Examining the upper frequency limit of dynamic cerebral autoregulation: Considerations across the cardiac cycle during eucapnia
Bibliographic record
Abstract
There are differences within the literature regarding the upper frequency cut-off point of the dynamic cerebral autoregulation (CA) high-pass filter. The projection pursuit regression approach has demonstrated that the upper frequency limit is ∼0.07 Hz, whereas another approach [transfer function analysis (TFA) phase approaching zero] indicated a theoretical upper frequency limit for the high-pass filter of 0.24 Hz. We investigated how these limits accurately represent the CA upper frequency limit, in addition to extending earlier findings with respect to biological sexes and across the cardiac cycle. Sixteen participants (nine females and seven males) performed repeated squat-stand manoeuvres at frequencies of 0.05, 0.10, 0.15, 0.20 and 0.25 Hz, with insonation of the middle and posterior cerebral arteries. Linear regression modelling with adjustment for sex and order of squat completion was used to compared TFA gain and phase with 0.25 Hz (above the theoretical limit of CA). The upper frequency limit of CA with TFA gain was within the range of 0.05-0.10 Hz, whereas TFA phase was within the range of 0.20-0.25 Hz, and consistent between vessels, between sexes and across the cardiac cycle. Females displayed greater middle cerebral artery gain compared with males (all P < 0.047), and no phase differences were present (all P > 0.072). Although sex-specific differences were present for specific TFA metrics at a given frequency, the upper frequency limit of autoregulation was similar between cerebral conduit vessels, cardiac cycle phase and biological sex. Future work is warranted to determine whether an upper frequency limit exists with respect to hysteresis analyses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".