Model of Blood–Brain Transfer of Oxygen Explains Nonlinear Flow-Metabolism Coupling During Stimulation of Visual Cortex
Bibliographic record
Abstract
The coupling between cerebral metabolic rate of oxygen (CMRO2) and blood flow (CBF) in response to visual stimulation was evaluated by means of a model of oxygen delivery. The model predicted a nonlinear relationship between stimulus-evoked changes of oxygen consumption and blood flow. The magnitude of the CMRO2/CBF ratio index (IO2) was used to indicate the degree of flow-metabolism coupling prevailing in specific areas of the brain during physiological stimulation. Therefore, the index provided a measure of the blood oxygenation level dependent (BOLD) magnetic resonance contrast. To evaluate the changes of IO2 in response to visual stimulation, the model was applied to the effect of a changing flicker rate of a visual stimulus on the magnitudes of CBF, CMRO2, and oxygen diffusion capacity, in the human brain. Positron emission tomography (PET) was used to measure the CBF and the CMRO2 in 12 healthy volunteers who viewed a cross-hair (baseline) or a yellow-blue annular checkerboard reversing at frequencies of 1, 4, or 8 Hz. The magnitude of CBF in the primary visual cortex increased as a function of the checkerboard reversal rate and reached a maximum at the frequency of 8 Hz (z=16.0), while the magnitude of CMRO2 reached a maximum at 4 Hz (z=4.0). Therefore, the calculated IO2 was lower at 8 Hz than at 1 and 4 Hz, in contrast to the oxidative metabolic rate that reached its maximum at 4 Hz. The model explained the increase of oxygen consumption as the combined effect of increased blood flow and increased oxygen diffusion capacity in the region of visual activation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".