Influence of Sleep State and Respiratory Pattern on Cyclical Fluctuations of Cerebral Blood Flow Velocity in Healthy Preterm Infants
Bibliographic record
Abstract
To examine the influence of sleep state, respiratory pattern, and ventilation on cyclical fluctuations (CF) in cerebral blood flow (CBF) velocity (CBFV), we studied 21 'healthy' preterm infants: birth weight 1,790 +/- 162 g (SEM), study weight 1,960 +/- 165 g, gestational age 32 +/- 1 weeks, postnatal age 20 +/- 4 (range 8-57) days. The CBFV was measured using on-line pulsed Doppler ultrasound by insonating the middle cerebral artery. Breathing was measured using a flow through system. The sleep state was monitored according to conventional criteria. Three hundred and seventy-five epochs of 1 min each were analyzed; 207 during quiet sleep (QS) and 168 during rapid eye movement (REM) sleep. CFs in CBFV were detected in all babies. The frequency of CF ranged from 0.5 to 6 cycles/min. The proportion of epochs showing CF was similar during both sleep states (56% QS vs. 59% REM; p = NS). Although the mean CBFV (cm/s) was similar in these two sleep states, the mean coefficient of variation, a measure of CF amplitude, was significantly higher during REM as compared with QS (6 +/- 0.5 vs. 4.3 +/- 0.2%; p < 0.05). Similarly, the mean CBFVs were similar with various respiratory patterns, but the coefficient of variation was significantly higher in periodic and apneic patterns as compared with regular and irregular respiratory patterns (5.6 +/- 0.6% periodic, 5.6 +/- 0.3% apneic, 3.6 +/- 0.3% regular, and 4.1 +/- 0.5% irregular, p < 0.05). The amplitude of CF was associated with the variability of the heart rate (p < 0.05), but not with the variability of the respiratory measurements. These findings suggest: (1) REM sleep is associated with a greater CBF variability than QS, and (2) periodic and apneic breathing are associated with a greater CBF variability than regular or irregular breathing. We speculate that sleep state and respiratory pattern do not determine but modulate the CBF. Our data suggest that in studies involving interpretation of CBFV data using the Doppler technique, breathing patterns should be taken into account in addition to sleep state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".