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Using Neuroimaging and Electroencephalography for Prediction of Treatment Resistance in Psychiatric Disorders

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Treatment Resistance in Psychiatry

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Abstract

Earlier identification of psychiatric patients who are prone to treatment resistance could avoid the frustration of a trial-and-error approach and might facilitate the design of more optimized treatment regimens and setting of individualized level of care. Although current candidate biomarkers for psychiatric disorders await further validation, knowledge on candidate genomic and brain-based biomarkers is increasing rapidly. Thus, this chapter illustrates recent study findings regarding clinical application of brain-based biomarkers derived from patients for the prediction of response or resistance to treatment, as well as for improved design of clinical studies, to find more robust brain-based biomarkers of treatment response or resistance. First, for patients diagnosed with psychotic disorders, mood disorders, or anxiety disorders, changing patterns of structural-functional brain characteristics that result from treatment with pharmacotherapy, cognitive behavioral therapy, as well as direct brain stimulation will be reviewed. Second, we will show the brain-based predictors of treatment response at baseline. Third, we will turn from exploration based on groupwise predictive power to the individual-level prediction of treatment response and focus on the recent trends in machine learning-based studies in which brain-based biomarkers are applied as explanatory or predictive features.

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References

  1. Ganella EP, Bartholomeusz CF, Seguin C, Whittle S, Bousman C, Phassouliotis C, et al. Functional brain networks in treatment-resistant schizophrenia. Schizophr Res. 2017;184:73–81.

    Article  Google Scholar 

  2. Iniesta R, Malki K, Maier W, Rietschel M, Mors O, Hauser J, et al. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J Psychiatr Res. 2016;78:94–102.

    Article  Google Scholar 

  3. 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;78(2):215–22.

    Article  Google Scholar 

  4. Cepeda MS, Reps J, Fife D, Blacketer C, Stang P, Ryan P. Finding treatment-resistant depression in real-world data: how a data-driven approach compares with expert-based heuristics. Depress Anxiety. 2018;35(3):220–8.

    Article  CAS  Google Scholar 

  5. da Costa SC, Passos IC, Lowri C, Soares JC, Kapczinski F. Refractory bipolar disorder and neuroprogression. Prog Neuro-Psychopharmacol Biol Psychiatry. 2016;70:103–10.

    Article  Google Scholar 

  6. Kapczinski NS, Mwangi B, Cassidy RM, Librenza-Garcia D, Bermudez MB, Kauer-Sant’anna M, et al. Neuroprogression and illness trajectories in bipolar disorder. Expert Rev Neurother. 2017;17(3):277–85.

    Article  CAS  Google Scholar 

  7. Passos IC, Mwangi B, Vieta E, Berk M, Kapczinski F. Areas of controversy in neuroprogression in bipolar disorder. Acta Psychiatr Scand. 2016;134(2):91–103.

    Article  CAS  Google Scholar 

  8. Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013;74(1):7–14.

    Article  Google Scholar 

  9. Huhtaniska S, Jaaskelainen E, Hirvonen N, Remes J, Murray GK, Veijola J, et al. Long-term antipsychotic use and brain changes in schizophrenia – a systematic review and meta-analysis. Hum Psychopharmacol. 2017;32(2):e2574.

    Article  Google Scholar 

  10. Egerton A, Bhachu A, Merritt K, McQueen G, Szulc A, McGuire P. Effects of antipsychotic administration on brain glutamate in schizophrenia: a systematic review of longitudinal 1H-MRS studies. Front Psychiatry. 2017;8:66.

    Article  Google Scholar 

  11. Guo W, Liu F, Chen J, Wu R, Li L, Zhang Z, et al. Olanzapine modulation of long- and short-range functional connectivity in the resting brain in a sample of patients with schizophrenia. Eur Neuropsychopharmacol. 2017;27(1):48–58.

    Article  CAS  Google Scholar 

  12. Stip E, Cherbal A, Luck D, Zhornitsky S, Bentaleb LA, Lungu O. A neuroimaging study of emotion-cognition interaction in schizophrenia: the effect of ziprasidone treatment. Psychopharmacology. 2017;234(7):1045–58.

    Article  CAS  Google Scholar 

  13. Aguilar EJ, Corripio I, Garcia-Marti G, Grasa E, Marti-Bonmati L, Gomez-Anson B, et al. Emotional fMR auditory paradigm demonstrates normalization of limbic hyperactivity after cognitive behavior therapy for auditory hallucinations. Schizophr Res. 2018;193:304–12.

    Article  Google Scholar 

  14. Wise T, Cleare AJ, Herane A, Young AH, Arnone D. Diagnostic and therapeutic utility of neuroimaging in depression: an overview. Neuropsychiatr Dis Treat. 2014;10:1509–22.

    PubMed  PubMed Central  Google Scholar 

  15. Brennan BP, Admon R, Perriello C, LaFlamme EM, Athey AJ, Pizzagalli DA, et al. Acute change in anterior cingulate cortex GABA, but not glutamine/glutamate, mediates antidepressant response to citalopram. Psychiatry Res. 2017;269:9–16.

    Article  Google Scholar 

  16. Kraus C, Klobl M, Tik M, Auer B, Vanicek T, Geissberger N, et al. The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI. Mol Psychiatry. 2018.

    Google Scholar 

  17. Admon R, Kaiser RH, Dillon DG, Beltzer M, Goer F, Olson DP, et al. Dopaminergic enhancement of striatal response to reward in major depression. Am J Psychiatry. 2017;174(4):378–86.

    Article  Google Scholar 

  18. Rubin-Falcone H, Weber J, Kishon R, Ochsner K, Delaparte L, Dore B, et al. Longitudinal effects of cognitive behavioral therapy for depression on the neural correlates of emotion regulation. Psychiatry Res. 2018;271:82–90.

    Article  Google Scholar 

  19. Philip NS, Barredo J, van’t Wout-Frank M, Tyrka AR, Price LH, Carpenter LL. Network mechanisms of clinical response to transcranial magnetic stimulation in posttraumatic stress disorder and major depressive disorder. Biol Psychiatry. 2018;83(3):263–72.

    Article  Google Scholar 

  20. Pantazatos SP, Talati A, Schneier FR, Hirsch J. Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment. Neuropsychopharmacology. 2014;39(2):425–34.

    Article  CAS  Google Scholar 

  21. Mansson KNT, Salami A, Carlbring P, Boraxbekk CJ, Andersson G, Furmark T. Structural but not functional neuroplasticity one year after effective cognitive behaviour therapy for social anxiety disorder. Behav Brain Res. 2017;318:45–51.

    Article  Google Scholar 

  22. Dunlop K, Woodside B, Olmsted M, Colton P, Giacobbe P, Downar J. Reductions in cortico-striatal hyperconnectivity accompany successful treatment of obsessive-compulsive disorder with dorsomedial prefrontal rTMS. Neuropsychopharmacology. 2016;41(5):1395–403.

    Article  CAS  Google Scholar 

  23. Thorsen AL, van den Heuvel OA, Hansen B, Kvale G. Neuroimaging of psychotherapy for obsessive-compulsive disorder: a systematic review. Psychiatry Res. 2015;233(3):306–13.

    Article  Google Scholar 

  24. Hasey GM, Kiang M. A review of recent literature employing electroencephalographic techniques to study the pathophysiology, phenomenology, and treatment response of schizophrenia. Curr Psychiatry Rep. 2013;15(9):388.

    Article  Google Scholar 

  25. Alegre M, Molero P, Valencia M, Mayner G, Ortuno F, Artieda J. Atypical antipsychotics normalize low-gamma evoked oscillations in patients with schizophrenia. Psychiatry Res. 2017;247:214–21.

    Article  CAS  Google Scholar 

  26. Zheng L, Liu W, He W, Yu S, Zhong G. Altered effective brain connectivity at early response of antipsychotics in first-episode schizophrenia with auditory hallucinations. Clin Neurophysiol. 2017;128(6):867–74.

    Article  Google Scholar 

  27. Ravan M, Hasey G, Reilly JP, MacCrimmon D, Khodayari-Rostamabad A. A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy. Clin Neurophysiol. 2015;126(4):721–30.

    Article  Google Scholar 

  28. Baskaran A, Farzan F, Milev R, Brenner CA, Alturi S, Pat McAndrews M, et al. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: a pilot study. J Affect Disord. 2018;227:542–9.

    Article  Google Scholar 

  29. Iseger TA, Korgaonkar MS, Kenemans JL, Grieve SM, Baeken C, Fitzgerald PB, et al. EEG connectivity between the subgenual anterior cingulate and prefrontal cortices in response to antidepressant medication. Eur Neuropsychopharmacol. 2017;27(4):301–12.

    Article  CAS  Google Scholar 

  30. Kazemi R, Rostami R, Khomami S, Horacek J, Brunovsky M, Novak T, et al. Electrophysiological correlates of bilateral and unilateral repetitive transcranial magnetic stimulation in patients with bipolar depression. Psychiatry Res. 2016;240:364–75.

    Article  Google Scholar 

  31. Sun Y, Farzan F, Mulsant BH, Rajji TK, Fitzgerald PB, Barr MS, et al. Indicators for remission of suicidal ideation following magnetic seizure therapy in patients with treatment-resistant depression. JAMA Psychiat. 2016;73(4):337–45.

    Article  Google Scholar 

  32. Cao J, Liu Q, Li Y, Yang J, Gu R, Liang J, et al. Cognitive behavioural therapy attenuates the enhanced early facial stimuli processing in social anxiety disorders: an ERP investigation. Behav Brain Funct. 2017;13(1):12.

    Article  Google Scholar 

  33. Carrasco M, Hong C, Nienhuis JK, Harbin SM, Fitzgerald KD, Gehring WJ, et al. Increased error-related brain activity in youth with obsessive-compulsive disorder and other anxiety disorders. Neurosci Lett. 2013;541:214–8.

    Article  CAS  Google Scholar 

  34. Riesel A, Endrass T, Auerbach LA, Kathmann N. Overactive performance monitoring as an endophenotype for obsessive-compulsive disorder: evidence from a treatment study. Am J Psychiatry. 2015;172(7):665–73.

    Article  Google Scholar 

  35. Altamura AC, Delvecchio G, Paletta S, Di Pace C, Reggiori A, Fiorentini A, et al. Gray matter volumes may predict the clinical response to paliperidone palmitate long-acting in acute psychosis: a pilot longitudinal neuroimaging study. Psychiatry Res. 2017;261:80–4.

    Article  Google Scholar 

  36. McNabb CB, Tait RJ, McIlwain ME, Anderson VM, Suckling J, Kydd RR, et al. Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia. Schizophr Res. 2017; https://doi.org/10.1016/j.schres.2017.10.015.

    Article  Google Scholar 

  37. Vanes LD, Mouchlianitis E, Collier T, Averbeck BB, Shergill SS. Differential neural reward mechanisms in treatment-responsive and treatment-resistant schizophrenia. Psychol Med. 2018;1–10.

    Google Scholar 

  38. 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-naive major depressive disorder. Brain Imaging Behav. 2018; https://doi.org/10.1007/s11682-018-9845-9.

  39. 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. 2017; https://doi.org/10.1007/s11682-017-9773-0.

    Article  Google Scholar 

  40. Gong L, Hou Z, Wang Z, He C, Yin Y, Yuan Y, et al. Disrupted topology of hippocampal connectivity is associated with short-term antidepressant response in major depressive disorder. J Affect Disord. 2018;225:539–44.

    Article  Google Scholar 

  41. Lan MJ, Rubin-Falcone H, Motiwala F, Chen Y, Stewart JW, Hellerstein DJ, et al. White matter tract integrity is associated with antidepressant response to lurasidone in bipolar depression. Bipolar Disord. 2017;19(6):444–9.

    Article  CAS  Google Scholar 

  42. Liu J, Xu X, Luo Q, Luo Y, Chen Y, Lui S, et al. Brain grey matter volume alterations associated with antidepressant response in major depressive disorder. Sci Rep. 2017;7(1):10464. https://doi.org/10.1038/s41598-017-10676-5.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Webb CA, Olson EA, Killgore WDS, Pizzagalli DA, Rauch SL, Rosso IM. Rostral anterior cingulate cortex morphology predicts treatment response to internet-based cognitive behavioral therapy for depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):255–62.

    Article  Google Scholar 

  44. Sambataro F, Doerig N, Hanggi J, Wolf RC, Brakowski J, Holtforth MG, et al. Anterior cingulate volume predicts response to psychotherapy and functional connectivity with the inferior parietal cortex in major depressive disorder. Eur Neuropsychopharmacol. 2018;28(1):138–48.

    Article  CAS  Google Scholar 

  45. Boes AD, Uitermarkt BD, Albazron FM, Lan MJ, Liston C, Pascual-Leone A, et al. Rostral anterior cingulate cortex is a structural correlate of repetitive TMS treatment response in depression. Brain Stimul. 2018; https://doi.org/10.1016/j.brs.2018.01.029.

    Article  Google Scholar 

  46. Mansson KN, Frick A, Boraxbekk CJ, Marquand AF, Williams SC, Carlbring P, et al. Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning. Transl Psychiatry. 2015;5:e530.

    Article  CAS  Google Scholar 

  47. Klumpp H, Fitzgerald JM, Kinney KL, Kennedy AE, Shankman SA, Langenecker SA, et al. Predicting cognitive behavioral therapy response in social anxiety disorder with anterior cingulate cortex and amygdala during emotion regulation. NeuroImage Clin. 2017;15:25–34.

    Article  Google Scholar 

  48. Klumpp H, Roberts J, Kennedy AE, Shankman SA, Langenecker SA, Gross JJ, et al. Emotion regulation related neural predictors of cognitive behavioral therapy response in social anxiety disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;75:106–12.

    Article  Google Scholar 

  49. White LK, Sequeira S, Britton JC, Brotman MA, Gold AL, Berman E, et al. Complementary features of attention bias modification therapy and cognitive-behavioral therapy in pediatric anxiety disorders. Am J Psychiatry. 2017;174(8):775–84.

    Article  Google Scholar 

  50. Gottlich M, Kramer UM, Kordon A, Hohagen F, Zurowski B. Resting-state connectivity of the amygdala predicts response to cognitive behavioral therapy in obsessive compulsive disorder. Biol Psychol. 2015;111:100–9.

    Article  Google Scholar 

  51. Light GA, Swerdlow NR. Future clinical uses of neurophysiological biomarkers to predict and monitor treatment response for schizophrenia. Ann N Y Acad Sci. 2015;1344(1):105–19.

    Article  CAS  Google Scholar 

  52. Wade EC, Iosifescu DV. Using electroencephalography for treatment guidance in major depressive disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(5):411–22.

    Article  Google Scholar 

  53. Arns M, Gordon E, Boutros NN. EEG abnormalities are associated with poorer depressive symptom outcomes with escitalopram and venlafaxine-XR, but not sertraline: results from the multicenter randomized iSPOT-D study. Clin EEG Neurosci. 2017;48(1):33–40.

    Article  Google Scholar 

  54. van Dinteren R, Arns M, Kenemans L, Jongsma ML, Kessels RP, Fitzgerald P, et al. Utility of event-related potentials in predicting antidepressant treatment response: an iSPOT-D report. Eur Neuropsychopharmacol. 2015;25(11):1981–90.

    Article  CAS  Google Scholar 

  55. Tenke CE, Kayser J, Pechtel P, Webb CA, Dillon DG, Goer F, et al. Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response. Psychophysiology. 2017;54(1):34–50.

    Article  Google Scholar 

  56. Lee BH, Park YM, Lee SH, Shim M. Prediction of long-term treatment response to selective serotonin reuptake inhibitors (SSRIs) using scalp and source loudness dependence of auditory evoked potentials (LDAEP) analysis in patients with major depressive disorder. Int J Mol Sci. 2015;16(3):6251–65.

    Article  CAS  Google Scholar 

  57. Mumtaz W, Xia L, Mohd Yasin MA, Azhar Ali SS, Malik AS. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLoS One. 2017;12(2):e0171409.

    Article  Google Scholar 

  58. Jaworska N, Wang H, Smith DM, Blier P, Knott V, Protzner AB. Pre-treatment EEG signal variability is associated with treatment success in depression. NeuroImage Clin. 2018;17:368–77.

    Article  Google Scholar 

  59. Bailey NW, Hoy KE, Rogasch NC, Thomson RH, McQueen S, Elliot D, et al. Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders. Brain Stimul. 2018;11(1):190–203.

    Article  CAS  Google Scholar 

  60. Stange JP, MacNamara A, Kennedy AE, Hajcak G, Phan KL, Klumpp H. Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: a person-centered event-related potential study. Neuropsychologia. 2017; https://doi.org/10.1016/j.neuropsychologia.2017.06.027.

  61. Dohrmann AL, Stengler K, Jahn I, Olbrich S. EEG-arousal regulation as predictor of treatment response in patients suffering from obsessive compulsive disorder. Clin Neurophysiol. 2017;128(10):1906–14.

    Article  Google Scholar 

  62. Schnack HG. Improving individual predictions: machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases). Schizophr Res. 2017; https://doi.org/10.1016/j.schres.2017.10.023.

  63. Kim YK, Na KS. Application of machine learning classification for structural brain MRI in mood disorders: critical review from a clinical perspective. Prog Neuro-Psychopharmacol Biol Psychiatry. 2018;80(Pt B):71–80.

    Article  Google Scholar 

  64. Johnston BA, Steele JD, Tolomeo S, Christmas D, Matthews K. Structural MRI-based predictions in patients with treatment-refractory depression (TRD). PLoS One. 2015;10(7):e0132958.

    Article  Google Scholar 

  65. Koutsouleris N, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, et al. Predicting response to repetitive transcranial magnetic stimulation in patients with Schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis. Schizophr Bull. 2017; https://doi.org/10.1093/schbul/sbx114.

    Article  Google Scholar 

  66. Li P, Jing RX, Zhao RJ, Ding ZB, Shi L, Sun HQ, et al. Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study. NPJ Schizophr. 2017;3(1):21.

    Article  Google Scholar 

  67. Bak N, Ebdrup BH, Oranje B, Fagerlund B, Jensen MH, Düring SW, et al. Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology. Transl Psychiatry. 2017;7(4):e1087.

    Article  CAS  Google Scholar 

  68. Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in LLD classification and response prediction. Int J Geriatr Psychiatry. 2015;30(10):1056–67.

    Article  Google Scholar 

  69. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Burger C, et al. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiat. 2016;73(6):557–64.

    Article  Google Scholar 

  70. Yun JY, Jang JH, Kim SN, Jung WH, Kwon JS. Neural correlates of response to pharmacotherapy in obsessive-compulsive disorder: individualized cortical morphology-based structural covariance. Prog Neuro-Psychopharmacol Biol Psychiatry. 2015;63:126–33.

    Article  CAS  Google Scholar 

  71. Reggente N, Moody TD, Morfini F, Sheen C, Rissman J, O’Neill J, et al. Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive-compulsive disorder. Proc Natl Acad Sci U S A. 2018;115(9):2222–7.

    Article  Google Scholar 

  72. Hahn T, Kircher T, Straube B, Wittchen HU, Konrad C, Strohle A, et al. Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiat. 2015;72(1):68–74.

    Article  Google Scholar 

  73. Berk M, Post R, Ratheesh A, Gliddon E, Singh A, Vieta E, et al. Staging in bipolar disorder: from theoretical framework to clinical utility. World Psychiatry. 2017;16(3):236–44.

    Article  Google Scholar 

  74. Dragioti E, Wiklund T, Siamouli M, Moutou K, Fountoulakis KN. Could PANSS be a useful tool in the determining of the stages of schizophrenia? A clinically operational approach. J Psychiatr Res. 2017;86:66–72.

    Article  Google Scholar 

  75. Duffy A, Malhi GS, Grof P. Do the trajectories of bipolar disorder and schizophrenia follow a universal staging model? Can J Psychiatry. 2017;62(2):115–22.

    Article  Google Scholar 

  76. Kupfer DJ, Frank E, Ritchey FC. Staging bipolar disorder: what data and what models are needed? Lancet Psychiatry. 2015;2(6):564–70.

    Article  Google Scholar 

  77. Li T, Wang Q, Zhang J, Rolls ET, Yang W, Palaniyappan L, et al. Brain-wide analysis of functional connectivity in first-episode and chronic stages of schizophrenia. Schizophr Bull. 2017;43(2):436–48.

    PubMed  Google Scholar 

  78. Anderzhanova E, Kirmeier T, Wotjak CT. Animal models in psychiatric research: the RDoC system as a new framework for endophenotype-oriented translational neuroscience. Neurobiol Stress. 2017;7:47–56.

    Article  Google Scholar 

  79. Carcone D, Ruocco AC. Six years of research on the National Institute of Mental Health’s Research Domain Criteria (RDoC) initiative: a systematic review. Front Cell Neurosci. 2017;11:46.

    Article  Google Scholar 

  80. Cohen AS, Le TP, Fedechko TL, Elvevag B. Can RDoC help find order in thought disorder? Schizophr Bull. 2017;43(3):503–8.

    Article  Google Scholar 

  81. Luyten P, Fonagy P. The stress-reward-mentalizing model of depression: an integrative developmental cascade approach to child and adolescent depressive disorder based on the research domain criteria (RDoC) approach. Clin Psychol Rev. 2017; https://doi.org/10.1016/j.cpr.2017.09.008.

    Article  Google Scholar 

  82. Nusslock R, Alloy LB. Reward processing and mood-related symptoms: an RDoC and translational neuroscience perspective. J Affect Disord. 2017;216:3–16.

    Article  Google Scholar 

  83. Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–30.

    Article  Google Scholar 

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Acknowledgment

This work was supported by a grant from the Korea Science and Engineering Foundation (KOSEF), funded by the Korean government (NRF-2018R1A2A2A05018505), and by the Ministry of Science, ICT and Future Planning (NRF-2015M3C7A1028252).

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Authors and Affiliations

  1. Seoul National University Hospital, Seoul, Republic of Korea

    Je-Yeon Yun

  2. Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea

    Je-Yeon Yun

  3. Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea

    Seung-Hwan Lee

  4. Department of Psychiatry, Inje University, Ilsan-Paik Hospital, Goyang, Republic of Korea

    Seung-Hwan Lee

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Correspondence to Seung-Hwan Lee .

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  1. Department of Psychiatry, College of Medicine, Korea University, Seoul, Korea (Republic of)

    Yong-Ku Kim

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Yun, JY., Lee, SH. (2019). Using Neuroimaging and Electroencephalography for Prediction of Treatment Resistance in Psychiatric Disorders. In: Kim, YK. (eds) Treatment Resistance in Psychiatry. Springer, Singapore. https://doi.org/10.1007/978-981-10-4358-1_4

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