Venous contribution to sodium MRI in the human brain
Bibliographic record
Abstract
Purpose Sodium MRI shows great promise as a marker for cerebral metabolic dysfunction in stroke, brain tumor, and neurodegenerative pathologies. However, cerebral blood vessels, whose volume and function are perturbed in these pathologies, have elevated sodium concentrations relative to surrounding tissue. This study aims to assess whether this fluid compartment could bias measurements of tissue sodium using MRI. Methods Density‐weighted and B1 corrected sodium MRI of the brain was acquired in 9 healthy participants at 4.7T. Veins were identified using co‐registered 1H ‐weighted images and venous partial volume estimates were calculated by down‐sampling the finer spatial resolution venous maps from the ‐weighted images to the coarser spatial resolution of the sodium data. Linear regressions of venous partial volume estimates and sodium signal were performed for regions of interest including just gray matter, just white matter, and all brain tissue. Results Linear regression demonstrated a significant venous sodium contribution above the underlying tissue signal. The apparent venous sodium concentrations derived from regression were 65.8 ± 4.5 mM (all brain tissue), 71.0 ± 7.4 mM (gray matter), and 55.0 ± 4.7 mM (white matter). Conclusion Although the partial vein linear regression did not yield the expected sodium concentration in blood (~87 mM), likely the result of point spread function smearing, this regression highlights that blood compartments may bias brain tissue sodium signals across neurological conditions where blood volumes may differ.
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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 | 0.003 |
| 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 | 0.001 | 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".