Relationship between blood pressure and cerebral blood flow during supine cycling: influence of aging
Bibliographic record
Abstract
The cerebral pressure-flow relationship can be quantified as a high-pass filter, where slow oscillations are buffered (<0.20 Hz) and faster oscillations are passed through relatively unimpeded. During moderate intensity exercise, previous studies have reported paradoxical transfer function analysis (TFA) findings (altered phase or intact gain). This study aimed to determine whether these previous findings accurately represent this relationship. Both younger (20-30 yr; n = 10) and older (62-72 yr; n = 9) adults were examined. To enhance the signal-to-noise ratio, large oscillations in blood pressure (via oscillatory lower body negative pressure; OLBNP) were induced during steady-state moderate intensity supine exercise (∼45-50% of heart rate reserve). Beat-to-beat blood pressure, cerebral blood velocity, and end-tidal Pco2 were monitored. Very low frequency (0.02-0.07 Hz) and low frequency (0.07-0.20 Hz) range spontaneous data were quantified. Driven OLBNP point estimates were sampled at 0.05 and 0.10 Hz. The OLBNP maneuvers augmented coherence to >0.97 at 0.05 Hz and >0.98 at 0.10 Hz in both age groups. The OLBNP protocol conclusively revealed the cerebrovascular system functions as a high-pass filter during exercise throughout aging. It was also discovered that the older adults had elevations (+71%) in normalized gain (+0.46 ± 0.36%/%: 0.05 Hz) and reductions (-34%) in phase (-0.24 ± 0.22 radian: 0.10 Hz). There were also age-related phase differences between resting and exercise conditions. It is speculated that these age-related changes in the TFA metrics are mediated by alterations in vasoactive factors, sympathetic tone, or the mechanical buffering of the compliance vessels.
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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) | 0.000 | 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 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".