ReviewReviews in the neurosciences2022
Neurofeedback and neural self-regulation: a new perspective based on allostasis.
Review in Reviews in the neurosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions.Frontiers in human neuroscience · 2024Pooled it
- Neurofeedback for functional neurological disorders: future directions for research and clinical practice.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- SMR/Theta Neurofeedback Training on Attention and Reading Abilities in Children with Attention Deficit Hyperactivity Disorder-Predominantly Inattentive.Applied psychophysiology and biofeedback · 2026Article
- Alpha power increases spontaneously during a neurofeedback session.Communications psychology · 2026Article
- Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions.Psychophysiology · 2026Article
- Effects of Electroencephalographic Biofeedback Therapy on Depression Level, Sleep Quality and Cognitive Function in Patients With Non-Demented Vascular Cognitive Impairment.Actas espanolas de psiquiatria · 2025Article
- Spontaneous Modulation of Standard EEG Frequency Bands During a Neurofeedback-Like Task.Psychophysiology · 2025Article
- γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.Military Medical Research · 2025Review
- Mindfulness-Based Interventions and the Hypothalamic-Pituitary-Adrenal Axis: A Systematic Review.Neurology international · 2024Review
- Improving Mental Skills in Precision Sports by Using Neurofeedback Training: A Narrative Review.Sports (Basel, Switzerland) · 2024Review
- The Correlation Between Cognitive Flexibility and Learning Strategies Adopted by Medical Students.Cureus · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
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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.