Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To analyze the texture of T2-weighted magnetic resonance imaging (MRI) of postmortem multiple sclerosis (MS) brain, and to determine whether and how MRI texture correlates with tissue pathology. METHODS: Ten brain samples from 3 subjects with MS were examined. Areas of complete, partial, or no loss of Luxol fast blue (myelin) and Bielschowsky (axons) staining were marked on histological images, and matched on corresponding MRI as lesions, diffusely abnormal white matter (DAWM), and normal-appearing white matter (NAWM). The number of CD45(+) cells (inflammation) was also counted. MRI texture was computed using polar Stockwell transform and compared to histology. RESULTS: Thirty-four lesions, 17 DAWM regions, and 36 NAWM regions were identified. After mixed effects modeling, MRI texture heterogeneity was greater in lesions than in DAWM (p < 0.001) and NAWM (p < 0.001), and was greater in DAWM than in NAWM (p < 0.001); the number of CD45+ cells was greater in both lesions (p < 0.001) and DAWM (p = 0.005) than in NAWM. In MRI, a gradient of texture heterogeneity was detected in lesions, with gradual tapering toward perilesional NAWM. Moreover, besides univariate correlation with histological markers, texture heterogeneity correlated independently with normalized myelin density (p < 0.01) when random effects were considered. Within sample, MRI texture correlated with myelin and axonal density in 7 of 10 samples (p < 0.01). INTERPRETATION: Texture analysis performed on routine clinical magnetic resonance images may be a potential measure of tissue integrity. Tissues with more severe myelin and axonal pathology are associated with greater texture heterogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".