Spinal Cord Versus Brain Imaging Biomarkers of Multiple Sclerosis Trajectory Combining 7T and 3T MRI
Bibliographic record
Abstract
Background: In multiple sclerosis (MS), 7 Tesla (7T) MRI improves the visualization of cortical (CLs) and white matter (WM) lesions with a paramagnetic rim (PRLs), associated with smoldering inflammation. Spinal cord (SC) atrophy is a critical determinant of clinical disability in MS, but its importance relative to PRLs and CLs in predicting neurological disability remains unclear. Purpose: To identify the most relevant predictors for baseline neurological disability and 4-year disease progression independent of relapse activity (PIRA) in a heterogeneous MS cohort. Materials and Methods: One-hundred-twelve MS patients (83 relapsing-remitting, 29 secondary progressive) were prospectively recruited between 2010 and 2024. 7T T2*-susceptibility-weighted imaging was acquired to segment CLs, PRLs, and non-rim WM lesions, and 3T T1-weighted brain MRI to estimate cortical thickness, brain WM volume, and the SC C2-C3 cross-sectional area (CSA) using FreeSurfer and Spinal Cord Toolbox. Expanded Disability Status Scale (EDSS) was assessed at baseline and longitudinally, in 97/112 MS patients, after a mean follow-up of 4.0 years. Associations between imaging metrics and clinical outcomes were evaluated using regression models. Results: progressed in half of cases (70% sensitivity, 50% specificity) within 4 years. Conclusion: In MS, different imaging biomarkers are associated with either the current disability or PIRA. Spinal cord atrophy mainly explains the current EDSS, while brain WM atrophy and PRLs provide additional insights into future disability trajectory. Among all markers, CLs emerged as the main driver for PIRA.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".