Experimental measurement of extravascular parenchymal BOLD effects and tissue oxygen extraction fractions using multi‐echo VASO fMRI at 1.5 and 3.0 T
Bibliographic record
Abstract
Quantitative interpretation of BOLD fMRI signal changes has predominantly employed empirical models for the whole parenchyma and a calibration step is usually needed to determine the physiological parameters during activation. Although analytical expressions are available for the extravascular and intravascular components of the BOLD effects, it is difficult to experimentally separate tissue from blood signal contributions at the low magnetic fields in which most fMRI studies are performed. Even if this can be achieved, an additional problem that remains is the separation of two types of extravascular BOLD effects, namely those around microvasculature (in the parenchyma close to the site of activation) and those around draining macrovasculature (e.g., in tissue and CSF more remote from the site of activation). In the recently developed vascular space occupancy technique, blood signals are nulled and the activations are localized predominantly in gray matter, allowing experimental measurement of parenchymal extravascular R(2)* and its changes accompanying activation. When comparing such data with total parenchymal R(2)* changes in BOLD fMRI, the extravascular fractions were found to be 47 +/- 7% (mean +/- SEM, n = 4) and 67 +/- 6% at 1.5 and 3.0 T, respectively, in line with expectations that intravascular BOLD contributions are reduced at higher field. The present approach provides a noninvasive means to determine parenchymal oxygen extraction fraction (OEF) in situ. During visual stimulation, OEF values measured at 1.5 and 3.0 T were in good agreement, giving 0.23 +/- 0.01 and 0.21 +/- 0.01, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".