Clinically feasible MTR is sensitive to cortical demyelination in MS
Bibliographic record
Abstract
OBJECTIVE: Presently there is no clinically feasible imaging modality that can effectively detect cortical demyelination in patients with multiple sclerosis (MS). The objective of this study is to determine if clinically feasible magnetization transfer ratio (MTR) imaging is sensitive to cortical demyelination in MS. METHODS: MRI were acquired in situ on 7 recently deceased patients with MS using clinically feasible sequences at 3 T, including relatively high-resolution T1-weighted and proton density-weighted images with/without a magnetization transfer pulse for calculation of MTR. The brains were rapidly removed and placed in fixative. Multiple cortical regions from each brain were immunostained for myelin proteolipid protein and classified as mostly myelinated (MM(ctx)), mostly demyelinated (MD(ctx)), or intermediately demyelinated (ID(ctx)). MRIs were registered with the cortical sections so that the cortex corresponding to each cortical section could be identified, along with adjacent subcortical white matter (WM). Mean cortical MTR normalized to mean WM MTR was calculated for each cortical region. Linear mixed-effects models were used to test if mean normalized cortical MTR was significantly lower in demyelinated cortex. RESULTS: We found that mean normalized cortical MTR was significantly lower in cortical tissue with any demyelination (ID(ctx) or MD(ctx)) compared to MM(ctx) (demyelinated cortex: least-squares mean [LSM] = 0.797, SE = 0.007; MM(ctx): LSM = 0.837, SE = 0.006; p = 0.01, n = 89). CONCLUSIONS: This result demonstrates that clinically feasible MTR imaging is sensitive to cortical demyelination and suggests that MTR will be a useful tool to help detect MS cortical lesions in living patients with MS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".