Single session real-time fMRI neurofeedback has a lasting impact on cognitive behavioral therapy strategies
Open accessPublished version of a preprint
Abstract
To benefit from cognitive behavioral therapy (CBT), individuals must not only learn new skills but also strategically implement them outside of session. Here, we tested a novel technique for personalizing CBT skills and facilitating their generalization to daily life. We hypothesized that showing participants the impact of specific CBT strategies on their own brain function using real-time functional magnetic imaging (rt-fMRI) neurofeedback would increase their metacognitive awareness, help them identify effective strategies, and motivate real-world use. In a within-subjects design, participants who had completed a clinical trial of a standardized course of CBT created a personal repertoire of negative autobiographical stimuli and mood regulation strategies. From each participant's repertoire, a set of experimental and control strategies were identified; only experimental strategies were practiced in the scanner. During the rt-fMRI neurofeedback session, participants used negative stimuli and strategies from their repertoire to manipulate activation in the anterior cingulate cortex, a region implicated in emotional distress. The primary outcome measures were changes in participant ratings of strategy difficulty, efficacy, and frequency of use. As predicted, ratings for unscanned control strategies were stable across observations, whereas ratings for experimental strategies changed after neurofeedback. At follow-up one month after the session, efficacy and frequency ratings for scanned strategies were predicted by neurofeedback during the rt-fMRI session. These results suggest that rt-fMRI neurofeedback created a salient and durable learning experience for patients, extending beyond the scan session to guide and motivate CBT skill use weeks later. This metacognitive approach to neurofeedback offers a promising model for increasing clinical benefits from cognitive behavioral therapy by personalizing skills and facilitating generalization.
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
Cites
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?
Rt-fMRI NF trainings have been shown to support perceived control and self-efficacy, thus potentially motivating behavioral change.
“Rt-fMRI NF trainings have been shown to support perceived control (Zweerings et al., 2018) and self-efficacy (Mehler et al., 2018), thus potentially motivating behavioral change (MacDuffie et al., 2018).”
From Rt-fMRI neurofeedback-guided cognitive reappraisal training modulates amygdala responsivity in posttraumatic stress disorder (2020) · cites this paper as MacDuffie et al., 2018
Presenting participants with information on the effects of strategies on brain activation may increase credibility and motivate application in daily life.
“Presenting participants with information on the effects of strategies on brain activation may increase credibility and motivate application in daily life (MacDuffie et al., 2018).”
From Rt-fMRI neurofeedback-guided cognitive reappraisal training modulates amygdala responsivity in posttraumatic stress disorder (2020) · cites this paper as MacDuffie et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Maldjian, J., Laurienti, P., Kraft, R., & Burdette, J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00169-1 · Cited by 5,453 · see citing works
Sufficient Sample Sizes for Multilevel Modeling
Maas, C. J. M., & Hox, J. J. (2005) · Methodology · doi:10.1027/1614-2241.1.3.86 · Cited by 3,582 · see citing works
Mediators and Mechanisms of Change in Psychotherapy Research
Kazdin, A. E. (2007) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091432 · Cited by 2,504 · cited 1× · see citing works
“Efficacious psychotherapies convey clinical benefit in part through learning, often relying on the appropriate application of new skills (Kazdin, 2007).”
The Seductive Allure of Neuroscience Explanations
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20040 · Cited by 1,086 · see citing works
Seeing is believing: The effect of brain images on judgments of scientific reasoning
McCabe, D. P., & Castel, A. D. (2007) · Cognition · doi:10.1016/j.cognition.2007.07.017 · Cited by 652 · see citing works
Real-time fMRI neurofeedback: Progress and challenges
Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., Bruehl, A. B., Cohen, L. G., deCharms, R. C., Gassert, R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.033 · Cited by 453 · see citing works
Real-Time Self-Regulation of Emotion Networks in Patients with Depression
Linden, D. E. J., Isabelle, H., Johnston, S. J., Linden, S. C., Ranjit, T., Subramanian, L., Sorger, B., Healy, D., & Goebel, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038115 · Cited by 438 · see citing works
Young, K. D., Zotev, V. S., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., & Bodurka, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088785 · Cited by 286 · see citing works
Karlin, B. E., & Cross, G. (2014) · American Psychologist · doi:10.1037/a0033888 · Cited by 283 · see citing works
Modulation of subgenual anterior cingulate cortex activity with real‐time neurofeedback
Hamilton, J. P., Glover, G. H., Hsu, J., Johnson, R. F., & Gotlib, I. H. (2010) · Human Brain Mapping · doi:10.1002/hbm.20997 · Cited by 172 · see citing works
The Chemical Imbalance Explanation of Depression: Reducing Blame at What Cost?
Deacon, B. J., & Baird, G. L. (2009) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.2009.28.4.415 · Cited by 159 · see citing works
Hundt, N. E., Mignogna, J., Underhill, C., & Cully, J. A. (2012) · Behavior Therapy · doi:10.1016/j.beth.2012.10.001 · Cited by 143 · see citing works
Superfluous Neuroscience Information Makes Explanations of Psychological Phenomena More Appealing
Fernandez‐Duque, D., Evans, J., Christian, C. B., & Hodges, S. D. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00750 · Cited by 136 · see citing works
The Seductive Allure of Neuroscience Explanations
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20.3.470 · Cited by 86 · see citing works
Hoet, A. C., Burgin, C. J., Eddington, K. M., & Silvia, P. J. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-017-9852-y · Cited by 34 · see citing works
Eddington, K. M., Silvia, P. J., Foxworth, T. E., Hoet, A. C., & Kwapil, T. R. (2015) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0039058 · Cited by 25 · see citing works
Eddington, K. M., Burgin, C. J., Silvia, P. J., Fallah, N., Majestic, C. M., & Kwapil, T. R. (2016) · Cognitive Therapy and Research · doi:10.1007/s10608-016-9816-7 · Cited by 22 · see citing works
Understanding Our Own Biology: The Relevance of Auto-Biological Attributions for Mental Health
MacDuffie, K. E., & Strauman, T. J. (2017) · Clinical Psychology Science and Practice · doi:10.1111/cpsp.12188 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
fMRI neurofeedback in emotion regulation: A literature review
Linhartová, P., Látalová, A., Kóša, B., Kašpárek, T., Schmahl, C. G., & Paret, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.011 · Cited by 162 · see citing works
Soleimani, G., Nitsche, M. A., Bergmann, T. O., Towhidkhah, F., Violante, I. R., Lorenz, R., Kuplicki, R., Tsuchiyagaito, A., Mulyana, B. E., Mayeli, A., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02565-5 · Cited by 126 · see citing works
Attentional bias in depression: understanding mechanisms to improve training and treatment
Mennen, A. C., Norman, K. A., & Turk‐Browne, N. B. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.07.036 · Cited by 123 · see citing works
Paret, C., Goldway, N., Zich, C., Keynan, J. N., Hendler, T., Linden, D. E. J., & Kadosh, K. C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116107 · Cited by 118 · see citing works
Neurofeedback through the lens of reinforcement learning
Lubianiker, N., Paret, C., Dayan, P., & Hendler, T. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.03.008 · Cited by 73 · see citing works
A systematic review of fMRI neurofeedback reporting and effects in clinical populations
Tursic, A., Eck, J., Lührs, M., Linden, D. E. J., & Goebel, R. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102496 · Cited by 70 · see citing works
Trambaiolli, L. R., Kohl, S. H., Linden, D. E. J., & Mehler, D. M. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.015 · Cited by 72 · see citing works
Fede, S. J., Dean, S., Manuweera, T., & Momenan, R. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00060 · Cited by 70 · see citing works
Мельников, М. Е. (2021) · Neural Plasticity · doi:10.1155/2021/8878857 · Cited by 67 · see citing works
Khorev, V. S., Kurkin, S., Badarin, A., Antipov, V. M., Николаевна, П. Е., Andreev, A. V., Grubov, V., Drapkina, O. M., Kiselev, A. R., & Hramov, A. E. (2024) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2307125 · Cited by 54 · see citing works
Zweerings, J., Sarkheil, P., Keller, M., Dyck, M., Klasen, M., Becker, B., Gaebler, A. J., Ibrahim, C. N., Turetsky, B. I., Zvyagintsev, M., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102483 · Cited by 45 · see citing works
“Rt-fMRI NF trainings have been shown to support perceived control (Zweerings et al., 2018) and selfefficacy (Mehler et al., 2018), thus potentially motivating behavioral change (MacDuffie et al., 2018).”
Kirschner, M., Sladky, R., Haugg, A., Stämpfli, P., Jehli, E., Hodel, M., Engeli, E., Hösli, S., Baumgartner, M. R., Sulzer, J. S., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.10.052 · Cited by 50 · see citing works
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges
Dennison, J. B., Sazhin, D., & Smith, D. V. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1589 · Cited by 44 · see citing works
Keller, M., Zweerings, J., Klasen, M., Zvyagintsev, M., Iglesias, J., Mendoza-Quiñones, R., & Mathiak, K. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.715898 · Cited by 33 · see citing works
Mennen, A. C., Turk‐Browne, N. B., Wallace, G., Seok, D., Jaganjac, A., Stock, J., deBettencourt, M. T., Cohen, J. D., Norman, K. A., & Sheline, Y. I. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.10.006 · Cited by 25 · see citing works
Enhancing the Effects of Neurofeedback Training: The Motivational Value of the Reinforcers
Pérez-Elvira, R., Oltra‐Cucarella, J., Carrobles, J. A., Moltó, J., Flórez, M., Parra, S., Agudo, M., Saez, C., Guarino, S., Costea, R. M., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11040457 · Cited by 25 · see citing works
Enriching CBT by Neuroscience: Novel Avenues to Achieve Personalized Treatments
Månsson, K. N. T., Lueken, U., & Frick, A. (2020) · International Journal of Cognitive Therapy · doi:10.1007/s41811-020-00089-0 · Cited by 21 · see citing works
Xia, Z., Yang, P., Chen, S., Zhou, H., & Yan, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae252 · Cited by 16 · see citing works
Bloom, P. A., Pagliaccio, D., Zhang, J., Bauer, C. C. C., Kyler, M., Greene, K. D., Treves, I. N., Morfini, F., Durham, K., Cherner, R., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05223-8 · Cited by 16 · see citing works
Feasibility of training the dorsolateral prefrontal-striatal network by real-time fMRI neurofeedback
Weiß, F., Zhang, J., Aslan, A., Kirsch, P., & Gerchen, M. F. (2022) · Scientific Reports · doi:10.1038/s41598-022-05675-0 · Cited by 15 · see citing works
Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review
Orth, L., Meeh, J., Gur, R. C., Neuner, I., & Sarkheil, P. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.933718 · Cited by 14 · see citing works
Watve, A., Haugg, A., Frei, N., Koush, Y., Willinger, D., Brühl, A. B., Stämpfli, P., Scharnowski, F., & Sladky, R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1286665 · Cited by 14 · see citing works
Amygdala self-neuromodulation capacity as a window for process-related network recruitment
Gurevitch, G., Lubianiker, N., Markovits, T., Or‐Borichev, A., Sharon, H., Fine, N. B., Fruchtman-Steinbok, T., Keynan, J. N., Shahar, M., Friedman, A., et al. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2024.0186 · Cited by 12 · see citing works
Ahrweiler, N., Santana‐Gonzalez, C., Zhang, N., Quandt, G., Ashtiani, N., Liu, G., Engstrom, M., Schultz, E., Liengswangwong, R., Teoh, J. Y., et al. (2022) · Brain Sciences · doi:10.3390/brainsci12091128 · Cited by 13 · see citing works
Compère, L., Siegle, G. J., & Young, K. D. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01507-3 · Cited by 12 · see citing works
Sculpting new visual categories into the human brain
Iordan, C. R., Ritvo, V. J. H., Norman, K. A., Turk‐Browne, N. B., & Cohen, J. D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410445121 · Cited by 11 · see citing works
Pyneal: Open Source Real-Time fMRI Software
MacInnes, J., Adcock, R. A., Stocco, A., Prat, C. S., Rao, R. P. N., & Dickerson, K. C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00900 · Cited by 10 · see citing works
Kerr, K. L., Ratliff, E. L., Cohen, Z. P., Fuller, S., Cosgrove, K. T., DeVille, D. C., Misaki, M., Morris, A., & Bodurka, J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.910951 · Cited by 6 · see citing works
Müller, E., Loukas, S., Häuselmann, S., Concetti, C., Ville, D. V. D., Gninenko, N., & Aybek, S. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103899 · Cited by 7 · see citing works
Predictors of neurofeedback treatment outcome in binge‐eating disorder : An exploratory study
Rösch, S. A., Schmidt, R., & Hilbert, A. (2023) · International Journal of Eating Disorders · doi:10.1002/eat.24062 · Cited by 7 · see citing works
Zeng, L., Wang, C., Sun, K., Pu, Y., Gao, Y., Wang, H., Liu, X., & Wen, Z. (2023) · Brain Sciences · doi:10.3390/brainsci13111516 · Cited by 6 · see citing works
Morrissey, G., Tsuchiyagaito, A., Takahashi, T., McMillin, J., Aupperle, R. L., Misaki, M., & Khalsa, S. S. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105680 · Cited by 6 · see citing works
Clinical Application of Real-Time fMRI-Based Neurofeedback for Depression
Mathiak, K., & Keller, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-981-33-6044-0_15 · Cited by 5 · see citing works
Training volitional control of the theory of mind network with real-time fMRI neurofeedback
Saxena, A., Shovestul, B. J., Dudek, E. M., Reda, S., Venkataraman, A., Lamberti, J. S., & Dodell‐Feder, D. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120334 · Cited by 4 · see citing works
Zeng, L., Gai, L., Sun, K., Yuan, Y., Gao, Y., Wang, H., Wang, X., & Wen, Z. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1513304 · Cited by 4 · see citing works
An Overview of Machine Learning Applications in Mood Disorders
Topolski, N., Jeong, S. H., & Mwangi, B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.017 · Cited by 4 · see citing works
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018 · Cited by 3 · see citing works
Belghadr, H., Alipisheh, F., Esmaeilnejad, M., & Zandi, H. (2025) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-025-09731-z · Cited by 2 · see citing works
Basal ganglia as an fMRI motor neurofeedback target in Parkinson’s disease
Baqapuri, H. I., Terneusen, A., Lührs, M., Peters, J. C., Kuijf, M. L., Goebel, R., Linden, D. E. J., Neuripides Study Group, Lozano, A. M., Mana, J., et al. (2025) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-025-09747-5 · Cited by 2 · see citing works
Neuroimaging Studies of Effects of Psychotherapy in Depression
Bauer, I. E., & Meyer, T. D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.021 · Cited by 2 · see citing works
Electrophysiological Biomarkers for Mood Disorders
Ramakrishnan, N., Murphy, N., Selvaraj, S., & Cho, R. Y. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.015 · Cited by 2 · see citing works
Tsang, M. H. L., Chen, J. C., Jiang, H., & Becker, B. (2025) · medRxiv · doi:10.1101/2025.07.29.25332351 · Cited by 2 · see citing works
Gninenko, N., Senn, P., Haller, S., & Ville, D. V. D. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103960 · Cited by 1 · see citing works
Brain Imaging and the Mechanisms of Antidepressant Action
Godlewska, B. R., Selvaraj, S., & Cowen, P. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.020 · Cited by 1 · see citing works
Effects of Lithium on Brain Structure in Bipolar Disorder
Kaur, J., Kafantaris, V., & Szeszko, P. R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.018 · Cited by 1 · see citing works
Functional Connectome in Bipolar Disorder
Cha, J., & Anand, A. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.008 · Cited by 1 · see citing works
Closed-loop fMRI neurofeedback to reduce negative attentional bias in depressionPreprint
Mennen, A. C., Turk‐Browne, N. B., Wallace, G., Seok, D., Jaganjac, A., Stock, J., deBettencourt, M. T., Cohen, J. D., Norman, K. A., & Sheline, Y. I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.07.137943 · Cited by 1 · see citing works
Imaging Genetic and Epigenetic Markers in Mood Disorders
Poletti, S., Mazza, E. A., Vai, B., & Benedetti, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.012
Brain Imaging of Reward Dysfunction in Unipolar and Bipolar Disorders
Kumar, P., Jiang, Y., & Whitton, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.006
Resting-State Functional Connectivity in Unipolar Depression
Chen, Z., & Gong, Q. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.007
Brain Imaging Methods in Mood Disorders
Selvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.002
Kious, B. M., & Renshaw, P. F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.009
Neuroimaging Brain Inflammation in Mood Disorders
Meyer, J. H. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.011
Functional Near-Infrared Spectroscopy Studies in Mood Disorders
Matsuo, K., & Matsubara, T. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.014
Neuroanatomical Findings in Unipolar Depression and the Role of the Hippocampus
Arnone, D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.003
Neuroimaging Biomarkers in Pediatric Mood Disorders
West, M. P., Tang, W. J., & Singh, M. K. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.005
fMRI Neurofeedback as Treatment for Depression
Skottnik, L., & Linden, D. E. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.013
Neuroanatomical Findings in Bipolar Disorder
Tronchin, G., & McDonald, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.004
Imaging Glutamatergic and GABAergic Abnormalities in Mood Disorders
Scotti-Muzzi, E., Otaduy, M. C. G., Soeiro‐de‐Souza, M. G., & Machado‐Vieira, R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.010
Molecular Imaging of Dopamine and Antipsychotics in Bipolar Disorder
Jauhar, S. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.019
Magnetoencephalography Studies in Mood Disorders
Nugent, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.016
Jerichow, M. M., & Watson, L. A. (2021) · Psyke & Logos · doi:10.7146/pl.v42i1.128775
Using fMRI neurofeedback to interrogate emotion, motivation, and social neurocognition
Dickerson, K. C., & Adcock, R. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822421-2.00001-6
Selvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.022
Selvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.001
Rasmijn, A. (2022) · doi:10.26481/dis.20220315ar
Bloom, P. A., Pagliaccio, D., Zhang, J., Bauer, C. C. C., Kyler, M., Greene, K. D., Treves, I. N., Morfini, F., Durham, K., Cherner, R., et al. (2023) · doi:10.31234/osf.io/v3fqx
Block, S. R., Schroder, H. S., Webster, C. T., Perricone, A. M., & Moser, J. S. (2026) · Neuroethics · doi:10.1007/s12152-026-09634-8
Halli, P., Weiß, F., Gerhardt, S., Zhang, J., Sommer, W. H., Kiefer, F., Kirsch, P., & Gerchen, M. F. (2026) · medRxiv · doi:10.64898/2026.05.29.26354435
Fu, R., Iglesias-Fuster, J., Zweerings, J., Keller, M., Kühne, S., Valdés-Sosa, M. J., Cong, F., & Mathiak, K. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122119
Tsang, M. H. L., Chen, J. C., Jiang, H., & Becker, B. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111918
Morris, L. S., Beltran, J., Kvamme, T. L., Chowdhury, A., Butler, G., Adams, A., Patel, T., Obie, E., Fontaine, S., Corniquel, M. B., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03911-x
Jindrová, M., Santangelo, P., Kleindienst, N., Schmahl, C. G., & Paret, C. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08767-7
- Authors
- MacDuffie, K. E., MacInnes, J., Dickerson, K. C., Eddington, K. M., Strauman, T. J., & Adcock, R. A.
- Affiliations
- University of Washington (US)
- Duke University (US)
- University of North Carolina at Greensboro (US)
- Corresponding author
- Adcock, R. Alison
- Venue
- NeuroImage Clinical
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 19
- Pages
- 868-875
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC6005804
- OpenAlex
- W2950669198
- Cited by
- 74
- References
- 18
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 31 · 2020: 8 · 2019: 3 · 2018: 1
- FWCI
- 14.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Center for Advancing Translational Sciences
- National Institute of Mental Health (R01MH094743)
- National Science Foundation
- National Institutes of Health (5KL2TR001115)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- real-time fMRI neurofeedback, cognitive behavioral therapy, metacognitive awareness, strategy generalization, anterior cingulate cortex, emotion regulation, personalized treatment, medication adherence
- MeSH terms
- Adult, Brain, Depressive Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Cognitive Behavioral Therapy, Treatment Outcome, Neurofeedback
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
