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Record W1491964194 · doi:10.1159/000371714

Neurofeedback, Self-Regulation, and Brain Imaging: Clinical Science and Fad in the Service of Mental Disorders

2015· review· en· W1491964194 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePsychotherapy and Psychosomatics · 2015
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsJewish General HospitalMcGill University
FundersCanadian Institutes of Health ResearchVolkswagen FoundationNatural Sciences and Engineering Research Council of CanadaMind and Life Institute
KeywordsNeurofeedbackPsychologyNeuroscienceSketchFunctional magnetic resonance imagingMental healthNeuroimagingPsychotherapistBrain–computer interfaceElectroencephalographyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Neurofeedback draws on multiple techniques that propel both healthy and patient populations to self-regulate neural activity. Since the 1970s, numerous accounts have promoted electroencephalography-neurofeedback as a viable treatment for a host of mental disorders. Today, while the number of health care providers referring patients to neurofeedback practitioners increases steadily, substantial methodological and conceptual caveats continue to pervade empirical reports. And yet, nascent imaging technologies (e.g., real-time functional magnetic resonance imaging) and increasingly rigorous protocols are paving the road towards more effective applications and a better scientific understanding of the underlying mechanisms. Here, we outline common neurofeedback methods, illuminate the tenuous state of the evidence, and sketch out future directions to further unravel the potential merits of this contentious therapeutic prospect.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.400
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it