Cortical Perfusion Alteration in Normal-Appearing Gray Matter Is Most Sensitive to Disease Progression in Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The role of gray matter in multiple sclerosis is increasingly evident; however, conventional images demonstrate limitations in cortical lesion identification. Perfusion imaging appears sensitive to changes in tissue type and disease severity in MS. We sought to use bookend perfusion to quantify parameters in healthy controls and normal-appearing and lesional tissue at different relapsing-remitting MS stages. MATERIALS AND METHODS: Thirty-nine patients with relapsing-remitting MS and 19 age-matched healthy controls were prospectively recruited. The Minimal Assessment of Cognitive Function in MS battery was used to assess cognitive performance. Perfusion parameters, including cerebral blood flow and volume and mean transit time, were compared for healthy controls and normal-appearing and lesional tissue for all study groups. Dispersion of perfusion measures for white matter lesions and cortical lesions was assessed. RESULTS: Twenty of the 39 patients with relapsing-remitting MS were cognitively impaired. Significant differences were displayed between all relapsing-remitting MS subgroups and healthy controls in all comparisons except for normal-appearing gray matter CBV between healthy controls and unimpaired patients with relapsing-remitting MS and for all normal-appearing white matter perfusion parameters between healthy controls and unimpaired patients with relapsing-remitting MS. White matter lesion but not cortical lesion perfusion was significantly reduced in cognitively impaired patients with relapsing-remitting MS versus unimpaired patients with relapsing-remitting MS. Perfusion reduction with disease progression was greater in normal-appearing gray matter and normal-appearing white matter compared with cortical lesions and white matter lesions. Smaller dispersion was observed for cortical lesions compared with white matter lesions for each perfusion parameter. CONCLUSIONS: Quantitative GM and WM analysis demonstrated significant but disproportionate white matter lesion, cortical lesion, normal-appearing white matter, and normal-appearing gray matter changes present between healthy controls and patients with relapsing-remitting MS with and without cognitive impairment, necessitating absolute rather than relative lesion perfusion measurement.
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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) | 0.001 | 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".