Infralow neurofeedback in the treatment of substance use disorders: a randomized controlled trial
Bibliographic record
Abstract
Background Infralow neurofeedback (ILF-NF) was recently developed as a subtype of traditional, frequency-based neurofeedback that targets cerebral rhythmic activity below 0.5 Hz and improves brain self-regulation. The efficacy of ILF-NF in the treatment of substance use disorder has not yet been evaluated, but clinical evidence suggests that it may prevent relapse by improving functioning in various life domains. The current study aimed to fill this research gap and extend empirical evidence related to this issue. Methods Ninety-three patients with substance use disorders at an outpatient unit in Norway were randomized to receive 20 sessions (30 minutes each) of ILF-NF training combined with treatment as usual (TAU), or TAU alone. The primary outcome was quality of life post-treatment as an overall measure of functioning. We analyzed between-group differences using Student t tests. Results We found no significant differences in quality of life between groups. We found similar nonsignificant results for most of the secondary outcome measures, including drug use, sleep, anxiety and depression. Compared to TAU, the ILF-NF + TAU group reported significantly lower restlessness scores post-treatment (mean difference −1.8, 95 % confidence interval −3.1 to −0.5; p = 0.006). Limitations: This study was limited by broad inclusion criteria and a lack of placebo control (sham neurofeedback treatment). Conclusion ILF-NF offered limited additional benefit when combined with TAU, except in the area of restlessness. Future studies could further investigate the relationship between ILF-NF, restlessness and substance use in targeted subpopulations to illuminate relapse mechanisms. Clinical trial registration ClinicalTrials.gov : NCT03356210
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".