Delineation of cortical pathology in multiple sclerosis using multi-surface magnetization transfer ratio imaging
Bibliographic record
Abstract
The purpose of our study was to evaluate the utility of measurements of cortical surface magnetization transfer ratio (csMTR) on the inner, mid and outer cortical boundaries as clinically accessible biomarkers of cortical gray matter pathology in multiple sclerosis (MS). Twenty-five MS patients and 12 matched controls were recruited from the MS Clinic of the Montreal Neurological Institute. Anatomical and magnetization transfer ratio (MTR) images were acquired using 3 Tesla MRI at baseline and two-year time-points. MTR maps were smoothed along meshes representing the inner, mid and outer neocortical boundaries. To evaluate csMTR reductions suggestive of sub-pial demyelination in MS patients, a mixed model analysis was carried out at both the individual vertex level and in anatomically parcellated brain regions. Our results demonstrate that focal areas of csMTR reduction are most prevalent along the outer cortical surface in the superior temporal and posterior cingulate cortices, as well as in the cuneus and precentral gyrus. Additionally, age regression analysis identified that reductions of csMTR in MS patients increase with age but appear to hit a plateau in the outer caudal anterior cingulate, as well as in the precentral and postcentral cortex. After correction for the naturally occurring gradient in cortical MTR, the difference in csMTR between the inner and outer cortex in focal areas in the brains of MS patients correlated with clinical disability. Overall, our findings support multi-surface analysis of csMTR as a sensitive marker of cortical sub-pial abnormality indicative of demyelination in 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 | 0.001 | 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".