Sleep Spindles Predict Response to Cognitive \nBehavioral Therapy for Chronic Insomnia
Bibliographic record
Abstract
Cognitive behavioral therapy (CBT-I) is a common and effective method for treating chronic insomnia, although patient responses to it are not uniform and research on predicting treatment response has mostly focused on psychological factors. Here, it is investigated whether brain oscillations during sleep at baseline, particularly sleep spindles, are predictive of treatment response. Twenty-four participants with chronic primary insomnia took part in a 6-week CBT-I performed in groups of 4 to 6 participants. Treatment response to CBT-I was assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI) measured at pre- and post-treatment. Secondary outcome measures included sleep diary (over seven days) and polysomnography (PSG) sleep efficiency (%) measured at pre- and post-treatment. Spindle density (as well as secondary measures of duration, amplitude, power, frequency, and spectral power in the sigma band) during stages N2-N3 sleep were extracted from the PSG recording at pre-treatment. Multiple regression assessed whether sleep spindle activity predicted treatment response to CBT-I. After controlling for baseline measures, age, sex, education level, treatment compliance, time in N2, and the location of the sleep recording, lower spindle density and sigma power at pre-treatment predicted poorer CBT-I response at post-treatment, as reflected by lower PSQI scores.
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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 | 0.003 |
| 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) | 0.002 | 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".