Time scales of autonomic information flow in near-term fetal sheep
Bibliographic record
Abstract
Autonomic information flow (AIF) characterizes fetal heart rate (FHR) variability (fHRV) in the time scale dependent complexity domain and discriminates sleep states [high voltage/low frequency (HV/LF) and low voltage/high frequency (LV/HF) electrocortical activity (ECoG)]. However, the physiologic relationship of AIF time scales to the underlying sympathetic and vagal rhythms is not known. Understanding this relationship will enhance the benefits derived from using fHRV to monitor fetal health non-invasively. We analyzed AIF measured as Kullback-Leibler entropy (KLE) in fetal sheep in late gestation as function of vagal and sympathetic modulation of fHRV, using atropine and propranolol, respectively (n = 6), and also analyzed changes in fHRV during sleep states (n = 12). Atropine blockade resulted in complexity decrease at 2.5 Hz compared to baseline HV/LF and LV/HF states and at 1.6 Hz compared to LV/HF. Propranolol blockade resulted in complexity increase in the 0.8-1 Hz range compared to LV/HF and in no changes when compared to HV/LF. During LV/HF state activity, fHRV complexity was lower at 2.5 Hz and higher at 0.15-0.19 Hz than during HV/LF. Our findings show that in mature fetuses near term vagal activity contributes to fHRV complexity on a wider range of time scales than sympathetic activity. Related to sleep, during LV/HF we found lower complexity at short-term time scale where complexity is also decreased due to vagal blockade. We conclude that vagal and sympathetic modulations of fHRV show sleep state-dependent and time scale-dependent complexity patterns captured by AIF analysis of fHRV. Specifically, we observed a vagally mediated and sleep state-dependent change in these patterns at a time scale around 2.5 Hz (0.2 s). A paradigm of state-dependent non-linear sympathovagal modulation of fHRV is discussed.
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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".