Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial | Depressive Disorders | JAMA Psychiatry | JAMA Network
[Skip to Navigation]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address Please contact the publisher to request reinstatement.
Cipriani  A, Furukawa  TA, Salanti  G,  et al.  Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis.  Lancet. 2018;391(10128):1357-1366. doi:10.1016/S0140-6736(17)32802-7PubMedGoogle ScholarCrossref
Khan  A, Faucett  J, Lichtenberg  P, Kirsch  I, Brown  WA.  A systematic review of comparative efficacy of treatments and controls for depression.  PLoS One. 2012;7(7):e41778. doi:10.1371/journal.pone.0041778PubMedGoogle Scholar
Fournier  JC, DeRubeis  RJ, Hollon  SD,  et al.  Antidepressant drug effects and depression severity: a patient-level meta-analysis.  JAMA. 2010;303(1):47-53. doi:10.1001/jama.2009.1943PubMedGoogle ScholarCrossref
Kirsch  I, Deacon  BJ, Huedo-Medina  TB, Scoboria  A, Moore  TJ, Johnson  BT.  Initial severity and antidepressant benefits: a meta-analysis of data submitted to the Food and Drug Administration.  PLoS Med. 2008;5(2):e45. doi:10.1371/journal.pmed.0050045PubMedGoogle Scholar
Trivedi  MH, Rush  AJ, Wisniewski  SR,  et al; STAR*D Study Team.  Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice.  Am J Psychiatry. 2006;163(1):28-40. doi:10.1176/appi.ajp.163.1.28PubMedGoogle ScholarCrossref
Dichter  GS, Gibbs  D, Smoski  MJ.  A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder.  J Affect Disord. 2015;172:8-17. doi:10.1016/j.jad.2014.09.028PubMedGoogle ScholarCrossref
Harmer  CJ.  Neural predictors of treatment response in depression.  Curr Behav Neurosci Rep. 2014;1(3):125-133. doi:10.1007/s40473-014-0021-2Google ScholarCrossref
Bagby  RM, Ryder  AG, Cristi  C.  Psychosocial and clinical predictors of response to pharmacotherapy for depression.  J Psychiatry Neurosci. 2002;27(4):250-257.PubMedGoogle Scholar
Nierenberg  AA.  Predictors of response to antidepressants general principles and clinical implications.  Psychiatr Clin North Am. 2003;26(2):345-352. doi:10.1016/S0193-953X(02)00105-3PubMedGoogle ScholarCrossref
Kemp  AH, Gordon  E, Rush  AJ, Williams  LM.  Improving the prediction of treatment response in depression: integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures.  CNS Spectr. 2008;13(12):1066-1086. doi:10.1017/S1092852900017120PubMedGoogle ScholarCrossref
Pizzagalli  DA.  Frontocingulate dysfunction in depression: toward biomarkers of treatment response.  Neuropsychopharmacology. 2011;36(1):183-206. doi:10.1038/npp.2010.166PubMedGoogle ScholarCrossref
Carmichael  O, Schwarz  AJ, Chatham  CH,  et al.  The role of fMRI in drug development.  Drug Discov Today. 2018;23(2):333-348. doi:10.1016/j.drudis.2017.11.012PubMedGoogle ScholarCrossref
Olbrich  S, Arns  M.  EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response.  Int Rev Psychiatry. 2013;25(5):604-618. doi:10.3109/09540261.2013.816269PubMedGoogle ScholarCrossref
Arns  M, Bruder  G, Hegerl  U,  et al.  EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study.  Clin Neurophysiol. 2016;127(1):509-519. doi:10.1016/j.clinph.2015.05.032PubMedGoogle ScholarCrossref
Frodl  T.  Recent advances in predicting responses to antidepressant treatment.  F1000Res. 2017;6:6. doi:10.12688/f1000research.10300.1PubMedGoogle ScholarCrossref
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. 2017;17:368-377. doi:10.1016/j.nicl.2017.10.035PubMedGoogle ScholarCrossref
Pizzagalli  D, Pascual-Marqui  RD, Nitschke  JB,  et al.  Anterior cingulate activity as a predictor of degree of treatment response in major depression: evidence from brain electrical tomography analysis.  Am J Psychiatry. 2001;158(3):405-415. doi:10.1176/appi.ajp.158.3.405PubMedGoogle ScholarCrossref
Pizzagalli  DA, Webb  CA, Dillon  DG,  et al.  Pretreatment rostral anterior cingulate cortex theta activity in relation to symptom improvement in depression: a randomized clinical trial.  JAMA Psychiatry. 2018;75(6):547-554. doi:10.1001/jamapsychiatry.2018.0252PubMedGoogle ScholarCrossref
Widge  AS, Bilge  MT, Montana  R,  et al.  Electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis.  Am J Psychiatry. 2019;176(1):44-56. doi:10.1176/appi.ajp.2018.17121358PubMedGoogle ScholarCrossref
Wade  EC, Iosifescu  DV.  Using electroencephalography for treatment guidance in major depressive disorder.  Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(5):411-422. doi:10.1016/j.bpsc.2016.06.002PubMedGoogle ScholarCrossref
Olbrich  S, van Dinteren  R, Arns  M.  Personalized medicine: review and perspectives of promising baseline EEG biomarkers in major depressive disorder and attention deficit hyperactivity disorder.  Neuropsychobiology. 2015;72(3-4):229-240. doi:10.1159/000437435PubMedGoogle ScholarCrossref
Schiller  MJ.  Quantitative electroencephalography in guiding treatment of major depression.  Front Psychiatry. 2019;9:779. doi:10.3389/fpsyt.2018.00779PubMedGoogle ScholarCrossref
Mumtaz  W, Malik  AS, Yasin  MAM, Xia  L.  Review on EEG and ERP predictive biomarkers for major depressive disorder.  Biomed Signal Process Control. 2015;22:85-98. doi:10.1016/j.bspc.2015.07.003Google ScholarCrossref
Korb  AS, Hunter  AM, Cook  IA, Leuchter  AF.  Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression.  Clin Neurophysiol. 2009;120(7):1313-1319. doi:10.1016/j.clinph.2009.05.008PubMedGoogle ScholarCrossref
Hipp  JF, Hawellek  DJ, Corbetta  M, Siegel  M, Engel  AK.  Large-scale cortical correlation structure of spontaneous oscillatory activity.  Nat Neurosci. 2012;15(6):884-890. doi:10.1038/nn.3101PubMedGoogle ScholarCrossref
Toll  R, Wu  W, Naparstek  S,  et al.  An electroencephalography connectomic profile of post-traumatic stress disorder.  Am J Psychiatry. In press.Google Scholar
Trivedi  MH, McGrath  PJ, Fava  M,  et al.  Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC): rationale and design.  J Psychiatr Res. 2016;78:11-23. doi:10.1016/j.jpsychires.2016.03.001PubMedGoogle ScholarCrossref
First  M, Spitzer  R, Gibbon  M, William  J.  Structured Clinical Interview for DSM-IV-TR Axis 1 disorders, Research Version, Patient Edition (SCID-I/P). New York: New York State Psychiatric Institute, Biometric Research; 2002.
Hämäläinen  MS, Ilmoniemi  RJ.  Interpreting magnetic fields of the brain: minimum norm estimates.  Med Biol Eng Comput. 1994;32(1):35-42. doi:10.1007/BF02512476PubMedGoogle ScholarCrossref
Rubinov  M, Sporns  O.  Complex network measures of brain connectivity: uses and interpretations.  Neuroimage. 2010;52(3):1059-1069. doi:10.1016/j.neuroimage.2009.10.003PubMedGoogle ScholarCrossref
Fallani  FdeV, Costa  LdaF, Rodriguez  FA,  et al.  A graph-theoretical approach in brain functional networks: possible implications in EEG studies.  Nonlinear Biomed Phys. 2010;4(suppl 1):S8. doi:10.1186/1753-4631-4-S1-S8PubMedGoogle ScholarCrossref
Lindstrom  ML, Bates  DM.  Nonlinear mixed effects models for repeated measures data.  Biometrics. 1990;46(3):673-687. doi:10.2307/2532087PubMedGoogle ScholarCrossref
Pinheiro  J, Bates  D. DebRoy  S, Sarkar  D; R Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-142 [computer program]. Accessed November 22, 2019.
Bernstein  DP, Fink  L.  Childhood Trauma Questionnaire: A Retrospective Self-Report: Manual. San Diego, CA: Psychological Corporation; 1998.
Rush  AJ, Trivedi  MH, Ibrahim  HM,  et al.  The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression.  Biol Psychiatry. 2003;54(5):573-583. doi:10.1016/S0006-3223(02)01866-8PubMedGoogle ScholarCrossref
Watson  D, Clark  LA.  The Mood and Anxiety Symptom Questionnaire. Iowa City: University of Iowa; 1991.
Spielberger  CD, Gorsuch  RL, Lushene  RE.  Manual for the State-Trait Anxiety Inventory (Self-Evaluation Questionnaire). Palo Alto, CA: Consulting Psychologists Press; 1970.
Snaith  RP, Hamilton  M, Morley  S, Humayan  A, Hargreaves  D, Trigwell  P.  A scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale.  Br J Psychiatry. 1995;167(1):99-103. doi:10.1192/bjp.167.1.99PubMedGoogle ScholarCrossref
Nakonezny  PA, Morris  DW, Greer  TL,  et al.  Evaluation of anhedonia with the Snaith-Hamilton Pleasure Scale (SHAPS) in adult outpatients with major depressive disorder.  J Psychiatr Res. 2015;65:124-130. doi:10.1016/j.jpsychires.2015.03.010PubMedGoogle ScholarCrossref
de Pasquale  F, Della Penna  S, Snyder  AZ,  et al.  Temporal dynamics of spontaneous MEG activity in brain networks.  Proc Natl Acad Sci U S A. 2010;107(13):6040-6045. doi:10.1073/pnas.0913863107PubMedGoogle ScholarCrossref
Liu  Z, Fukunaga  M, de Zwart  JA, Duyn  JH.  Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography.  Neuroimage. 2010;51(1):102-111. doi:10.1016/j.neuroimage.2010.01.092PubMedGoogle ScholarCrossref
Nikouline  VV, Linkenkaer-Hansen  K, Huttunen  J, Ilmoniemi  RJ.  Interhemispheric phase synchrony and amplitude correlation of spontaneous beta oscillations in human subjects: a magnetoencephalographic study.  Neuroreport. 2001;12(11):2487-2491. doi:10.1097/00001756-200108080-00040PubMedGoogle ScholarCrossref
Bächinger  M, Zerbi  V, Moisa  M,  et al.  Concurrent tACS-fMRI reveals causal influence of power synchronized neural activity on resting state fMRI connectivity.  J Neurosci. 2017;37(18):4766-4777. doi:10.1523/JNEUROSCI.1756-16.2017PubMedGoogle ScholarCrossref
Hall  EL, Robson  SE, Morris  PG, Brookes  MJ.  The relationship between MEG and fMRI.  Neuroimage. 2014;102(pt 1):80-91. doi:10.1016/j.neuroimage.2013.11.005PubMedGoogle ScholarCrossref
Mukamel  R, Gelbard  H, Arieli  A, Hasson  U, Fried  I, Malach  R.  Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex.  Science. 2005;309(5736):951-954. doi:10.1126/science.1110913PubMedGoogle ScholarCrossref
Zumer  JM, Brookes  MJ, Stevenson  CM, Francis  ST, Morris  PG.  Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting.  Neuroimage. 2010;49(2):1479-1489. doi:10.1016/j.neuroimage.2009.09.020PubMedGoogle ScholarCrossref
Haegens  S, Nácher  V, Luna  R, Romo  R, Jensen  O.  α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking.  Proc Natl Acad Sci U S A. 2011;108(48):19377-19382. doi:10.1073/pnas.1117190108PubMedGoogle ScholarCrossref
Hayden  BY, Smith  DV, Platt  ML.  Electrophysiological correlates of default-mode processing in macaque posterior cingulate cortex.  Proc Natl Acad Sci U S A. 2009;106(14):5948-5953. doi:10.1073/pnas.0812035106PubMedGoogle ScholarCrossref
Ossandón  T, Jerbi  K, Vidal  JR,  et al.  Transient suppression of broadband gamma power in the default-mode network is correlated with task complexity and subject performance.  J Neurosci. 2011;31(41):14521-14530. doi:10.1523/JNEUROSCI.2483-11.2011PubMedGoogle ScholarCrossref
Gorka  SM, Phan  KL, Shankman  SA.  Convergence of EEG and fMRI measures of reward anticipation.  Biol Psychol. 2015;112:12-19. doi:10.1016/j.biopsycho.2015.09.007PubMedGoogle ScholarCrossref
Liu  F, Guo  W, Yu  D,  et al.  Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans.  PLoS One. 2012;7(7):e40968. doi:10.1371/journal.pone.0040968PubMedGoogle Scholar
Wisniewski  D, Reverberi  C, Momennejad  I, Kahnt  T, Haynes  JD.  The role of the parietal cortex in the representation of task-reward associations.  J Neurosci. 2015;35(36):12355-12365. doi:10.1523/JNEUROSCI.4882-14.2015PubMedGoogle ScholarCrossref
Nelson  BD, Perlman  G, Klein  DN, Kotov  R, Hajcak  G.  Blunted neural response to rewards as a prospective predictor of the development of depression in adolescent girls.  Am J Psychiatry. 2016;173(12):1223-1230. doi:10.1176/appi.ajp.2016.15121524PubMedGoogle ScholarCrossref
Shankman  SA, Klein  DN, Tenke  CE, Bruder  GE.  Reward sensitivity in depression: a biobehavioral study.  J Abnorm Psychol. 2007;116(1):95-104. doi:10.1037/0021-843X.116.1.95PubMedGoogle ScholarCrossref
Shankman  SA, Nelson  BD, Sarapas  C,  et al.  A psychophysiological investigation of threat and reward sensitivity in individuals with panic disorder and/or major depressive disorder.  J Abnorm Psychol. 2013;122(2):322-338. doi:10.1037/a0030747PubMedGoogle ScholarCrossref
Fitzgerald  PJ, Watson  BO.  Gamma oscillations as a biomarker for major depression: an emerging topic.  Transl Psychiatry. 2018;8(1):177. doi:10.1038/s41398-018-0239-yPubMedGoogle ScholarCrossref
Fitzgerald  PB, Laird  AR, Maller  J, Daskalakis  ZJ.  A meta-analytic study of changes in brain activation in depression.  Hum Brain Mapp. 2008;29(6):683-695. doi:10.1002/hbm.20426PubMedGoogle ScholarCrossref
Akar  SA, Kara  S, Agambayev  S, Bilgic  V.  Nonlinear analysis of EEG in major depression with fractal dimensions.  Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7410-7413. doi:10.1109/EMBC.2015.7320104PubMedGoogle Scholar
Noda  Y, Zomorrodi  R, Saeki  T,  et al.  Resting-state EEG gamma power and theta-gamma coupling enhancement following high-frequency left dorsolateral prefrontal rTMS in patients with depression.  Clin Neurophysiol. 2017;128(3):424-432. doi:10.1016/j.clinph.2016.12.023PubMedGoogle ScholarCrossref
Pathak  Y, Salami  O, Baillet  S, Li  Z, Butson  CR.  Longitudinal changes in depressive circuitry in response to neuromodulation therapy.  Front Neural Circuits. 2016;10:50. doi:10.3389/fncir.2016.00050PubMedGoogle ScholarCrossref
Enck  P, Klosterhalfen  S, Weimer  K, Horing  B, Zipfel  S.  The placebo response in clinical trials: more questions than answers.  Philos Trans R Soc Lond B Biol Sci. 2011;366(1572):1889-1895. doi:10.1098/rstb.2010.0384PubMedGoogle ScholarCrossref
Stolk  P, Ten Berg  MJ, Hemels  ME, Einarson  TR.  Meta-analysis of placebo rates in major depressive disorder trials.  Ann Pharmacother. 2003;37(12):1891-1899. doi:10.1345/aph.1D172PubMedGoogle ScholarCrossref
Walsh  BT, Seidman  SN, Sysko  R, Gould  M.  Placebo response in studies of major depression: variable, substantial, and growing.  JAMA. 2002;287(14):1840-1847. doi:10.1001/jama.287.14.1840PubMedGoogle ScholarCrossref
Al-Harbi  KS.  Treatment-resistant depression: therapeutic trends, challenges, and future directions.  Patient Prefer Adherence. 2012;6:369-388. doi:10.2147/PPA.S29716PubMedGoogle ScholarCrossref
Cipriani  A, Geddes  JR.  Placebo for depression: we need to improve the quality of scientific information but also reject too simplistic approaches or ideological nihilism.  BMC Med. 2014;12:105. doi:10.1186/1741-7015-12-105PubMedGoogle ScholarCrossref
Khan  A, Redding  N, Brown  WA.  The persistence of the placebo response in antidepressant clinical trials.  J Psychiatr Res. 2008;42(10):791-796. doi:10.1016/j.jpsychires.2007.10.004PubMedGoogle ScholarCrossref
Peciña  M, Bohnert  AS, Sikora  M,  et al.  Association between placebo-activated neural systems and antidepressant responses: neurochemistry of placebo effects in major depression.  JAMA Psychiatry. 2015;72(11):1087-1094. doi:10.1001/jamapsychiatry.2015.1335PubMedGoogle ScholarCrossref
Rutherford  BR, Roose  SP.  A model of placebo response in antidepressant clinical trials.  Am J Psychiatry. 2013;170(7):723-733. doi:10.1176/appi.ajp.2012.12040474PubMedGoogle ScholarCrossref
Salanti  G, Chaimani  A, Furukawa  TA,  et al.  Impact of placebo arms on outcomes in antidepressant trials: systematic review and meta-regression analysis.  Int J Epidemiol. 2018;47(5):1454-1464. doi:10.1093/ije/dyy076PubMedGoogle ScholarCrossref
Leuchter  AF, Cook  IA, Witte  EA, Morgan  M, Abrams  M.  Changes in brain function of depressed subjects during treatment with placebo.  Am J Psychiatry. 2002;159(1):122-129. doi:10.1176/appi.ajp.159.1.122PubMedGoogle ScholarCrossref
Mayberg  HS, Silva  JA, Brannan  SK,  et al.  The functional neuroanatomy of the placebo effect.  Am J Psychiatry. 2002;159(5):728-737. doi:10.1176/appi.ajp.159.5.728PubMedGoogle ScholarCrossref
Sikora  M, Heffernan  J, Avery  ET, Mickey  BJ, Zubieta  JK, Peciña  M.  Salience network functional connectivity predicts placebo effects in major depression.  Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(1):68-76. doi:10.1016/j.bpsc.2015.10.002PubMedGoogle ScholarCrossref
Khan  A, Brodhead  AE, Kolts  RL, Brown  WA.  Severity of depressive symptoms and response to antidepressants and placebo in antidepressant trials.  J Psychiatr Res. 2005;39(2):145-150. doi:10.1016/j.jpsychires.2004.06.005PubMedGoogle ScholarCrossref
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.
Der-Avakian  A, Markou  A.  The neurobiology of anhedonia and other reward-related deficits.  Trends Neurosci. 2012;35(1):68-77. doi:10.1016/j.tins.2011.11.005PubMedGoogle ScholarCrossref
Heshmati  M, Russo  SJ.  Anhedonia and the brain reward circuitry in depression.  Curr Behav Neurosci Rep. 2015;2(3):146-153. doi:10.1007/s40473-015-0044-3PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    January 2, 2020

    Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial

    Author Affiliations
    • 1Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
    • 2Wu Tsai Neuroscience Institute, Stanford University, Stanford, California
    • 3Veterans Affairs Palo Alto Healthcare System, Palo Alto, California
    • 4Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California
    • 5Department of Psychiatry, Dell Medical School, The University of Texas at Austin
    • 6School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
    • 7Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
    • 8Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
    • 9New York State Psychiatric Institute, Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York
    • 10now at Alto Neuroscience Inc, Los Altos, California
    JAMA Psychiatry. 2020;77(4):397-408. doi:10.1001/jamapsychiatry.2019.3867
    Key Points

    Question  What electroencephalographic connectivity features are neural moderators of antidepressant treatment response?

    Findings  In this secondary analysis of a randomized clinical trial, greater alpha-band and lower gamma-band connectivity—most prominently parietal—predicted better placebo treatment response and worse antidepressant treatment response. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia.

    Meaning  Key cortical features differentiating placebo response from antidepressant response were identified, providing an alternative direction toward establishing a placebo signature in clinical trials.


    Importance  Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging–like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.

    Objective  To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.

    Design, Setting, and Participants  In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.

    Interventions  Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.

    Main Outcomes and Measures  Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.

    Results  Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions—most prominently parietal—for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.

    Conclusions and Relevance  These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.

    Trial Registration identifier: NCT01407094