A comparison of inhomogeneous magnetization transfer, myelin volume fraction, and diffusion tensor imaging measures in healthy children
Bibliographic record
Abstract
Sensitive and specific biomarkers of myelin can help define baseline brain health and development, identify and monitor disease pathology, and evaluate response to treatment where myelin content is affected. Diffusion measures such as radial diffusivity (RD) are commonly used to assess myelin content, but are not specific to myelin. Inhomogeneous magnetization transfer (ihMT) and multicomponent driven equilibrium single-pulse observation of T1 and T2 (mcDESPOT) offer quantitative parameters (qihMT and myelin volume fraction/VFm, respectively) which are suggested to have improved sensitivity to myelin. We compared RD, qihMT, and VFm in a cohort of 23 healthy children aged 8–13 years to evaluate the similarities and differences across these measures. All 3 measures were significantly related across brain voxels, but VFm and qihMT were significantly more strongly correlated (qihMT-VFm r = 0.89) than either measure was with RD (RD-qihMT r = −0.66, RD-VFm r = −0.74; all p < 0.001). Mean parameters differed in several regions, especially in subcortical gray matter. These differences can likely be explained by unique sensitivities of each measure to non-myelin factors, such as crossing fiber geometry, axonal packing, fiber orientation, glial density, or magnetization transfer effects in a voxel. We also observed an orientation dependence of qihMT in white matter, such that qihMT decreased as fiber orientation went from parallel to perpendicular to B0. All measures appear to be sensitive to myelin content, though qihMT and VFm appear to be more specific to it than RD. Scan time, noise tolerance, and resolution requirements may inform researchers of the appropriate measure to choose for a specific application.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".