Cited by 189Treatment of Major DepressionTranscranial Magnetic Stimulation StudiesAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

The DOI matches its Crossref record; 116 of the 117 references it cites were found in a registry or an index.

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

Open access

Pizzagalli, Diego A., Webb, Christian A., Dillon, Daniel G., Tenke, Craig E., Kayser, Jürgen, Goer, Franziska, Fava, Maurizio, McGrath, Patrick, Weissman, Myrna, Parsey, Ramin, Adams, Phil, Trombello, Joseph, Cooper, Crystal, Deldin, Patricia, Oquendo, Maria A., McInnis, Melvin G., Carmody, Thomas, Bruder, Gerard, Trivedi, Madhukar H.·2018·JAMA Psychiatry

DOI 10.1001/jamapsychiatry.2018.0252PMC PMC6083825

Open this paper in CoChat — free account Open PDF

Abstract

Importance: Major depressive disorder (MDD) remains challenging to treat. Although several clinical and demographic variables have been found to predict poor antidepressant response, these markers have not been robustly replicated to warrant implementation in clinical care. Increased pretreatment rostral anterior cingulate cortex (rACC) theta activity has been linked to better antidepressant outcomes. However, no prior study has evaluated whether this marker has incremental predictive validity over clinical and demographic measures. Objective: To determine whether increased pretreatment rACC theta activity would predict symptom improvement regardless of randomization arm. Design, Setting, and Participants: A multicenter randomized clinical trial enrolled outpatients without psychosis and with chronic or recurrent MDD between July 29, 2011, and December 15, 2015 (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care [EMBARC]). Patients were consecutively recruited from 4 university hospitals: 634 patients were screened, 296 were randomized to receive sertraline hydrochloride or placebo, 266 had electroencephalographic (EEG) recordings, and 248 had usable EEG data. Resting EEG data were recorded at baseline and 1 week after trial onset, and rACC theta activity was extracted using source localization. Intent-to-treat analysis was conducted. Data analysis was performed from October 7, 2016, to January 19, 2018. Interventions: An 8-week course of sertraline or placebo. Main Outcomes and Measures: The 17-item Hamilton Rating Scale for Depression score (assessed at baseline and weeks 1, 2, 3, 4, 6, and 8). Results: The 248 participants (160 [64.5%] women, 88 [35.5%] men) with usable EEG data had a mean (SD) age of 36.75 (13.15) years. Higher rACC theta activity at both baseline (b = -1.05; 95% CI, -1.77 to -0.34; P = .004) and week 1 (b = -0.83; 95% CI, -1.60 to -0.06; P < .04) predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome. These effects were not moderated by treatment arm. The rACC theta marker, in combination with clinical and demographic variables, accounted for an estimated 39.6% of the variance in symptom change (with 8.5% of the variance uniquely attributable to the rACC theta marker). Conclusions and Relevance: Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome. This is the first study to date to demonstrate that rACC theta activity has incremental predictive validity. Trial Registration: clinicaltrials.gov Identifier: NCT01407094.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1001/jamapsychiatry.2018.0252

Cites

116 of 116 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

A rating scale for depression
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817
A new depression scale designed to be sensitive to change
Montgomery, S. A., & Åsberg, M. (1979) · British Journal of Psychiatry · doi:10.1192/bjp.134.4.382 · Cited by 13,962
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Development of a Rating Scale for Primary Depressive Illness
Hamilton, M. (1967) · British Journal of Social and Clinical Psychology · doi:10.1111/j.2044-8260.1967.tb00530.x · Cited by 8,410
Development and validation of a brief screening version of the Childhood Trauma Questionnaire
Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., Stokes, J., Handelsman, L., Medrano, M., Desmond, D., et al. (2003) · Child Abuse & Neglect · doi:10.1016/s0145-2134(02)00541-0 · Cited by 6,419
Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., et al. (2006) · American Journal of Psychiatry · doi:10.1176/ajp.2006.163.11.1905 · Cited by 5,643
Self-citation
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552
Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice
Trivedi, M. H., Rush, A. J., Wisniewski, S. R., Nierenberg, A. A., Warden, D., Ritz, L., Norquist, G., Howland, R. H., Lebowitz, B., McGrath, P. J., et al. (2006) · American Journal of Psychiatry · doi:10.1176/appi.ajp.163.1.28 · Cited by 3,879
Self-citation
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
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., Markowitz, J. C., Ninan, P. T., Kornstein, S., Manber, R., et al. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(02)01866-8 · Cited by 3,829
Self-citation

Flagged first, then by citations

Show all 117 references

1 of the 117 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

First, 2002, Structured Clinical Interview for DSM-IV-TR Axis I Disorders-Non-Patient Edition (SCID-I/NP, 11/2002 revision)

Sorted by citations

Mental health before and during the COVID-19 pandemic in two longitudinal UK population cohorts
Kwong, A. S. F., Pearson, R. M., Adams, M. J., Northstone, K., Tilling, K. M., Major‐Smith, D., Fawns‐Ritchie, C., Bould, H., Warne, N., Zammit, S., et al. (2020) · The British Journal of Psychiatry · doi:10.1192/bjp.2020.242 · Cited by 543
Prognosis and improved outcomes in major depression: a review
Kraus, C., Kadriu, B., Lanzenberger, R., Zarate, C. A., & Kasper, S. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0460-3 · Cited by 482
Candidate biomarkers in psychiatric disorders: state of the field
Abi‐Dargham, A., Moeller, S. J., Ali, F., DeLorenzo, C., Domschke, K., Horga, G., Jutla, A., Kotov, R., Paulus, M. P., Rubio, J. M., et al. (2023) · World Psychiatry · doi:10.1002/wps.21078 · Cited by 421
An electroencephalographic signature predicts antidepressant response in major depression
Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0397-3 · Cited by 302
Self-citation
Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics
Buch, A. M., & Liston, C. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00789-3 · Cited by 289
Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis
Widge, A. S., Bilge, M. T., Montana, R. E., Chang, W. C., Rodríguez, C. I., Deckersbach, T., Carpenter, L. L., Kalin, N. H., & Nemeroff, C. B. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17121358 · Cited by 200
Machine learning and big data in psychiatry: toward clinical applications
Rutledge, R. B., Chekroud, A. M., & Huys, Q. J. M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.006 · Cited by 206
Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study
Webb, C. A., Trivedi, M. H., Cohen, Z. D., Dillon, D. G., Fournier, J. C. J., Goer, F., D, M. F. M., McGrath, P. J., Weissman, M. M., Parsey, R. V., et al. (2018) · Psychological Medicine · doi:10.1017/s0033291718001708 · Cited by 177
Self-citation
Toward Circuit Mechanisms of Pathophysiology in Depression
Spellman, T., & Liston, C. M. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20030280 · Cited by 180
Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function
Widge, A. S., Zorowitz, S., Basu, I., Paulk, A. C., Cash, S. S., Eskandar, E. N., Deckersbach, T., Miller, E. K., & Dougherty, D. D. (2019) · Nature Communications · doi:10.1038/s41467-019-09557-4 · Cited by 152
Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data
Jaworska, N., de la Salle, S., Ibrahim, M. H., Blier, P., & Knott, V. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00768 · Cited by 106
Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.
Webb, C. A., Cohen, Z. D., Beard, C., Forgeard, M. J. C., Peckham, A., & Björgvinsson, Þ. (2019) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000451 · Cited by 96
Self-citation
Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder
Rolle, C. E., Fonzo, G. A., Wu, W., Toll, R. T., Jha, M. K., Cooper, C. M., Fatt, C. C., Pizzagalli, D. A., Trombello, J. M., Deckersbach, T., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3867 · Cited by 90
Self-citation
Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures
Bailey, N. W., Hoy, K. E., Rogasch, N. C., Thomson, R. H., McQueen, S., Elliot, D., Sullivan, C., Fulcher, B., Daskalakis, Z. J., & Fitzgerald, P. B. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.058 · Cited by 96
Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review
Dunlop, K., Talishinsky, A., & Liston, C. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1072-6 · Cited by 80
Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial
Whitton, A. E., Webb, C. A., Dillon, D. G., Jürgen Kayser, A., Rutherford, A. V., Goer, F., D, M. F. M., McGrath, P. J., Weissman, M. M., Parsey, R. V., et al. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.007 · Cited by 69
Self-citation
Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies
Klooster, D., Voetterl, H., Baeken, C., & Arns, M. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.09.009 · Cited by 64
Predicting Treatment Outcome in Major Depressive Disorder Using Serotonin 4 Receptor PET Brain Imaging, Functional MRI, Cognitive-, EEG-Based, and Peripheral Biomarkers: A NeuroPharm Open Label Clinical Trial Protocol
Köhler‐Forsberg, K., Jorgensen, A., Dam, V. H., Stenbæk, D. S., Fisher, P. M., Ip, C.-T., Ganz, M., Poulsen, H. E., Giraldi, A. G. E., Ozenne, B., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00641 · Cited by 67
Prognosis and Improved Outcomes in Major Depression: A Review
Kraus, C., Kadriu, B., Lanzenberger, R. R., Zarate, C. A., & Kasper, S. F. (2020) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.18205 · Cited by 64
Brain regulation of emotional conflict predicts antidepressant treatment response for depression
Fonzo, G. A., Etkin, A., Zhang, Y., Wu, W., Cooper, C. M., Fatt, C. C., Jha, M. K., Trombello, J. M., Deckersbach, T., Adams, P., et al. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0732-1 · Cited by 57
Self-citation
Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis
Zhou, Z., Gao, Y., Bao, W., Liang, K., Cao, L., Tang, M., Li, H., Hu, X., Zhang, L., Sun, H., et al. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105583 · Cited by 53
Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study
Meyer, B. M., Rabl, U., Huemer, J., Bartova, L., Kalcher, K., Provenzano, J., Brandner, C., Sezen, P., Kasper, S., Schatzberg, A. F., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0395-8 · Cited by 52
2,3,5,4 ′‐Tetrahydroxystilbene‐2‐O‐beta‐D‐glucoside Reverses Stress‐Induced Depression via Inflammatory and Oxidative Stress Pathways
Jiang, C.-Y., Qin, X., Yuan, M.-M., Lu, G.-J., & Cheng, Y. (2018) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2018/9501427 · Cited by 53
Metabolic activity in subcallosal cingulate predicts response to deep brain stimulation for depression
Brown, E. C., Clark, D., Forkert, N. D., Molnar, C., Kiss, Z. H. T., & Ramasubbu, R. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0745-5 · Cited by 53
A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial
Trivedi, M. H., South, C. C., Jha, M. K., Rush, A. J., Cao, J., Kurian, B. T., Phillips, M. L., Pizzagalli, D. A., Trombello, J. M., Oquendo, M. A., et al. (2018) · Psychotherapy and Psychosomatics · doi:10.1159/000491093 · Cited by 49
Self-citation
Pharmacotherapies Targeting GABA-Glutamate Neurotransmission for Treatment-Resistant Depression
Vecera, C. M., C. Courtes, A., Jones, G., Soares, J. C., & Machado-Vieira, R. (2023) · Pharmaceuticals · doi:10.3390/ph16111572 · Cited by 48
“In both MDD and TRD, region-speciﬁc normalization of GABA concentrations in the occipital cortex (OCC), ACC, and DMN have repeatedly been observed in response to all successful antidepressant therapies [19,22,23,63].”
Background
Structuro-functional surrogates of response to subcallosal cingulate deep brain stimulation for depression
Elias, G. J. B., Germann, J., Boutet, A., Pancholi, A., Beyn, M. E., Bhatia, K. D., Neudorfer, C., Loh, A., Rizvi, S. J., Bhat, V., et al. (2021) · Brain · doi:10.1093/brain/awab284 · Cited by 49
3T MRI of rapid brain activity changes driven by subcallosal cingulate deep brain stimulation
Elias, G. J. B., Germann, J., Boutet, A., Loh, A., Li, B. C. W., Pancholi, A., Beyn, M. E., Naheed, A., Bennett, N., Pinto, J., et al. (2021) · Brain · doi:10.1093/brain/awab447 · Cited by 49
Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder
McVoy, M. K., Aebi, M. E., Loparo, K. A., Lytle, S., Morris, A., Woods, N., Deyling, E. A., Tatsuoka, C. M., Kaffashi, F., Lhatoo, S. D., et al. (2019) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2018.0166 · Cited by 47
Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals
Greenberg, T., Fournier, J. C. J., Stiffler, R. S., Chase, H. W., Almeida, J., Aslam, H. A., Deckersbach, T., Cooper, C. M., Toups, M. S. P., Carmody, T., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0490-5 · Cited by 48
Self-citation
Alpha peak frequency-based Brainmarker-I as a method to stratify to pharmacotherapy and brain stimulation treatments in depression
Voetterl, H., Sack, A. T., Olbrich, S., Stuiver, S., Rouwhorst, R., Prentice, A., Pizzagalli, D. A., van der Vinne, N., van Waarde, J. A., Brunovský, M., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00160-7 · Cited by 46
Self-citation
Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression
Jiao, Y., Zhao, K., Wei, X., Carlisle, N. B., Keller, C. J., Oathes, D. J., Fonzo, G. A., & Zhang, Y. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02974-6 · Cited by 51
Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder
Dang, W., Gao, Z., Sun, X., Li, R., Cai, Q., & Grebogi, C. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-020-05665-9 · Cited by 43
Pretreatment Reward Sensitivity and Frontostriatal Resting-State Functional Connectivity Are Associated With Response to Bupropion After Sertraline Nonresponse
Ang, Y., Kaiser, R. H., Deckersbach, T., Almeida, J., Phillips, M. L., Chase, H. W., Webb, C. A., Parsey, R. V., D, M. F. M., McGrath, P. J., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.04.009 · Cited by 43
Self-citation
Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial
Jamieson, A. J., Harrison, B. J., Razi, A., & Davey, C. G. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01214-z · Cited by 41
Quantitative Electroencephalography in Guiding Treatment of Major Depression
Schiller, M. J. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00779 · Cited by 40
EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial
van der Vinne, N., Vollebregt, M. A., Rush, A. J., Eebes, M., van Putten, M. J. A. M., & Arns, M. (2021) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.12.005 · Cited by 39
Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity
Smith, E. E., Tenke, C. E., Deldin, P. J., Trivedi, M. H., Weissman, M. M., Auerbach, R. P., Bruder, G. E., Pizzagalli, D. A., & Jürgen Kayser, A. (2019) · Psychophysiology · doi:10.1111/psyp.13483 · Cited by 38
Self-citation
Associations between anterior cingulate thickness, cingulum bundle microstructure, melancholia and depression severity in unipolar depression
Mertse, N., Denier, N., Walther, S., Breit, S., Grosskurth, E. D., Federspiel, A., Wiest, R., & Bracht, T. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.01.035 · Cited by 38
Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study
Ip, C.-T., Olbrich, S., Ganz, M., Ozenne, B., Köhler‐Forsberg, K., Dam, V. H., Beniczky, S., Jørgensen, M. B., Frøkjær, V. G., Søgaard, B., et al. (2021) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2021.03.024 · Cited by 34
Connectivity between the anterior insula and dorsolateral prefrontal cortex links early symptom improvement to treatment response
Yuan, H., Zhu, X. L., Tang, W., Cai, Y., Shi, S., & Luo, Q. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.09.041 · Cited by 31
A resting state fMRI study of major depressive disorder with and without anxiety
Li, G., Liu, P.-H., Zhang, A., Andari, E., & Zhang, K. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114697 · Cited by 33
Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following Behavioral Activation Therapy for Depression
Walsh, E., Eisenlohr-Moul, T., Minkel, J., Bizzell, J., Petty, C., Crowther, A. J., Carl, H., Smoski, M., & Dichter, G. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.09.065 · Cited by 33
An electroencephalographic signature predicts craving for methamphetamine
Tian, W., Zhao, D., Ding, J., Zhan, S., Zhang, Y., Etkin, A., Wu, W., & Yuan, T. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101347 · Cited by 31
NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD
Ip, C.-T., Ganz, M., Dam, V. H., Ozenne, B., Rüesch, A., Köhler‐Forsberg, K., Jørgensen, M. B., Frøkjær, V. G., Søgaard, B., Christensen, S. R., et al. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.06.021 · Cited by 30
A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment
Tsai, Y., Li, C., & Juan, C.-H. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1073984 · Cited by 32
Cerebral Blood Perfusion Predicts Response to Sertraline versus Placebo for Major Depressive Disorder in the EMBARC Trial
Cooper, C. M., Fatt, C. C., Jha, M. K., Fonzo, G. A., Grannemann, B. D., Carmody, T. J., Ali, A., Aslan, S., Almeida, J., Deckersbach, T., et al. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.04.007 · Cited by 29
Self-citation
Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres
Yu, M., Cullen, N. C., Linn, K. A., Oathes, D. J., Seok, D., Cook, P. A., Duprat, R., Aselcioglu, I., Moore, T. M., Davatzikos, C. A., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01039-8 · Cited by 29
Self-citation
Yijinjing Qigong intervention shows strong evidence on clinical effectiveness and electroencephalography signal features for early poststroke depression: A randomized, controlled trial
Sun, P., Zhang, S., Jiang, L., Ma, Z., Yao, C., Zhu, Q., & Fang, M. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.956316 · Cited by 29
QEEG in affective disorder: about to be a biomarker, endophenotype and predictor of treatment response
Kesebir, S., & Yosmaoğlu, A. (2018) · Heliyon · doi:10.1016/j.heliyon.2018.e00741 · Cited by 29
Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression
Huang, S., Yu, Y., Chen, H.-H., Hung, C.-C., Wang, Y.-T., Chang, C. H., Peng, S., & Kuo, P. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-04958-8 · Cited by 28
Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance
Homan, S., Muscat, W., Joanlanne, A., Marousis, N., Cecere, G., Hofmann, L. A., Ji, E., Neumeier, M. S., Vetter, S., Seifritz, E., et al. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.11.033 · Cited by 29
Functional stability predicts depressive and cognitive improvement in major depressive disorder: A longitudinal functional MRI study
Li, X., Zhang, Y., Meng, C., Zhang, C., Zhao, W., Zhu, D., & Zhu, J. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110396 · Cited by 27
Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial
Nguyen, K. P., Fatt, C. C., Treacher, A., Mellema, C., Cooper, C. M., Jha, M. K., Kurian, B. T., D, M. F. M., McGrath, P. J., Weissman, M. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.09.011 · Cited by 28
Self-citation
Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy
Zhang, X., Braun, U., Tost, H., & Bassett, D. S. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.12.015 · Cited by 31
Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression
Wu, G., Wang, X., & Baeken, C. (2019) · Human Brain Mapping · doi:10.1002/hbm.24828 · Cited by 25
A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants
Nemati, S., Akiki, T. J., Roscoe, J., Ju, Y., Averill, C. L., Fouda, S., Dutta, A., McKie, S., Krystal, J. H., Deakin, J. F. W., et al. (2019) · iScience · doi:10.1016/j.isci.2019.100800 · Cited by 28
Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT
Zilcha-Mano, S., & Webb, C. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.609585 · Cited by 25
Self-citation
Pre-stimulus Brain Activity Is Associated With State-Anxiety Changes During Single-Session Transcranial Direct Current Stimulation
Nishida, K., Koshikawa, Y., Morishima, Y., Yoshimura, M., Katsura, K., Ueda, S., Ikeda, S., Ishii, R., Pascual-Marqui, R. D., & Kinoshita, T. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00266 · Cited by 25
Associations between lifetime stress exposure and the error-related negativity (ERN) differ based on stressor characteristics and exposure timing in young adults
Banica, I., Sandre, A., Shields, G. S., Slavich, G. M., & Weinberg, A. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00883-z · Cited by 31
Critical role of rhythms in prefrontal transcranial magnetic stimulation for depression: A randomized sham‐controlled study
Tsai, Y., Li, C., Liang, W., Muggleton, N. G., Tsai, C., Huang, N. E., & Juan, C.-H. (2021) · Human Brain Mapping · doi:10.1002/hbm.25740 · Cited by 23
Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder
Zhang, Y., Lei, L., Liu, Z., Gao, M., Liu, Z., Sun, N., Yang, C., Zhang, A., Wang, Y., & Zhang, K. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.827536 · Cited by 26
Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review
Strafella, R., Chen, R. E., Rajji, T. K., Blumberger, D. M., & Voineskos, D. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.940759 · Cited by 23
Predicting Antidepressant Effects of Ketamine: the Role of the Pregenual Anterior Cingulate Cortex as a Multimodal Neuroimaging Biomarker
Weigand, A., Gärtner, M., Scheidegger, M., Wyss, P. O., Henning, A., Seifritz, E., Stippl, A., Herrera-Meléndez, A. L., Bajbouj, M., Aust, S., et al. (2022) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyac049 · Cited by 22
Pilot Study of An Intracranial Electroencephalography Biomarker of Depressive Symptoms in Epilepsy
Scangos, K. W., Ahmad, H. S., Shafi, A., Sellers, K. K., Dawes, H. E., Krystal, A. D., & Chang, E. F. (2019) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.19030081 · Cited by 23
Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive‐emotional training for treatment‐resistant depression
Nikolin, S., Martin, D. M., Loo, C. K. A., Iacoviello, B. M., & Boonstra, T. W. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14656 · Cited by 22
Thalamocortical dysrhythmia in patients with schizophrenia spectrum disorder and individuals at clinical high risk for psychosis
Kim, M., Lee, T. H., Park, H., Moon, S., Lho, S. K., & Kwon, J. S. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01180-6 · Cited by 25
EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder
Oakley, T., Coskuner, J., Cadwallader, A., Ravan, M., & Hasey, G. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3204861 · Cited by 22
Predictive Biomarkers of Treatment Response in Major Depressive Disorder
Stolz, L. A., Kohn, J. N., Smith, S. E., Benster, L. L., & Appelbaum, L. G. (2023) · Brain Sciences · doi:10.3390/brainsci13111570 · Cited by 22
Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response
Tong, X., Xie, H., Wu, W., Keller, C. J., Fonzo, G. A., Chidharom, M., Carlisle, N. B., Etkin, A., & Zhang, Y. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.177 · Cited by 22
Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation
Koshiyama, D., Kirihara, K., Usui, K., Tada, M., Fujioka, M., Morita, S., Kawakami, S., Yamagishi, M., Sakurada, H., Sakakibara, E., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.030 · Cited by 22
Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
Fan, S., Nemati, S., Akiki, T. J., Roscoe, J., Averill, C. L., Fouda, S., Averill, L. A., & Abdallah, C. G. (2020) · Chronic Stress · doi:10.1177/2470547020984726 · Cited by 20
Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission
Davey, C. G., Cearns, M., Jamieson, A. J., & Harrison, B. J. (2021) · Psychological Medicine · doi:10.1017/s0033291721004323 · Cited by 21
Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder
Chao, Z. C., Dillon, D. G., Liu, Y.-H., Barrick, E. M., & Wu, C. (2022) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.220046 · Cited by 20
Self-citation
Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression
Jones, C., & Nemeroff, C. B. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-981-33-6044-0_27 · Cited by 23
Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG
Campanella, S., Arikan, K., Babiloni, C., Balconi, M., Bertollo, M., Betti, V., Bianchi, L., Brunovský, M., Buttinelli, C., Comani, S., et al. (2020) · Clinical EEG and Neuroscience · doi:10.1177/1550059420954054 · Cited by 21
Selection of the optimal dose of sertraline for depression: A dose-response meta-analysis of randomized controlled trials
Luo, X., Zhu, D., Li, J., Ren, M., Liu, Y., Si, T., & Chen, Y. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115391 · Cited by 19
Neurometabolite changes in response to antidepressant medication: A systematic review of 1H-MRS findings
Boucherie, D. E., Reneman, L., Ruhé, H. G., & Schrantee, A. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103517 · Cited by 23
Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression
Prompiengchai, S., & Dunlop, K. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01907-1 · Cited by 23
Early change of prefrontal theta cordance and occipital alpha asymmetry in the prediction of responses to antidepressants
Bareš, M., Novák, T., Vlček, P., Hejzlar, M., & Brunovský, M. (2019) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2019.06.006 · Cited by 19
Chronic stress pathology and ketamine-induced alterations in functional connectivity in major depressive disorder: An abridged review of the clinical evidence
(2020) · Advances in Pharmacology · doi:10.1016/bs.apha.2020.04.003 · Cited by 18
Utility of Cognitive Neural Features for Predicting Mental Health Behaviors
Kato, R., Balasubramani, P. P., Ramanathan, D., & Mishra, J. (2022) · Sensors · doi:10.3390/s22093116 · Cited by 21
Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation
Li, C., Chen, C.-S., Cheng, C., Chen, C.-P., Chen, J.-P., Chen, M., Bai, Y., & Tsai, S. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.059 · Cited by 16
Intra- and Inter-Network connectivity of the default mode network differentiates Treatment-Resistant depression from Treatment-Sensitive depression
Barreiros, A. R., Breukelaar, I. A., Prentice, A., Mayur, P. M., Tomimatsu, Y., Funayama, K., Foster, S., Malhi, G. S., Arns, M., Harris, A. D., et al. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103656 · Cited by 14
Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression
Thai, M., Olson, E. A., Nickels, S., Dillon, D. G., Webb, C. A., Ren, B., Killgore, W. D. S., Rauch, S. L., Rosso, I. M., & Pizzagalli, D. A. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03020-9 · Cited by 13
Self-citation
Acute effects of ketamine on the pregenual anterior cingulate: linking spontaneous activation, functional connectivity, and glutamate metabolism
Gärtner, M., Weigand, A., Scheidegger, M., Lehmann, M., Wyss, P. O., Wunder, A., Henning, A., & Grimm, S. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-021-01377-2 · Cited by 13
Task-Modulated Brain Activity Predicts Antidepressant Responses of Prefrontal Repetitive Transcranial Magnetic Stimulation: A Randomized Sham-Control Study
Li, C., Cheng, C., Juan, C.-H., Tsai, Y., Chen, M., Bai, Y., Tsai, S., & Su, T. (2021) · Chronic Stress · doi:10.1177/24705470211006855 · Cited by 14
Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial
Ang, Y.-S., Bruder, G. E., Keilp, J. G., Rutherford, A., Alschuler, D. M., Pechtel, P., Webb, C. A., Carmody, T., Fava, M., Cusin, C., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291720004286 · Cited by 12
Self-citation
Cingulate-mediated depressive symptoms in neurologic disease and therapeutics
Riva‐Posse, P., Holtzheimer, P. E., & Mayberg, H. S. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-444-64196-0.00021-2 · Cited by 14
Prospective testing of a neurophysiologic biomarker for treatment decisions in major depressive disorder: The PRISE-MD trial
Cook, I. A., Hunter, A. M., Caudill, M. M., Abrams, M., & Leuchter, A. F. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.02.028 · Cited by 12
A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals
Guan, Z., Zhang, X., Huang, W., Li, K., Chen, D., Li, W., Sun, J., Chen, L., Mao, Y., Sun, H., et al. (2024) · Neuroscience Bulletin · doi:10.1007/s12264-024-01319-7 · Cited by 12
Exposure and response prevention therapy augmented with naltrexone in kleptomania: a controlled case study using galvanic skin response for monitoring
Olbrich, S., Jahn, I., & Stengler, K. (2019) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s1352465819000213 · Cited by 11
Mindfulness augmentation for anxiety through concurrent use of transcranial direct current stimulation: a randomized double-blind study
Nishida, K., Morishima, Y., Pascual-Marqui, R. D., Minami, S., Yamane, T., Michikura, M., Ishikawa, H., & Kinoshita, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-02177-3 · Cited by 11
Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future
Godlewska, B. R. (2020) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-020-00198-2 · Cited by 15
BMRMI Reduces Depressive Rumination Possibly through Improving Abnormal FC of Dorsal ACC
Yang, M., Guo, Z., Lv, X., Zhang, Z.-Q., Wang, W., Wang, J., Hong, L., Lin, Y.-N., & Liu, C.-H. (2022) · Neural Plasticity · doi:10.1155/2022/8068988 · Cited by 12
Predicting antidepressant response by electroencephalography
Michel, C. M., & Pascual‐Leone, Á. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0476-5 · Cited by 10
Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate
Smith, E. E., Choi, K. S., Veerakumar, A., Obatusin, M., Howell, B. L., Smith, A. H., Tiruvadi, V., Crowell, A. L., Riva‐Posse, P., Alagapan, S., et al. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.939258 · Cited by 10
Reduced frontocingulate theta connectivity during emotion regulation in major depressive disorder
Steinmann, S., Tiedemann, K. J., Kellner, S., Wellen, C. M., Haaf, M., Mulert, C., Rauh, J., & Leicht, G. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.03.022 · Cited by 9
Brain–heart interaction disruption in major depressive disorder: disturbed rhythm modulation of the cardiac cycle on brain transient theta bursts
Zhou, H., Xiong, T., Dai, Z., Zou, H., Wang, X., Tang, H., Huang, Y., Sun, H., You, W., Yao, Z., et al. (2023) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-023-01628-4 · Cited by 10
Pre-operative predictors of response to deep brain stimulation in depression: A systematic review and meta-analysis
Mol, G. J. J., Runia, N., van Wingen, G. A., Denys, D. A. J. P., Mocking, R. J. T., & Bergfeld, I. O. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119387 · Cited by 10

Authors: Pizzagalli, D. A., Webb, C. A., Dillon, D. G., Tenke, C. E., Kayser, J., Goer, F., Fava, M., McGrath, P., Weissman, M., Parsey, R., Adams, P., Trombello, J., Cooper, C., Deldin, P., Oquendo, M. A., McInnis, M. G., Carmody, T., Bruder, G., & Trivedi, M. H.
Affiliations: Harvard University (US)
New York State Psychiatric Institute (US)
Department of Psychiatry, New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York
Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston
Stony Brook University (US)
The University of Texas Southwestern Medical Center (US)
University of Michigan (US)
Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia
Corresponding author: Pizzagalli, Diego A.
ORCID: Pizzagalli, Diego A.
Webb, Christian A.
Dillon, Daniel G.
Tenke, Craig E.
Goer, Franziska
McGrath, Patrick
Weissman, Myrna
Parsey, Ramin
Trombello, Joseph
Cooper, Crystal
Deldin, Patricia
McInnis, Melvin G.
Carmody, Thomas
Trivedi, Madhukar H.
Venue: JAMA Psychiatry
Publisher: American Medical Association (AMA)
Published: 2018-06-01
Volume: 75
Issue: 6
Pages: 547
ISSN: 2168-622X (ISSN-L)
DOI: 10.1001/jamapsychiatry.2018.0252
PMC: PMC6083825
OpenAlex: W2797156514
Cited by: 189
References: 117
Citations by year: 2026: 19 · 2025: 16 · 2024: 18 · 2023: 19 · 2022: 16 · 2021: 41 · 2020: 25 · 2019: 27 · 2018: 8
FWCI: 16.29
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: JAMA Psychiatry (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: John Templeton Foundation
Bristol-Myers Squibb
Eli Lilly and Company
Pfizer
AstraZeneca
GlaxoSmithKline
Novartis
Sanofi
Teva Pharmaceutical Industries
Sunovion
National Alliance for Research on Schizophrenia and Depression
Vanda Pharmaceuticals
Arcadia Fund
Avanir Pharmaceuticals
Cerecor
Supernus Pharmaceuticals
American Society of Clinical Psychopharmacology
Usona Institute
Otsuka America
Belvoir Media Group
Vistagen Therapeutics
University of Oxford
Servier
H. Lundbeck A/S
Agency for Healthcare Research and Quality
Allergan
National Institute of Mental Health (R00MH094438, U01MH092250, K23MH108752, U01MH092221)
National Institute of Diabetes and Digestive and Kidney Diseases
Janssen Research and Development
National Center for Advancing Translational Sciences
Topics: Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords: major depressive disorder, rostral anterior cingulate cortex, theta activity, resting-state EEG, antidepressant response prediction, sertraline, source localization, Hamilton Depression Rating Scale, incremental predictive validity
MeSH terms: Adult, Antidepressive Agents, Depressive Disorder, Major, Major Depressive Disorder, Electroencephalography, Female, Gyrus Cinguli, Humans, Male, Middle Aged, Prognosis, Theta Rhythm, Treatment Outcome, Sertraline, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks