Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: how is brain regulation mediated?
Open accessAbstract
An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success.
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TL;DR
This individual-participant-data meta-analysis of 12 real-time fMRI neurofeedback studies found that anterior insula and basal ganglia are consistently active during brain self-regulation across different target regions. The authors conclude that some common target regions, notably insula and ACC, may also be involved in the regulation process itself, creating a potential confound.
Key findings
- Across all 12 studies (175 subjects, 899 neurofeedback runs, nine target regions), the third-level mixed-effects GLM showed consistent activation in the bilateral anterior insula and basal ganglia during neurofeedback.
- In the 8-study subsample with larger overlapping field of view (103 subjects), additional significant activations included posterior ACC, bilateral vlPFC, bilateral dlPFC extending to premotor cortex, temporo-parietal areas, and lateral occipital/visual association areas including temporo-occipital junction; deactivations included PCC, precuneus, and bilateral transverse temporal area.
- The authors conclude that a regulation network including anterior insula, basal ganglia, ACC, dlPFC, vlPFC, temporo-parietal area, and visual association areas is consistently recruited during self-regulation independent of the targeted region.
- They caution that if rt-fMRI neurofeedback targets regions within this regulation network, such as anterior insula, it is difficult to distinguish activation changes related to successful regulation from those related to the regulation process per se.
Methods
- Study selection used a Web of Knowledge search in January 2014 plus community literature updates in August 2015, yielding 316 publications; inclusion criteria were rt-fMRI neurofeedback, 1.5 or 3.0 T, at least four healthy participants, and at least three neurofeedback runs. Twenty-eight studies met criteria, and 12 corresponding authors provided raw data.
- Included studies targeted nine ROIs: insula (5), amygdala (2), primary motor cortex (1), premotor cortex (1), auditory cortex (1), visual cortex (1), ACC (1), substantia nigra/ventral tegmental area (1), and vlPFC (1), totaling 175 subjects and 899 runs.
- MRI analysis used FSL 5.0.6 with standard preprocessing (motion correction, co-registration, MNI normalization, 5 mm smoothing); first-level modeled each study’s block design, second-level combined runs per subject in fixed effects, and third-level used FLAME1 mixed effects across all subjects with study coregressors. The analysis was repeated in an 8-study subsample with larger overlapping FoV; results were FWE-corrected at p < 0.05.
Limitations
- The paper states there is no gold standard for measuring regulation success, so it could not unambiguously define a universal regulation success parameter across studies; it also notes limited FoV due to individual slice positioning, inclusion of only visual-feedback studies, possible learning confounds, and a relatively low number of included studies (8 for large FoV, 12 for small FoV).
- The paper states the analysis is retrospective and the original studies were not optimized for IPD meta-analysis, with considerable variation in acquisition parameters and paradigms (blocks, runs, sessions, up/down regulation, stimuli, instructions).
- The text makes evident that self-regulation cannot be distinguished from feedback processing and learning because the current study does not include control runs without feedback (“transfer runs”); it also notes that up- and down-regulation studies were combined under the assumption that the regulation network is active regardless of direction.
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Claims the paper makes
- Findingreasoning
Data acquisition parameters and paradigm (blocks, runs, sessions, up or down regulation, stimuli, instructions) vary considerably across studies.
“Therefore, data acquisition parameters and paradigm (blocks, runs, sessions, up or down regulation, stimuli, instructions) vary considerably across studies.”
Quote in the paperSubstantial claim - Findingown data
Brain self-regulation during rt-fMRI neurofeedback involves a complex regulation network, including notably AIC, BG and the ACC.
“Brain self-regulation during rt-fMRI neurofeedback involves a complex regulation network, including notably AIC, BG and the ACC.”
Quote in the paperSubstantial claim - Findingown data
Across the included real-time fMRI neurofeedback studies, the anterior insula and the basal ganglia, notably the striatum, were consistently active during regulation of brain activation.
“The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies.”
- Also stated in: § Results · plain
“The third level mixed effects analysis of all 12 studies yielded two main regions that are consistently activated during neurofeedback: the bilateral anterior insula and the basal ganglia.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Interpretationreasoning
Some target regions of rt-fMRI neurofeedback studies (notably insula and ACC) are also implicated in the process of regulation per se.
“our results suggest that some target regions of rt-fMRI neurofeedback studies (notably insula and ACC) are also implicated in the process of regulation per se.”
Quote in the paperSubstantial claim - Interpretationreasoning
The implication that insula and ACC are implicated in regulation per se may represent a potential confound for the regulation of these areas.
“This may therefore represent a potential confound for the regulation of these areas.”
Quote in the paperSubstantial claim
- May combine two claims. 4
- Classified as probably not a substantial claim. 4
May combine two claims.
Findingown dataIn the subsample of 8 studies with a larger overlapping field of view, additional significant activations during neurofeedback included the posterior ACC, bilateral ventrolateral prefrontal cortex, bilateral dorsolateral prefrontal cortex extending to premotor cortex, a large bilateral temporo-parietal area, and lateral occipital areas including visual association areas and the temporo-occipital
“Considering the subsample analysis with a larger field of view (n = 8 studies) additional significant areas include the posterior ACC (pACC), the bilateral ventrolateral prefrontal cortex (vlPFC) and an area in the bilateral dorsolateral prefrontal cortex (dlPFC) extending to the premotor cortex (PMC), a large temporo-parietal area bilaterally, and lateral occipital areas including visual ass”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenThe self-regulation network includes the ACC, dorsolateral PFC extending to PMC, ventrolateral PFC, anterior insula, basal ganglia and thalamus, inferior and superior parietal lobule extending to temporo-parietal junction, and lateral occipital cortex extending to temporo-occipital junction.
“This network includes the ACC (yellow), the dorsolateral PFC extending to PMC (dark green), the ventrolateral PFC (light green), the anterior insula (red), the basal ganglia and thalamus (orange), part of the inferior and superior parietal lobule extending to the temporo-parietal junction (violet) and the lateral occipital cortex extending to the temporo-occipital junction (blue).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe presented findings may be somewhat limited by the relatively low number of studies included (8 for large FoV, 12 for small FoV).
“The presented findings may be somewhat limited by the relatively low number of studies included (8 for large FoV, 12 for small FoV).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningThe variability across studies can be considered a strength because it indicates the general validity of our results, as the data covers a range of different experimental setups and designs.
“On the other hand, this can also be considered as strength as it indicates the general validity of our results as the data covers a range of different experimental setups and designs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Study design and conductnone givenUnifying the analysis steps using original data instead of comparing activation clusters reported in the literature should enhance the transparency and thus interpretability of results.
“On the other hand the procedure of unifying the analysis steps using original data instead of comparing activation clusters reported in the literature should enhance the transparency and thus interpretability of results.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenThis analysis is retrospective and the design of the studies was not optimized for the IPD meta-analysis.
“In addition, this analysis is retrospective and the design of the studies was not optimized for the IPD meta-analysis.”
- Also stated in: § Conclusion · plain
“Taking into account the limitation that the current investigation is a retrospective IPD meta-analysis of rt-fMRI studies, which were not specifically designed for this purpose”
- Also stated in: § Outlook · plain
“As this was a post-hoc analysis using studies that were designed independently, not all interesting scientific questions could be answered using these data.”
Quote in the paperSubstantial claim- Also stated in: § Conclusion · plain
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?
By learning to self-upregulate isolated regions, participants learn to co-regulate other areas interconnected with the target region, suggesting modulation of entire networks.
“Importantly, by learning to self-upregulate isolated regions, participants learn to co-regulate other areas interconnected with the target region, suggesting modulation of entire networks (Emmert et al., 2016; Thibault et al., 2016).”
From Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation (2018) · cites this paper as Emmert et al., 2016
The Discussion and Conclusion (page numbers unavailable) identify multiple brain regions active during neurofeedback self-regulation, supporting the broader network point. But the Introduction says the analysis cannot distinguish learning from other neurofeedback processes, and the paper does not establish that participants learn to co-regulate areas interconnected with a particular target. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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Self-citationReal-time fMRI-based neurofeedback reinforces causality of attention networks
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The Obese Brain Athlete: Self-Regulation of the Anterior Insula in Adiposity
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Self-citationNormal functional imaging of the basal ganglia
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Closed-loop brain training: the science of neurofeedback
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“Importantly, by learning to self-upregulate isolated regions, participants learn to co-regulate other areas interconnected with the target region, suggesting modulation of entire networks (Emmert et al., 2016; Thibault et al., 2016).”
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Self-citation
- Authors
- Emmert, K., Kopel, R., Sulzer, J., Brühl, A. B., Berman, B. D., Linden, D. E. J., Horovitz, S. G., Breimhorst, M., Caria, A., Frank, S., Johnston, S., Long, Z., Paret, C., Robineau, F., Veit, R., Bartsch, A., Beckmann, C. F., Van De Ville, D., & Haller, S.
- Affiliations
- University of Geneva (CH)
- The University of Texas at Austin (US)
- University of Zurich (CH)
- University of Colorado Anschutz (US)
- Cardiff University (GB)
- National Institutes of Health (US)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- University of Tübingen (DE)
- Swansea University (GB)
- Beijing Normal University (CN)
- Heidelberg University (DE)
- Radboud University Nijmegen (NL)
- Corresponding author
- Emmert, Kirsten
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 124
- Issue
- Pt A
- Pages
- 806-812
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1778650359
- Semantic Scholar
- b0b5dd2a4a854d9d27d554a39b21a25c747f8e04
- Cited by
- 245
- References
- 77
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 22 · 2023: 15 · 2022: 23 · 2021: 47 · 2020: 26 · 2019: 27 · 2018: 24 · 2017: 21 · 2016: 14
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, other-oa)
- Site cant be reached (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (320030_127079/1, 320030_147126\1, 147126, 145442, 162223)
- Centre d'Imagerie BioMédicale
- Medical Research Council (MR/L010305/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- real-time fMRI neurofeedback, self-regulation of brain activity, anterior insula, basal ganglia, striatum, anterior cingulate cortex, prefrontal cortex, temporoparietal junction, visual association areas, interoception, cognitive control
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
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