Autonomic Modulation of Cardiac Activity Across Levels of Sleep Depth in Individuals With Depression and Sleep Complaints
Bibliographic record
Abstract
OBJECTIVE: We assessed mean heart rate (HR) and HR variability (HRV) across wake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep, and across varying levels of NREM sleep depth in individuals with depression and sleep complaints. METHODS: Retrospective polysomnographic data were obtained for 25 individuals diagnosed as having depression (84% female; mean age = 33.8 ± 12.2 years) and 31 mentally healthy controls (58.1% female; mean age = 37.2 ± 12.4 years). All were free of psychotropic and cardiovascular medication, cardiovascular disease, and sleep-related breathing disorders. HR and time-domain HRV parameters were computed on 30-second electrocardiography segments and averaged across the night for each stage of sleep and wake. RESULTS: Compared with the control group, the depression group had higher HR across wake, REM, and all levels of NREM depth (F(1,51) = 6.3, p = .015). Significant group by sleep stage interactions were found for HRV parameters: SD of normal-to-normal intervals (SDNN; F(2.1,107.7) = 4.4, p = .014) and root mean square differences of successive R-R intervals (RMSSD; F(2.2,113.5) = 3.2, p = .041). No significant group difference was found for SDNN or RMSSD during wake (all, p ≥ .32). However, compared with the control group, the depression group had significantly lower SDNN in REM (p = .040) and all NREM stages (all p ≤ .045), and lower RMSSD during NREM 2 (p = .033) and NREM 3 (p = .034). CONCLUSIONS: This study suggests that the abnormalities in autonomic cardiac regulation associated with depression and sleep problems are more prominent during sleep, especially NREM sleep, than during wake. This may be due to abnormalities in parasympathetic modulation of cardiac activity.
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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 | 0.000 | 0.001 |
| 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".