Whole‐brain <scp>BOLD</scp> responses to graded hypoxic challenges at 7 <scp>T</scp> , 9.4 <scp>T</scp> , and 15.2 <scp>T</scp> : Implications for ultrahigh‐field functional and dynamic susceptibility contrast <scp>MRI</scp>
Bibliographic record
Abstract
Abstract Purpose Blood oxygen–level dependent (BOLD) functional MRI signals depend on changes in deoxyhemoglobin content, which is associated with baseline cerebral blood volume (CBV) and blood oxygen saturation change. To accurately interpret activation‐induced BOLD responses and quantify perfusion values by BOLD dynamic susceptibility contrast (BOLD‐DSC) with transient hypoxia, it is critical to assess Δ values in tissue and blood across varying levels of hypoxia and magnetic field strengths (B 0 ). Methods Whole‐brain BOLD responses were examined using 5‐s graded hypoxic challenges with 10%, 5%, and 0% O 2 at ultrahigh field strengths of 7 T, 9.4 T, and 15.2 T. Both tissue and blood responses were analyzed for BOLD‐DSC quantification. Results Substantial heterogeneity in hypoxia‐induced Δ was observed among regions under different hypoxic doses and B 0 . Nonlinear Δ responses with increasing field strength were observed, depending on hypoxic levels: 10% O 2 condition exhibited pronounced supralinear trends, whereas 0% and 5% O 2 conditions showed nearly linear dependencies. Blood arterial and venous responses showed a similar dependence as tissue. However, at 15.2 T, the venous signal saturated under 5% and 0% O 2 conditions. Quantitative CBV values obtained from BOLD‐DSC data showed dependency on susceptibility effects, and higher B 0 and hypoxic severity resulted in slightly higher CBV, indicating that caution is needed when comparing quantitative CBV values derived from different experimental protocols. Normalizing regional CBV values to those of white matter effectively reduced the impact of varying susceptibility contrasts. Conclusions Our investigations provide biophysical insights into the BOLD contrast mechanism at ultrahigh fields, and address quantification issues in susceptibility‐based CBV measurements.
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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 | 0.000 |
| 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".