Integrative regulation of human brain blood flow
Bibliographic record
Abstract
Herein, we review mechanisms regulating cerebral blood flow (CBF), with specific focus on humans. We revisit important concepts from the older literature and describe the interaction of various mechanisms of cerebrovascular control. We amalgamate this broad scope of information into a brief review, rather than detailing any one mechanism or area of research. The relationship between regulatory mechanisms is emphasized, but the following three broad categories of control are explicated: (1) the effect of blood gases and neuronal metabolism on CBF; (2) buffering of CBF with changes in blood pressure, termed cerebral autoregulation; and (3) the role of the autonomic nervous system in CBF regulation. With respect to these control mechanisms, we provide evidence against several canonized paradigms of CBF control. Specifically, we corroborate the following four key theses: (1) that cerebral autoregulation does not maintain constant perfusion through a mean arterial pressure range of 60-150 mmHg; (2) that there is important stimulatory synergism and regulatory interdependence of arterial blood gases and blood pressure on CBF regulation; (3) that cerebral autoregulation and cerebrovascular sensitivity to changes in arterial blood gases are not modulated solely at the pial arterioles; and (4) that neurogenic control of the cerebral vasculature is an important player in autoregulatory function and, crucially, acts to buffer surges in perfusion pressure. Finally, we summarize the state of our knowledge with respect to these areas, outline important gaps in the literature 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".