ephedrine-induced emergence of bipolar symptoms. Bipolar Disord. 2006;8:203–5.

    Article  Google Scholar 

  53. Ioannidis JPA. Why Most Published Research Findings Are False. PLoS Med [Internet]. 2005 [cited 2020 Feb 28];2(8):e124. Available from: https://doi.org/10.1371/journal.pmed.0020124

  54. Siontis GCM, Tzoulaki I, Castaldi PJ, Ioannidis JPA. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol [Internet]. 2015;68(1):25–34. Available from: https://doi.org/10.1016/j.jclinepi.2014.09.007

  55. Hyman SE. Revolution stalled. Sci Transl Med. 2012;

    Google Scholar 

  56. Insel TR, Cuthbert BN. Brain disorders? Precisely. Science (80- ). 2015;

    Google Scholar 

  57. Breiman L. Statistical modeling: the two cultures. Stat Sci. 2001;16(3):199–215.

    Article  Google Scholar 

  58. Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiat. 2020;77(5):534–40.

    Article  Google Scholar 

  59. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;

    Google Scholar 

  60. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev [Internet]. 1958 [cited 2020 Mar 26];65–386. Available from: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.588.3775

  61. Kotsiantis SB. Supervised machine learning: a review of classification techniques. Inform. 2007;31(3):249–68.

    Google Scholar 

  62. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. 2017 [cited 2020 Feb 23]; Available from: http://arxiv.org/abs/1711.05225

  63. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  Google Scholar 

  64. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688–702.e13.

    Article  CAS  Google Scholar 

  65. Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, et al. Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry. 2020;

    Google Scholar 

  66. Dwyer DB, Kalman JL, Budde M, Kambeitz J, Ruef A, Antonucci LA, et al. An investigation of psychosis subgroups with prognostic validation and exploration of genetic underpinnings: The PsyCourse study. JAMA Psychiat. 2020:1–11.

    Google Scholar 

  67. Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, et al. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry. 2016;3(10):935–46.

    Article  Google Scholar 

  68. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry [Internet] 2016;3(3):243–50. Available from: https://doi.org/10.1016/S2215-0366(15)00471-X

  69. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):1–8.

    Article  Google Scholar 

  70. Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337–40.

    Article  CAS  Google Scholar 

  71. Wilkinson J, Arnold KF, Murray EJ, Smeden M Van, Carr K, Sippy R, et al. Viewpoint Time to reality check the promises of machine learning- powered precision medicine. Lancet [Internet]. 2020;7500(20):1–4. Available from: https://doi.org/10.1016/S2589-7500(20)30200-4

  72. Winkelbeiner S, Leucht S, Kane JM, Homan P. Evaluation of differences in individual treatment response in schizophrenia Spectrum disorders: a meta-analysis. JAMA Psychiat. 2019;

    Google Scholar 

  73. Winkelbeiner S, Muscat W, Joanlanne A, Marousis N, Vetter S, Seifritz E, et al. Treatment effect variation in brain stimulation across psychiatric disorders 2020;1–12.

    Google Scholar 

  74. Senn S. Statistical pitfalls of personalized medicine. Nature. 2018;

    Google Scholar 

  75. Uher R, Muthén B, Souery D, Mors O, Jaracz J, Placentino A, et al. Trajectories of change in depression severity during treatment with antidepressants. Psychol Med. 2010;

    Google Scholar 

  76. 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 Psychiat. 2017;

    Google Scholar 

  77. Cearns M, Hahn T, Baune BT. Recommendations and future directions for supervised machine learning in psychiatry. Transl Psychiatry. 2019;

    Google Scholar 

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  1. Department of Psychiatry and Psychotherapy, LMU Munich, Munich, Germany

    Gerrit Burkhardt, Stephan Goerigk & Frank Padberg

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Editors and Affiliations

  1. Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil

    André R. Brunoni

  2. Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany

    Michael A. Nitsche

  3. Black Dog Institute & School of Psychiatry, University of New South Wales, Sydney, Australia

    Colleen K. Loo

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Burkhardt, G., Goerigk, S., Padberg, F. (2021). Mood Disorders: Predictors of tDCS Response. In: Brunoni, A.R., Nitsche, M.A., Loo, C.K. (eds) Transcranial Direct Current Stimulation in Neuropsychiatric Disorders. Springer, Cham. https://doi.org/10.1007/978-3-030-76136-3_22

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