Tract-based Quantitative MRI for Resolving the Clinico-Radiological Paradox in Multiple Sclerosis
Bibliographic record
Abstract
Abstract The clinico-radiological paradox in multiple sclerosis (MS) arises because conventional MRI measures, particularly total lesion volume, fail to fully capture the true burden of disability. These broad volumetric measures overlook the dual influence of where lesions occur and what their microstructural composition is. This study aimed to improve the clinical relevance of lesion analysis in MS by combining tract-based spatial filtering with quantitative microstructural MRI metrics. We hypothesized that filtering lesions through clusters of functionally meaningful white matter tracts, rather than broad anatomical compartments, would enable more accurate identification of clinically relevant damage. We studied 132 participants, including 89 patients with MS (49 relapsing–remitting, 17 primary progressive, 23 secondary progressive; 62 women and 27 men) and 43 healthy controls (28 women and 15 men). All underwent standardised 3 Tesla MRI including FLAIR, T1 mapping, magnetisation transfer ratio (MTR), and diffusion tensor imaging (DTI) with fractional anisotropy (FA) and mean diffusivity (MD). We evaluated associations between imaging measures and disability and assessed predictive performance with ridge-penalised regression across EDSS, motor scores (T25FW, 9HPT), and MSPro-defined progression risk. Tract-based models remained superior to classical region-based models, achieving higher discrimination and better model fit for binarised EDSS and MSPro (AUC = 0.86–0.96 vs 0.57– 0.86) and substantially greater variance explained for continuous motor outcomes (RZ = 0.245– 0.43 vs 0.01–0.195). By integrating lesion location and microstructural composition, tract-based quantitative MRI enhances disability prediction and provides interpretable imaging markers to support disability characterisation and individualized monitoring in MS. Abbreviated Summary Abdullah et al. show that anchoring lesions within functionally critical white-matter tracts and measuring their microstructural tissue composition reveals stronger associations with motor and progression-related clinical measures in multiple sclerosis, helping explain why conventional lesion burden alone often poorly reflects disability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".