Multi-scale MRI spectrum detects differences in myelin integrity between MS lesion types
Bibliographic record
Abstract
BACKGROUND: Lesions with different extents of myelin pathology are found at autopsy in multiple sclerosis (MS), but the differences are not discernible in magnetic resonance imaging (MRI). OBJECTIVE: To determine whether analysis of the local spectrum in MRI is sensitive to lesion differences in myelin integrity. METHODS: We imaged fresh brain slices from 21 MS patients using 1.5T scanners. White matter lesions were identified in T2-weighted MRI, matched to corresponding specimens, and then classified into five categories in histology: pre-active (intact myelin); active, chronic active, chronic inactive (complete demyelination); and remyelinated lesions. Voxel-based frequency spectrum was calculated using T2-weighted MRI to characterize lesion structure (image texture). RESULTS: MRI texture heterogeneity resulting from all spectral scales was greater in completely demyelinated lesions than in myelin-preserved lesions (p = 0.02) and normal-appearing white matter (p < 0.01). Moreover, the spectral distribution pattern over low-frequency scales differentiated demyelinated lesions from remyelinated and pre-active lesions (p < 0.01), where different lesion types also showed distinct texture scales. CONCLUSION: Using multi-scale spectral analysis, it may be possible for standard MRI to evaluate myelin integrity in MS lesions. This can be critical for monitoring disease activity and assessing remyelination therapies for MS patients.
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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) | 0.002 | 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".