ArticleNeuroscience2018
When the Brain Takes 'BOLD' Steps: Real-Time fMRI Neurofeedback Can Further Enhance the Ability to Gradually Self-regulate Regional Brain Activation.
Article in Neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?Human brain mapping · 2020Pooled it
- A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review.Frontiers in human neuroscience · 2020Pooled it
- Trial
- Basal ganglia as an fMRI motor neurofeedback target in Parkinson's disease.Applied psychophysiology and biofeedback · 2025Article
- Online self-evaluation of fMRI-based neurofeedback performance.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression.Frontiers in psychiatry · 2023Article
- Neurofeedback and neural self-regulation: a new perspective based on allostasis.Reviews in the neurosciences · 2022Review
- Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review.Human brain mapping · 2020Review
- Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).Brain : a journal of neurology · 2020Article
- Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.Frontiers in neuroscience · 2020Article
- A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients.Frontiers in human neuroscience · 2020Article
- Graded fMRI Neurofeedback Training of Motor Imagery in Middle Cerebral Artery Stroke Patients: A Preregistered Proof-of-Concept Study.Frontiers in human neuroscience · 2020Article
- Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback.Frontiers in human neuroscience · 2020Article
- A Novel Brain-Computer Interface Virtual Environment for Neurofeedback During Functional MRI.Frontiers in neuroscience · 2020Article
- Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback.Brain connectivity · 2019Article
- Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum.Brain and behavior · 2019Article
- Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies.NeuroImage · 2019Article
- The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback.NeuroImage · 2019Article
- Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing.Frontiers in human neuroscience · 2019Article
- Existence of Initial Dip for BCI: An Illusion or Reality.Frontiers in neurorobotics · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i.e., to allow the BCI user to choose from as many options as possible. Recently, the ability to voluntarily modulate spatial and/or temporal blood oxygenation level-dependent (BOLD)-signal features has been explored implementing different mental tasks and/or different encoding time intervals, respectively. Our two-session fMRI feasibility study systematically investigated for the first time the possibility of using magnitudinal BOLD-signal features for intention encoding. Particularly, in our novel paradigm, participants (n=10) were asked to alternately self-regulate their regional brain-activation level to 30%, 60% or 90% of their maximal capacity by applying a selected activation strategy (i.e., performing a mental task, e.g., inner speech) and modulation strategies (e.g., using different speech rates) suggested by the experimenters. In a second step, we tested the hypothesis that the additional availability of feedback information on the current BOLD-signal level within a region of interest improves the gradual-self regulation performance. Therefore, participants were provided with neurofeedback in one of the two fMRI sessions. Our results show that the majority of the participants were able to gradually self-regulate regional brain activation to at least two different target levels even in the absence of neurofeedback. When provided with continuous feedback on their current BOLD-signal level, most participants further enhanced their gradual self-regulation ability. Our findings were observed across a wide variety of mental tasks and across clinical MR field strengths (i.e., at 1.5T and 3T), indicating that these findings are robust and can be generalized across mental tasks and scanner types. The suggested novel parametric activation paradigm enriches the spectrum of current rtfMRI-neurofeedback and BCI methodology and has considerable potential for fundamental and clinical neuroscience applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.