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Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive- behavioral therapy or antidepressant medication for major depressive disorder
Open accessAbstract
OBJECTIVE: The purpose of this article was to inform the first-line treatment choice between cognitive-behavioral therapy (CBT) or an antidepressant medication for treatment-naive adults with major depressive disorder by defining a neuroimaging biomarker that differentially identifies the outcomes of remission and treatment failure to these interventions. METHOD: Functional MRI resting-state functional connectivity analyses using a bilateral subcallosal cingulate cortex (SCC) seed was applied to 122 patients from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study who completed 12 weeks of randomized treatment with CBT or antidepressant medication. Of the 122 participants, 58 achieved remission (Hamilton Depression Rating Scale [HAM-D] score ≤7 at weeks 10 and 12), and 24 had treatment failure (<30% decrease from baseline in HAM-D score). A 2×2 analysis of variance using voxel-wise subsampling permutation tests compared the interaction of treatment and outcome. Receiver operating characteristic curves constructed using brain connectivity measures were used to determine possible classification rates for differential treatment outcomes. RESULTS: The resting-state functional connectivity of the following three regions with the SCC was differentially associated with outcomes of remission and treatment failure to CBT and antidepressant medication and survived application of the subsample permutation tests: the left anterior ventrolateral prefrontal cortex/insula, the dorsal midbrain, and the left ventromedial prefrontal cortex. Using the summed SCC functional connectivity scores for these three regions, overall classification rates of 72%-78% for remission and 75%-89% for treatment failure was demonstrated. Positive summed functional connectivity was associated with remission with CBT and treatment failure with medication, whereas negative summed functional connectivity scores were associated with remission to medication and treatment failure with CBT. CONCLUSIONS: Imaging-based depression subtypes defined using resting-state functional connectivity differentially identified an individual's probability of remission or treatment failure with first-line treatment options for major depression. This biomarker should be explored in future research through prospective testing and as a component of multivariate treatment prediction models.
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“it is possible that the subcallosal cingulate stimulation affects pathways that connect with the OFC (Johansen-Berg et al., 2008; Lujan et al., 2013; Dunlop et al., 2017; Riva-Posse et al., 2018).”
From The orbitofrontal cortex: reward, emotion and depression (2020) · cites this paper as Dunlop et al., 2017
Experimental — accuracy not yet measuredIn the citing paper
“Incorporation of gene-environment interactions improves the prediction of depression over the polygenic risk score approach. 302”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [302]
In the citing paper
“For example, a study of 122 treatment-resistant depression cases revealed two subtypes (53). One had negative sub- callosal cingulate cortex to left insula connectivity and responded to medication but not cognitive behavioral therapy. The other had positive connectivity and responded to therapy but not medication.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [53]
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Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex
Tian, S., Sun, Y., Shao, J., Zhang, S., Mo, Z., Liu, X., Wang, Q., Wang, L., Zhao, P., Chattun, M. R., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24872 · Cited by 50
“FC of subcallosal cingulate cortex could identify individuals' treatment outcome to cognitive behavioral therapy (CBT) and antidepressant medicine (Dunlop et al., 2017).”
BackgroundTherapeutic potential of ketamine for alcohol use disorder
Worrell, S. D., & Gould, T. J. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.05.006 · Cited by 47
Kappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M. B., Klein, D. N., Arnow, B. A., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01623-9 · Cited by 51
“These cover a wide range of different approaches such as using big data sets and machine learning models to predict the MDD course from baseline self-reports [19, 20], explorative data-mining strategies in order to define decision trees for the treatment of depression [21], algorithmbased treatments associated with sh…”
BackgroundSelf-citationUnpacking Major Depressive Disorder: From Classification to Treatment Selection
Kennedy, S. H., & Ceniti, A. K. (2017) · The Canadian Journal of Psychiatry · doi:10.1177/0706743717748883 · Cited by 50
“In a separate example, using both positron emission tomography and fMRI, baseline moderators of response to either escitalopram or cognitive behavioural therapy (CBT) were identified: levels of glucose metabolism in the right anterior insula and functional connectivity of the subcallosal cingulate cortex were strong p…”
ResultResting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review
Taylor, J. J., Kurt, H. G., & Anand, A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.565136 · Cited by 50
“By contrast, remission was negatively correlated with rostral/subgenual ACC RSFC to right precentral gyrus and right posterior putamen (71).”
BackgroundBrain-based mechanisms of late-life depression: Implications for novel interventions
Gunning, F. M., Oberlin, L. E., Schier, M. M., & Victoria, L. W. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.05.002 · Cited by 49
The ventromedial prefrontal cortex and emotion regulation: lost in translation?
Alexander, L., Wood, C. M., & Roberts, A. (2022) · The Journal of Physiology · doi:10.1113/jp282627 · Cited by 48
“…and activity change within it (Downar et al., 2014; Fox et al., 2012; Hiser & Koenigs, 2018; Salomons et al., 2014) as well as its inter-regional functional connectivity (Dunlop et al., 2017; Weigand et al., 2018) has been identified as a good predictor of response to a variety of treatments.”
BackgroundDudek, E. M., & Dodell‐Feder, D. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.12.020 · Cited by 45
Ahmed, A. T., Frye, M. A., Rush, A. J., Biernacka, J. M., Craighead, W. E., McDonald, W. M., Bobo, W. V., Riva‐Posse, P., Tye, S. J., Mayberg, H. S., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.05.005 · Cited by 44
Self-citationKelley, M. E., Dunlop, B. W., Nemeroff, C. B., Lori, A., Carrillo‐Roa, T., Binder, E. B., Kutner, M., Aponte‐Rivera, V., Craighead, W. E., & Mayberg, H. S. (2018) · Depression and Anxiety · doi:10.1002/da.22832 · Cited by 48
Self-citationWilliams, L. M., Carpenter, W. T., Carretta, C. M., Papanastasiou, E., & Vaidyanathan, U. (2023) · CNS Spectrums · doi:10.1017/s1092852923002420 · Cited by 49
Katayama, N., Nakagawa, A., Umeda, S., Terasawa, Y., Kurata, C., Tabuchi, H., Kikuchi, T., & Mimura, M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101877 · Cited by 45
“These findings are consistent with the hypothesis that frontopolar cortical dysfunction plays a key role in the hopelessness that manifests in patients with MDD. Non-standard abbreviations MDD Major depressive disorder BA10 Brodmann area [BA] 10 GRID-HAMD GRID-Hamilton Depression Rating Scale BDI-II Beck Depression In…”
BackgroundInfluentialDang, W., Gao, Z., Sun, X., Li, R., Cai, Q., & Grebogi, C. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-020-05665-9 · Cited by 43
Jamieson, A. J., Harrison, B. J., Razi, A., & Davey, C. G. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01214-z · Cited by 41
“The rACC has also been shown to predict treatment response and remission [29, 35].”
ResultKlok, M., van Eijndhoven, P., Árgyelán, M., Schene, A. H., & Tendolkar, I. (2019) · BJPsych Open · doi:10.1192/bjo.2019.58 · Cited by 40
Identifying CBT non-response among OCD outpatients: A machine-learning approach
Hilbert, K., Jacobi, T., Kunas, S. L., Elsner, B., Reuter, B., Lueken, U., & Kathmann, N. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1839140 · Cited by 44
Fu, C. H. Y., Antoniades, M., Erus, G., García, J., Yong Fan, A., Arnone, D., Arnott, S. R., Chen, T., Choi, K. S., Fatt, C. C., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-023-00187-w · Cited by 38
“On the basis of functional connectivity measures, two to four MDD subtypes have been reported 11–15 .”
Self-citationNeuroimaging for precision medicine in psychiatry
Williams, L. M., & Whitfield, S. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01917-z · Cited by 42
Riva‐Posse, P., Inman, C. S., Choi, K. S., Crowell, A. L., Gross, R. E., Hamann, S. B., & Mayberg, H. S. (2019) · Brain stimulation · doi:10.1016/j.brs.2019.01.015 · Cited by 37
Self-citationBrain–computer interfaces for neuropsychiatric disorders
Oganesian, L. L., & Shanechi, M. M. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00177-2 · Cited by 47
“Besides, the biomarker might change over time owing to variations in the disorder stage and other stimulation-induced eff ects 31,35,41,42,95–97 .”
Trivedi, M. H., Fatt, C. C., Jha, M. K., Cooper, C. M., Trombello, J. M., Mason, B. L., Hughes, J. L., Gadad, B. S., Czysz, A. H., Toll, R. T., et al. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.12.004 · Cited by 36
Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response
Kaiser, R. H., Chase, H. W., Phillips, M. L., Deckersbach, T., Parsey, R. V., D, M. F. M., McGrath, P. J., Weissman, M. M., Oquendo, M. A., McInnis, M. G., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.03.020 · Cited by 37
Godlewska, B. R. (2018) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.12.010 · Cited by 38
Seeberg, I., Kjærstad, H. L., & Miskowiak, K. W. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00337 · Cited by 37
“In keeping with this, one study of patients undergoing either pharmacotherapy with fluoxetine or CBT found that clinical remission to pharmacotherapy was predicted by greater GM volume density in the right rostral ACC, left PCC, whereas no prediction was found for CBT (68).”
BackgroundMethodInfluentialCristea, I. A., Karyotaki, E., Hollon, S. D., Cuijpers, P., & Gentili, C. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.03.022 · Cited by 34
Dunlop, K., Grosenick, L., Downar, J. H., Vila‐Rodriguez, F., Gunning, F. M., Daskalakis, Z. J., Blumberger, D. M., & Liston, C. M. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.01.012 · Cited by 36
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Hahn, A., Lanzenberger, R. R., & Kasper, S. F. (2018) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy100 · Cited by 40
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Fulong, X., Spruyt, K., Lu, C., Dianjiang, Z., Jun, Z., & Fang, H. (2020) · SLEEP · doi:10.1093/sleep/zsaa018 · Cited by 38
Chang, K.-Y., Tik, M., Mizutani-Tiebel, Y., Schuler, A., Taylor, P. C. J., Campana, M., Vogelmann, U., Huber, B., Dechantsreiter, E., Thielscher, A., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120596 · Cited by 35
“To analyze the change of BOLD activation over the course of an iTBS session, we conducted an investigation wherein the complete 20 iTBS trains were divided into four blocks, each consisting of 5 trains of iTBS (equivalent to 150 stimuli).”
Wang, Y., Wang, C., Zhou, J., Chen, X., Liu, R., Zhang, Z., Feng, Y., Feng, L., Liu, J., Zhou, Y., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03117-1 · Cited by 35
“In a study using fMRI, Dunlop et al. [18] discovered that the rsFC of the sgACC was differentially associated with treatment outcomes for cognitive behavior therapy and antidepressant medication [18].”
Resting State Functional Connectivity and Outcomes of Psychotherapies for Late-Life Depression
Solomonov, N., Victoria, L. W., Dunlop, K., Respino, M., Hoptman, M. J., Zilcha‐Mano, S., Oberlin, L. E., Liston, C. M., Areán, P. A., Gunning, F. M., et al. (2020) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2020.04.008 · Cited by 35
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Engelmann, J. B., Berns, G. S., & Dunlop, B. W. (2017) · Psychological Medicine · doi:10.1017/s0033291717001428 · Cited by 34
“Furthermore, resting state metabolism of the AI (McGrath et al. 2013; Dunlop et al. 2015), and its functional connectivity with the subcallosal cingulate cortex (Dunlop et al. 2017), has been identified as a potential biomarker for treatment selection in patients with MDD.”
BackgroundSelf-citationJaeckle, T., Williams, S., Barker, G. J., Basilio, R., Carr, E., Goldsmith, K., Colasanti, A., Giampietro, V., Cleare, A. J., Young, A. H., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721004797 · Cited by 36
“This work builds on extensive evidence for the pathophysiological importance of SC networks in MDD (Drevets & Savitz, 2008; Dunlop et al., 2017; Price & Drevets, 2010; Ressler & Mayberg, 2007; Siegle, Carter, & Thase, 2006).”
Paolini, M., Harrington, Y. A., Colombo, F., Bettonagli, V., Poletti, S., Carminati, M., Colombo, C., Benedetti, F., & Zanardi, R. (2023) · Journal of Psychopharmacology · doi:10.1177/02698811231190859 · Cited by 33
“…regions: lower rs-FC between ACC, ventro-medial prefrontal cortex and posterior cingulate cortex has been associated with better treatment response (Dunlop et al., 2017; Goldstein-Piekarski et al., 2018), as well as higher connectivity with right dorsolateral prefrontal cortex (Straub et al.,…”
BackgroundWu, H., Liu, R., Zhou, J., Feng, L., Wang, Y., Chen, X., Zhang, Z., Cui, J., Zhou, Y., & Wang, G. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02152-0 · Cited by 30
“An accuracy of 70–89% was achieved by using fMRI to predict whether MDD patients were in remission after 12 weeks of antidepressant treatment [14, 15, 18].”
Circuit based anti-correlation, attention orienting, and major depression
Fossati, P. (2019) · CNS Spectrums · doi:10.1017/s1092852918001402 · Cited by 30
Blame-rebalance fMRI neurofeedback in major depressive disorder: A randomised proof-of-concept trial
Zahn, R., Weingartner, J. H., Basílio, R., Bado, P., Mattos, P., Sato, J. R., de Oliveira‐Souza, R., Fontenelle, L. F., Young, A. H., & Moll, J. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101992 · Cited by 29
Zou, L.-W., Wu, X.-Y., Tao, S.-M., Yang, Y.-J., Zhang, Q., Hong, X., Xie, Y., Li, T.-T., Zheng, S.-S., & Tao, F. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab016 · Cited by 27
“In addition, ACC was regarded as a promising predictor for antidepression response from several studies (Dunlop et al., 2017; Godlewska et al., 2018; Wolke et al., 2019).”
Result
Metadata
- Authors
- Dunlop, B. W., Rajendra, J. K., Craighead, W. E., Kelley, M. E., McGrath, C. L., Choi, K. S., Kinkead, B., Nemeroff, C. B., & Mayberg, H. S.
- Affiliations
- Harvard University (US)
- ORCID
- Venue
- American Journal of Psychiatry
- Published
- 2017-06-01
- Volume
- 174
- Issue
- 6
- Pages
- 533-545
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC5453828
- OpenAlex
- W2600118519
- Cited by
- 342
- References
- 80
- Citations by year
- 2026: 20 · 2025: 32 · 2024: 30 · 2023: 30 · 2022: 31 · 2021: 47 · 2020: 55 · 2019: 50 · 2018: 28 · 2017: 19
- FWCI
- 23.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01mh080880-05, UL1TR000454)
- National Institute of Mental Health (K23MH086690, R01-MH080880, P50MH077083)
- National Center for Research Resources (UL1RR025008, M01RR000039)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- major depressive disorder, resting-state functional connectivity, cognitive behavioral therapy, antidepressants, treatment response prediction, remission, treatment failure, neuroimaging biomarkers, ventromedial prefrontal cortex, dorsal midbrain, anterior insula, ventrolateral prefrontal cortex, individualized treatment selection
- MeSH terms
- Duloxetine Hydrochloride, Adolescent, Adult, Aged, Antidepressive Agents, Depressive Disorder, Major, Major Depressive Disorder, Dominance, Cerebral, Female, Gyrus Cinguli, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Psychiatric Status Rating Scales, Citalopram, Cognitive Behavioral Therapy, Outcome Assessment, Health Care, Prefrontal Cortex, Young Adult
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