BOLD‐specific cerebral blood volume and blood flow changes during neuronal activation in humans
Bibliographic record
Abstract
To understand and predict the blood-oxygenation level-dependent (BOLD) fMRI signal, an accurate knowledge of the relationship between cerebral blood flow (DeltaCBF) and volume (DeltaCBV) changes is critical. Currently, this relationship is widely assumed to be characterized by Grubb's power-law, derived from primate data, where the power coefficient (alpha) was found to be 0.38. The validity of this general formulation has been examined previously, and an alpha of 0.38 has been frequently cited when calculating the cerebral oxygen metabolism change (DeltaCMRo(2)) using calibrated BOLD. However, the direct use of this relationship has been the subject of some debate, since it is well established that the BOLD signal is primarily modulated by changes in 'venous' CBV (DeltaCBV(v), comprising deoxygenated blood in the capillary, venular, and to a lesser extent, in the arteriolar compartments) instead of total CBV, and yet DeltaCBV(v) measurements in humans have been extremely scarce. In this work, we demonstrate reproducible DeltaCBV(v) measurements at 3 T using venous refocusing for the volume estimation (VERVE) technique, and report on steady-state DeltaCBV(v) and DeltaCBF measurements in human subjects undergoing graded visual and sensorimotor stimulation. We found that: (1) a BOLD-specific flow-volume power-law relationship is described by alpha = 0.23 +/- 0.05, significantly lower than Grubb's constant of 0.38 for total CBV; (2) this power-law constant was not found to vary significantly between the visual and sensorimotor areas; and (3) the use of Grubb's value of 0.38 in gradient-echo BOLD modeling results in an underestimation of DeltaCMRo(2).
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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) | 0.001 | 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".