Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination
Bibliographic record
Abstract
The cuprizone mouse model of demyelination was used to investigate the influence that white matter changes have on different magnetic resonance imaging results. In vivo T2 -weighted and magnetization transfer images (MTIs) were acquired weekly in control (n = 5) and cuprizone-fed (n = 5) mice, with significant increases in signal intensity in T2 -weighted images (p < 0.001) and lower magnetization transfer ratio (p < 0.001) in the corpus callosum of the cuprizone-fed mice starting at 3 weeks and peaking at 4 and 5 weeks, respectively. Diffusion tensor imaging (DTI), quantitative MTI (qMTI), and T1/T2 measurements were used to analyze freshly excised tissue after 6 weeks of cuprizone administration. In multicomponent T2 analysis with 10 ms echo spacing, there was no visible myelin water component associated with the short T2 value. Quantitative MTI metrics showed significant differences in the corpus callosum and external capsule of the cuprizone-fed mice, similar to previous studies of multiple sclerosis in humans and animal models of demyelination. Fractional anisotropy was significantly lower and mean, axial, and radial diffusivity were significantly higher in the cuprizone-fed mice. Cellular distributions measured in electron micrographs of the corpus callosum correlated strongly to several different quantitative MRI metrics. The largest Spearman correlation coefficient varied depending on cellular type: T1 versus the myelinated axon fraction (ρ = -0.90), the bound pool fraction (ƒ) versus the myelin sheath fraction (ρ = 0.93), and axial diffusivity versus the non-myelinated cell fraction (ρ = 0.92). Using Pearson's correlation coefficient, ƒ was strongly correlated to the myelin sheath fraction (r = 0.98) with a linear equation predicting myelin content (5.37ƒ - 0.25). Of the calculated MRI metrics, ƒ was the strongest indicator of myelin content, while longitudinal relaxation rates and diffusivity measurements were the strongest indicators of changes in tissue structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".