The role of vascular resistance in BOLD responses to progressive hypercapnia
Bibliographic record
Abstract
Abstract The ability of the cerebral vasculature to regulate vascular diameter, hence resistance and cerebral blood flow (CBF), in response to metabolic demands (neurovascular coupling), and perfusion pressure changes (autoregulation) may be assessed by measuring the CBF response to carbon dioxide (CO2). In healthy individuals, the CBF response to a ramp CO2 stimulus from hypocapnia to hypercapnia is assumed sigmoidal or linear. However, other response patterns commonly occur, especially in individuals with cerebrovascular disease, and these remain unexplained. CBF responses to CO2 in a vascular region are determined by the combined effects of the innate vascular responses to CO2 and the local perfusion pressure; the latter ensuing from pressure‐flow interactions within the cerebral vascular network. We modeled this situation as two vascular beds perfused in parallel from a fixed resistance source. Our premise is that all vascular beds have a sigmoidal reduction of resistance in response to a progressive rise in CO2. Surrogate CBF data to test the model was provided by magnetic resonance imaging of blood oxygen level‐dependent (BOLD) signals. The model successfully generated all the various BOLD‐CO2 response patterns, providing a physiological explanation of CBF distribution as relative differences in the network of vascular bed resistance responses to CO2. Hum Brain Mapp 38:5590–5602, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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".