Cerebral blood flow during exercise: mechanisms of regulation
Bibliographic record
Abstract
The response of cerebral vasculature to exercise is different from other peripheral vasculature; it has a small vascular bed and is strongly regulated by cerebral autoregulation and the partial pressure of arterial carbon dioxide (Pa(CO(2))). In contrast to other organs, the traditional thinking is that total cerebral blood flow (CBF) remains relatively constant and is largely unaffected by a variety of conditions, including those imposed during exercise. Recent research, however, indicates that cerebral neuronal activity and metabolism drive an increase in CBF during exercise. Increases in exercise intensity up to approximately 60% of maximal oxygen uptake produce elevations in CBF, after which CBF decreases toward baseline values because of lower Pa(CO(2)) via hyperventilation-induced cerebral vasoconstriction. This finding indicates that, during heavy exercise, CBF decreases despite the cerebral metabolic demand. In contrast, this reduced CBF during heavy exercise lowers cerebral oxygenation and therefore may act as an independent influence on central fatigue. In this review, we highlight methodological considerations relevant for the assessment of CBF and then summarize the integrative mechanisms underlying the regulation of CBF at rest and during exercise. In addition, we examine how CBF regulation during exercise is altered by exercise training, hypoxia, and aging and suggest avenues for future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".