Evaluation of white matter myelin water fraction in chronic stroke
Bibliographic record
Abstract
Multi-component T2 relaxation imaging (MCRI) provides specific in vivo measurement of myelin water content and tissue water environments through myelin water fraction (MWF), intra/extra-cellular water fraction (I/EWF) and intra/extracellular and global geometric mean T2 (GMT2) times. Quantitative MCRI assessment of tissue water environments has provided new insights into the progression and underlying white matter pathology in neural disorders such as multiple sclerosis. It has not previously been applied to investigate changes in white matter in the stroke-affected brain. Thus, the purposes of this study were to 1) use MCRI to index myelin water content and tissue water environments in the brain after stroke 2) evaluate relationships between MWF and diffusion behavior indexed by diffusion tensor imaging-based metrics and 3) examine the relationship between white matter status (MWF and fractional anisotropy) and motor behavior in the chronic phase of stroke recovery. Twenty individuals with ischemic stroke and 12 matched healthy controls participated. Excellent to good test/re-test and inter-rater reliability was observed for region of interest-based voxelwise MWF data. Reduced MWF was observed in whole-cerebrum white matter (p < 0.001) and in the ipsilesional (p = 0.017) and contralesional (p = 0.037) posterior limb of internal capsule (PLIC) after stroke compared to whole-cerebrum and bilateral PLIC MWF in healthy controls. The stroke group also demonstrated increased I/EWF, I/E GMT2 and global GMT2 times for whole-cerebrum white matter. Measures of diffusion behavior were also significantly different in the stroke group across each region investigated (p < 0.001). MWF was not significantly correlated with specific tensor-based measures of diffusion in the PLIC for either group. Fractional anisotropy in the ipsilesional PLIC correlated with motor behavior in chronic stroke. These results provide novel insights into tissue-specific changes within white matter after stroke that may have important applications for the understanding of the neuropathology of stroke.
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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 | 0.001 |
| 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 | 0.000 | 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".