Myelin biomarkers in the healthy adult brain: Correlation, reproducibility, and the effect of fiber orientation
Bibliographic record
Abstract
Purpose We investigated the correlation, reproducibility, and effect of white matter fiber orientation for three myelin‐sensitive MRI techniques: magnetization transfer ratio (MTR), inhomogeneous magnetization transfer ratio (ihMTR), and gradient and spin echo–derived myelin water fraction (MWF). Methods We measured the three metrics in 17 white and three deep grey matter regions in 17 healthy adults at 3 T. Results We found a strong correlation between ihMTR and MTR (r = 0.70, p < 0.001) and ihMTR and MWF (r = 0.79, p < 0.001), and a weaker correlation between MTR and MWF (r = 0.54, p < 0.001). The dynamic range in white matter was greatest for MWF (2.0%–27.5%), followed by MTR (14.4%–23.2%) and then ihMTR (1.2%–5.4%). The average scan–rescan coefficient of variation for white matter regions was 0.6% MTR, 0.3% ihMTR, and 0.7% MWF in metric units; however, when adjusted by the dynamic range, these became 6.3%, 6.1% and 2.8%, respectively. All three metrics varied with fiber direction: MWF and ihMTR were lower in white matter fibers perpendicular to B0 by 6% and 1%, respectively, compared with those parallel, whereas MTR was lower by 0.5% at about 40°, with the highest values at 90°. However, separating the apparent orientation dependence by white matter region revealed large dissimilarities in the trends, suggesting that real differences in myelination between regions are confounding the apparent orientation dependence measured using this method. Conclusion The strong correlation between ihMTR and MWF suggests that these techniques are measuring the same myelination; however, the larger dynamic range of MWF may provide more power to detect small differences in myelin.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".