Venous refocusing for volume estimation: VERVE functional magnetic resonance imaging
Bibliographic record
Abstract
A novel, noninvasive magnetic resonance imaging-based method for measuring changes in venous cerebral blood volume (CBV(v)) is presented. Venous refocusing for volume estimation (VERVE) exploits the dependency of the spin-spin relaxation rate of deoxygenated blood on the refocusing interval. Interleaved CPMG EPI acquisitions following a train of either tightly or sparsely spaced hard refocusing pulses (every 3.7 or 30 msec, respectively) at matched echo time were used to isolate the blood signal while minimizing the intravascular blood oxygenation level dependent (BOLD) signal contribution. The technique was employed to determine the steady-state increase in the CBV(v) in the visual cortex (VC) in seven healthy adult volunteers during flickering checkerboard photic stimulation. A functional activation model and a set of previously collected in vitro human whole blood relaxometry data were used to evaluate the intravascular BOLD effect on the VERVE signal. The average VC venous blood volume change was estimated to be 16 +/- 2%. This method has the potential to provide efficient and continuous monitoring of venous cerebral blood volume, thereby enabling further exploration of the mechanism underlying BOLD signal changes upon physiologic, pathophysiologic, and pharmacologic perturbations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".