ArticleScientific data2021
The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Schizophrenia, variability, and the Anna Karenina principle.Psychiatry research. Neuroimaging · 2026Article
- Synthetic image evolution for affective science.Psychiatry and clinical neurosciences · 2026Article
- Schizophrenia, variability, and the Anna Karenina principle.bioRxiv : the preprint server for biology · 2025Article
- Neural Mechanisms of Feedback Processing and Regulation Recalibration During Neurofeedback Training.Human brain mapping · 2025Article
- Neurorights in the Constitution: from neurotechnology to ethics and politics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback.Journal of electrical bioimpedance · 2024Article
- Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis.Frontiers in psychology · 2024Article
- Laboratory models of post-traumatic stress disorder: The elusive bridge to translation.Neuron · 2022Review
- From internal models toward metacognitive AI.Biological cybernetics · 2021Article
- Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges.Frontiers in neuroergonomics · 2021Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.
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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.