Examining alterations in subjective sleep ratings in individuals with major depressive disorder receiving daily theta burst stimulation
Bibliographic record
Abstract
Background Major depressive disorder is often accompanied by sleep disturbances, which have been found to influence response to antidepressant treatments. Repetitive transcranial magnetic stimulation (rTMS), including novel optimized protocols like theta burst stimulation (TBS), is an effective intervention for treatment-resistant depression, although little is known about the relationship between sleep and the antidepressant effects of this treatment. Methods Sixty-six individuals with treatment-resistant depression received 4 to 6 weeks of daily TBS treatments targeting the left-unilateral or bilateral dorsolateral prefrontal cortex (DLPFC). Depression severity was measured using the Hamilton Rating Scale for Depression (HRSD-17) and subjective sleep using the Leeds Sleep Evaluation Questionnaire (LSEQ). Data was analysed with linear mixed models and Spearman correlations. Results TBS significantly reduced HRSD-17 scores and improved LSEQ subscales reflecting sleep quality, ease of awakening from sleep, and behaviour following wakefulness. Improvements in symptoms of depression were associated with improvement in behaviour following waking after 20 and 30 TBS sessions, but not with sleep quality. Limitations Limitations include a limited sample size, lack of sham condition, subjective measures of sleep and variable number of treatments (20 or 30 TBS sessions). Conclusions These findings suggest that TBS treatments concurrently improve subjective sleep quality and depression symptoms. Additionally, changes in depression more closely aligned with changes in sleep-related daytime functioning than with sleep quality per se. Further work is required to delineate how sleep improvements following neuromodulation may contribute to the antidepressant response.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".