Non-Invasive Brain Stimulation for Insomnia - A Review of Current Data and Future Implications
Bibliographic record
Abstract
Neuroimaging evidences point to the role of hyperarousal in the pathophysiology of insomnia. While actual treatments fail to directly target brain hyperarousal, emergent complementary therapies known as neuromodulation techniques aim to improve sleep in people with insomnia by targeting irregularities in their brain activity. In this paper, we narratively review the most relevant studies reporting the application of neuromodulation techniques to improve sleep in individuals with insomnia. Using a non-systematic approach, we retrieved relevant literature across health-related bibliographic databases. Studies were included if they specifically assessed the effects of a neuromodulation technique on sleep in a sample of patients with insomnia. Three studies on transcranial direct current stimulation (tDCS) and six studies on repetitive transcranial magnetic stimulation (rTMS) were retained. No study on transcranial alternating current stimulation (tACS) was found. Preliminary data on tDCS in a sample of individuals with insomnia shows that targeting frontal regions may have a positive impact on sleep. Findings of rTMS studies, especially 1-Hz low-frequency stimulation, suggest that it improves objective and subjective sleep in individuals with insomnia. Nonetheless, in both neuromodulation techniques, significant variability was found between stimulation parameters, study samples, and sleep outcomes. Although evidence on the impact of neuromodulation for insomnia remains scarce, recent data suggest it may have a sleep-deepening effect. Based on this review, and the limitations indicated by authors of included studies, we urge researchers to promote this field of research by testing different stimulation parameters, replicating already existing protocols, or adding standardized sleep-related outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".