Modulation of cardiac autonomic activity across consciousness states and levels of sleep depth in individuals with sleep complaints and bipolar disorder or unipolar depressive disorders
Bibliographic record
Abstract
OBJECTIVE: Autonomic nervous system dysfunction and reduced heart rate variability (HRV) often co-exist with mood disorders, a phenomenon likely influenced by sleep disturbances. This study investigated heart rate (HR) and HRV across wake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep in individuals with sleep complaints and bipolar or unipolar depressive disorder. METHODS: Polysomnographic data was retrospectively collated for 120 adult patients with sleep complaints and depressive symptoms [60 diagnosed with bipolar disorder, 60 diagnosed with a unipolar depressive disorder], and 60 healthy controls. HR and time-based HRV variables were computed on 30-s segments and averaged across the night for wake, NREM and REM sleep. RESULTS: Significant group by consciousness state interactions showed that the unipolar and bipolar groups had lower standard deviation of normal-to-normal intervals root mean square of successive R-R interval differences compared to controls during NREM and REM sleep, but not during wake (SDNN: F(4, 330) = 3.0, p = .021, np2 = 0.035; RMSSD: F(4, 332) = 5.8, p < .001, np2 = 0.065). The magnitude of these group differences did not vary significantly between NREM 1, NREM 2 and NREM 3 sleep. These interactions persisted after excluding individuals taking 3rd generation antipsychotic, lithium, anticonvulsant, and cardiovascular medications. CONCLUSION: Although further work is required to account for the impact of psychotropic and cardiac medications, as well as manic and euthymic states, these findings suggest that the sleep-based autonomic signature of depressive states differs across different types of mood disorders and could potentially inform the development of biomarkers and therapeutic targets.
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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".