Multiple Sclerosis: Improved Identification of Disease-relevant Changes in Gray and White Matter by Using Susceptibility-based MR Imaging
Bibliographic record
Abstract
PURPOSE: To evaluate the potential of quantitative susceptibility (QS) and R2* mapping as surrogate biomarkers of clinically relevant, age-adjusted demyelination and iron deposition in multiple sclerosis (MS). MATERIALS AND METHODS: All study participants gave written informed consent, and the study was approved by the institutional review board. Quantitative maps of the magnetic resonance imaging susceptibility parameters (R2* and QS) were computed for 25 patients with either clinically isolated syndrome (CIS) or relapsing-remitting MS, as well as for 15 age- and sex-matched control subjects imaged at 7 T. The candidate MR imaging biomarkers were correlated with Extended Disability Status Scale (EDSS), time since CIS diagnosis, time since MS diagnosis, and age. RESULTS: QS maps aided identification of significant, voxel-level increases in iron deposition in subcortical gray matter (GM) of patients with MS compared with control subjects. These voxel-level increases were not observed on R2* maps. Region-of-interest analysis of mean R2* and QS in subcortical GM demonstrated that R2* (R ≥ 0.39, P < .01) and QS (R ≥ 0.44, P < .01) were strongly correlated with EDSS. In white matter (WM), the volume of total WM damage (defined by a z score of less than -2.0 criterion, indicating demyelination) on QS maps correlated significantly with EDSS (R = 0.46, P = .02). Voxelwise QS also supported a significant contribution of age to demyelination in patients with MS, suggesting that age-adjusted clinical scores may provide more robust measures of MS disease severity compared with non-age-adjusted scores. CONCLUSION: Using QS and R2* mapping, evidence of both significant increases in iron deposition in subcortical GM and myelin degeneration along the WM skeleton of patients with MS was identified. Both effects correlated strongly with EDSS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".