Predicting disability progression and cognitive worsening in multiple sclerosis using grey matter network measures
Bibliographic record
Abstract
Abstract Objective In multiple sclerosis (MS), magnetic resonance imaging (MRI) measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven network-based measures of regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). Methods We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale [EDSS], 9-Hole Peg Test [9HPT], and Symbol Digit Modalities Test [SDMT], from a clinical trial in 988 people with progressive MS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA) to identify co-varying patterns of GM volume change. We used survival models to determine whether baseline GM network measures predict cognitive and motor worsening. Results We identified 15 networks of regionally co-varying GM features. Compared with whole brain GM, deep GM, and lesion volumes, ICA-components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR= 1.29, 95% CI [1.09-1.52], p< 0.005). Two ICA-components were associated with 9HPT worsening (HR=1.30, 95% CI [1.06:1.60], p<0.01; and HR= 1.21, 95%CI [1.01:1.45], p<0.05). Post-hoc analyses revealed that for 9HPT and SDMT survival models including network-based measures reported a higher discrimination power (respectively, C-index= 0.69, se= 0.03; C-index= 0.71, se= 0.02) compared to models including only whole and regional MRI measures (respectively, C-index= 0.65, se= 0.03; C-index= 0.69, se= 0.02). Conclusions The disability progression was better predicted by networks of covarying GM regions, rather than by single regional or whole-brain measures. Network analysis can be applied in future clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".