Functional magnetic resonance imaging of cerebral blood volume
Bibliographic record
Abstract
This dissertation describes a novel method for quantifying venous cerebral blood volume (CBVv) changes accompanying normal functional activation and employs quantitative functional magnetic resonance imaging (fMRI) methods to study the hemodynamic and metabolic changes accompanying neuronal inhibition. An in vivo occipital lobe relaxometry study was performed first to investigate the dependence of the spin-spin relaxation time constant of tissue, T2tissue , on the refocusing interval over the range of interest and thus test the basis of the proposed CBVv method. The small decrease of the apparent T2tissue with refocusing interval elongation is consistent with blood being the only significant source of refocusing interval dependence of apparent T2 in grey and white matter of the occipital lobe. In ensuing in vitro blood relaxometry studies, ensemble fitting of the entire set of T2blood estimates, obtained over an extensive range of blood oxygenation levels and refocusing intervals, was performed using both the fast chemical exchange model and the model of diffusion in weak magnetic field inhomogeneities. The results support the application of a diffusion model in describing the deoxyhemoglobin-induced enhancement in blood transverse relaxation rate at 1.5 T. Given the uniqueness of T2blood dependence on the refocusing rates over the range of interest, the novel CBVv method - venous refocusing for volume estimation (VERVE) - successfully isolates the blood signal by refocusing rate variation. A model of functional brain activation was developed and in vitro blood relaxometry data used to assess the effect of the intravascular spin-echo blood oxygenation level dependent (BOLD) contrast on the activation-induced VERVE signal change, allowing robust estimation of venous CBV changes. The method was demonstrated in a visual stimulation study of healthy young adults, where an average venous blood volume in the visual cortex increase was estimated
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".