Cited by 175Transcranial Magnetic Stimulation StudiesTreatment of Major DepressionNeural and Behavioral Psychology Studies

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Targeting the affective brain—a randomized controlled trial of real-time fMRI neurofeedback in patients with depression

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Mehler, David M. A., Sokunbi, Moses O., Habes, Isabelle, Barawi, Kali, Subramanian, Leena, Range, Maxence, Evans, John, Hood, Kerenza, Lührs, Michael, Keedwell, Paul, Goebel, Rainer, Linden, David E. J.·2018·Neuropsychopharmacology

DOI 10.1038/s41386-018-0126-5PMC PMC6186421

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Abstract

Functional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = -0.415 [95% CI -4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative.

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Fig. 1 CONSORT diagram of clinical trialCC BY · PubMed Central

Fig. 2 Main clinical outcome measure HDRS-17 for NFE and NFS groups at baseline, post-intervention, and follow up. a Mean and standard error of the mean (SEM) HDRS scores, b remission rate in %age based on HDRS, c Robust regression analysis between change in total self-efficacy scores, and residualized HDRS-17 scores at primary endpoint. *p < 0.05CC BY · PubMed Central

Fig. 3 ROI analysis. Average t-value of target ROIs for each session. Pooled mean shown, as well as means for both groups with error bars showing standard error of the mean (SEM). *p < 0.05 at FDR correctionCC BY · PubMed Central

Fig. 4 Whole-brain analysis. a Probability map (PM) of the localizer. b Activity of intervention groups, shown separately for NF-emotion and NF-scene group. c Group contrast. Key areas are labeled with numbers: (1) Insular cortex/ ventrolateral prefrontal cortex, (2) parahippocampal Place area (PPA), (3) supplementary motor area, (4) lingual gyrus, (5) premotor cortex, (6) superior parietal lobule, and (7) ventrolateral prefrontal cortex. Statistical maps cluster-threshold corrected for multiple comparison (p < 0.001)CC BY · PubMed Central

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The Hospital Anxiety and Depression Scale
Zigmond, A. S., & Snaith, R. P. (1983) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.1983.tb09716.x · Cited by 47,139
Self-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Depression and quality of life in older persons: A review
Sivertsen, H., Bjørkløf, G. H., Engedal, K., Selbæk, G., & Helvik, A.-S. (2015) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000437299 · Cited by 14,152
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales
Carver, C. S., & White, T. L. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.67.2.319 · Cited by 7,306
Robust Regression and Outlier Detection
Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183
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 · cited 3×
“5% for secondary augmentation therapy [43].”
Influential
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
The Self-Efficacy Scale: Construction and Validation
Sherer, M., Maddux, J. E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., & Rogers, R. W. (1982) · Psychological Reports · doi:10.2466/pr0.1982.51.2.663 · Cited by 4,028
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

Show all 53 references

Research works printed but not linked: 1

Qids S, Qids-c CR, Rush aJ, Trivedi MH, Ibrahim HM, Carmody TJ, et al. The 16-Item quick inventory of depressive. Depression. 2003;54:573–83.

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Self-citation
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Authors: Mehler, D. M. A., Sokunbi, M. O., Habes, I., Barawi, K., Subramanian, L., Range, M., Evans, J., Hood, K., Lührs, M., Keedwell, P., Goebel, R., & Linden, D. E. J.
Affiliations: Cardiff University (GB)
De Montfort University (GB)
Maastricht University (NL)
King's College London (GB)
Corresponding author: Linden, David E. J.
ORCID: Mehler, David M. A.
Sokunbi, Moses O.
Barawi, Kali
Evans, John
Hood, Kerenza
Lührs, Michael
Goebel, Rainer
Linden, David E. J.
Venue: Neuropsychopharmacology
Publisher: Springer Science and Business Media LLC
Published: 2018-06-23
Volume: 43
Issue: 13
Pages: 2578-2585
ISSN: 0893-133X (ISSN-L)
1740-634X
DOI: 10.1038/s41386-018-0126-5
PMC: PMC6186421
OpenAlex: W2808876118
Cited by: 175
References: 53
Citations by year: 2026: 9 · 2025: 14 · 2024: 16 · 2023: 19 · 2022: 25 · 2021: 54 · 2020: 21 · 2019: 13 · 2018: 4
FWCI: 10.35
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
Locations: Neuropsychopharmacology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Research Publications (Maastricht University) (publishedVersion, cc-by)
ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
DMU Open Research Archive (De Montfort University) (submittedVersion)
Research Publications (Maastricht University) (submittedVersion, other-oa)
Funders: National Institute for Social Care and Health Research (HS/10/25)
Health and Care Research Wales (HS/14/20)
Cancer Research UK
European Commission (602186)
Medical Research Council (MR/L010305/1)
Topics: Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords: fMRI neurofeedback, depression, emotion regulation, real-time fMRI, visual scene processing, antidepressant augmentation, self-efficacy, placebo effect, regression to the mean, self-regulation training
MeSH terms: Adult, Brain, Computer Systems, Depressive Disorder, Female, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pilot Projects, Treatment Outcome, Self Efficacy, Neurofeedback

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Targeting the affective brain—a randomized controlled trial of real-time fMRI neurofeedback in patients with depression

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