Régulation des symptômes de trouble de stress post-traumatique par la rétroaction neurologique : rétablir le contrôle mental
Bibliographic record
Abstract
Neurofeedback is emerging as a psychophysiological treatment where self-regulation is achieved through online feedback of neural states. Novel personalized medicine approaches are particularly important for the treatment of posttraumatic stress disorder (PTSD), as symptom presentation of the disorder, as well as responses to treatment, are highly heterogeneous. Learning to achieve control of specific neural substrates through neurofeedback has been shown to display therapeutic evidence in patients with a wide variety of psychiatric disorders, including PTSD. This article outlines the neural mechanisms underlying neurofeedback and examines converging evidence for the efficacy of neurofeedback as an adjunctive treatment for PTSD via both electroencephalography (EEG) and real-time functional magnetic resonance imaging (fMRI) modalities. Further, implications for the treatment of PTSD via neurofeedback in the military member and Veteran population is examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".