Susceptibility‐sensitive MRI of multiple sclerosis lesions and the impact of normal‐appearing white matter changes
Bibliographic record
Abstract
Susceptibility-sensitive magnetic resonance imaging (MRI) has gained importance in multiple sclerosis (MS) research because of its versatility, high resolution and excellent sensitivity to changes in tissue structure and composition. In particular, mapping of the resonance frequency of the MR signal and quantitative susceptibility mapping (QSM) have been explored for the description of MS lesions. Many current studies utilizing these techniques attribute increases in the MR frequency or QSM to elevated tissue iron content, in addition to myelin loss. However, this common interpretation is inconsistent with recent histopathological studies. Here, we investigate the nature of MR frequency shifts related to MS lesions by comparing post-mortem MRI data with histology, and contrast them with numerical simulations of the MR signal. We demonstrate that iron accumulation is not the driving source of the MR frequency or QSM image contrast in our sample; rather, most chronic MS lesions are characterized by advanced loss of both myelin and iron. Moreover, our results suggest that the appearance of MS lesions on MR frequency maps and QSM depends on changes in the non-lesional white matter surrounding the lesions. Understanding and accounting for these changes is essential for the quantitative interpretation of MR frequency or QSM data in white matter.
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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 | 0.002 |
| 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".