Cited by 342Functional Brain Connectivity StudiesTreatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

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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

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Dunlop, Boadie W., Rajendra, Justin K., Craighead, W. Edward, Kelley, Mary E., McGrath, Callie L., Choi, Ki Sueng, Kinkead, Becky, Nemeroff, Charles B., Mayberg, Helen S.·2017·American Journal of Psychiatry

DOI 10.1176/appi.ajp.2016.16050518PMC PMC5453828

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Abstract

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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Method
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Background
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Background
Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder
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Background
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Method
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Result
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Background
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Background
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Self-citation
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Self-citation
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Background
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Background
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“DMN is more active in the resting state, while in the task state, its activities is weakened (29).”
Background
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“ventrolateral PFC and FP with the SCA was associated with remission and treatment failure.(43) Although we did”
Method
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“In PReDICT, lower connectivity of the subcallosal cingulate with the insula and ventral frontal regions was associated with remission to escitalopram and treatment failure with cognitive behavioral therapy (55).”
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“Finally, Dunlop et al. (2017) used multivariate pattern analysis to examine connectivity signatures of clients with major depression who responded to CBT and antidepressant medication compared to clients who were not responsive to treatment.”
Method
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“Moreover, in MDD the connectivity between different brain areas under resting conditions has been shown to significantly predict response to first-line treatment, either psychotherapy or to medication [36].”
Background
Pathways to neuroprediction: opportunities and challenges to prediction of treatment response in depression
Langenecker, S. A., Crane, N. A., Jenkins, L. M., Phan, K. L., & Klumpp, H. (2018) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-018-0140-2 · Cited by 11
Multidimensional imaging techniques for prediction of treatment response in major depressive disorder
Langenecker, S. A., Klumpp, H., Peters, A. T., Crane, N. A., DelDonno, S. R., Bessette, K. L., Ajilore, O., Leow, A., Shankman, S. A., Walker, S. J., et al. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2018.07.001 · Cited by 11
“…the role of intrinsic network connectivity (e.g., KeyNet) in the prediction of treatment response in mood and related disorders (Klumpp et al., 2014; Salomons et al., 2013; Dichter et al., 2015; Whitfield-Gabrieli et al., 2015; van Waarde et al., 2014; Ge et al., n.d.; Dunlop et al., 2017).”
Personalized mental health: Artificial intelligence technologies for treatment response prediction in anxiety disorders
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Baseline Patterns of Resting Functional Connectivity within Posterior Default-Mode Intranetwork Associated with Remission to Antidepressants in Major Depressive Disorder
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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: Dunlop, Boadie W.
Rajendra, Justin K.
Craighead, W. Edward
Kelley, Mary E.
Choi, Ki Sueng
Kinkead, Becky
Nemeroff, Charles B.
Mayberg, Helen S.
Venue: American Journal of Psychiatry
Publisher: American Psychiatric Association Publishing
Published: 2017-06-01
Volume: 174
Issue: 6
Pages: 533-545
ISSN: 0002-953X (ISSN-L)
1535-7228
DOI: 10.1176/appi.ajp.2016.16050518
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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Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive- behavioral therapy or antidepressant medication for major depressive disorder

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