Myelin water and tensor‐valued diffusion imaging: (How) are they related?
Bibliographic record
Abstract
Abstract Purpose Conventional MRI offers limited insight into specific characteristics of central nervous system tissue, whereas quantitative MRI measures can provide more detailed information about different aspects of microstructure. A multi‐metric approach involving multiple quantitative measures may improve our understanding of healthy tissue and pathology. Previous work shows myelin water fraction (MWF) is related to fractional anisotropy (FA), but this relationship is complicated by confounding factors that may be resolved using tensor‐valued diffusion imaging, which yields measurements of microscopic FA (μFA) and tissue heterogeneity (CMD). Our aims were to better understand how measures from myelin water and tensor‐valued diffusion imaging relate to one another, and to demonstrate how these measures can be used to characterize microstructure in both healthy white matter and pathological changes. Methods We assessed the relationship between MWF, FA, μFA, and CMD from 25 healthy individuals through atlas comparison, correlation analysis, and tract profiling. We also applied z‐score analysis and tract profiling in five people with multiple sclerosis (MS) to evaluate the multi‐metric utility of these measures in assessing pathology. Results Although correlation analysis showed moderate, but potentially misleading relationships between metrics, tract profiling showed consistent tract‐specific pattern differences between metrics in healthy tissue. In MS, MWF, μFA, and CMD were the most sensitive to pathological changes, showing regions of abnormality even in normal‐appearing white matter and along lesional tracts, and highlighting different types of damage. Conclusion Using MWF, μFA, and CMD to separately assess myelination, anisotropy, and tissue heterogeneity enhances our ability to investigate development, aging, disease, and injury.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".