Thalamic iron in multiple sclerosis: Waning support for the early-rise late-decline hypothesis
Bibliographic record
Abstract
BACKGROUND: Studies of thalamic iron levels in multiple sclerosis (MS) have yielded variable findings, potentially due to differences in study cohorts. For example, studies in relatively young cohorts (average ages below 40 years) have reported elevated susceptibility in people with MS (pwMS), whereas studies in older cohorts (above 40 years) found decreased susceptibility. OBJECTIVE: To test the "early-rise late-decline" hypothesis, which posits that age differences in study cohorts are responsible for conflicting findings regarding thalamic susceptibility in MS. METHODS: We chose to replicate one of the previous studies that showed evidence of elevated thalamic iron concentrations in younger pwMS (Rudko et al., 2014). We also replicated a study involving older pwMS (Pudlac et al., 2020) to serve as a control. We assessed thalamic susceptibility using the QSM processing and analysis methodology outlined by Rudko et al. RESULTS: Although cohort characteristics, QSM processing, and analytical methods were closely matched, we found significantly lower thalamic susceptibility in the younger pwMS compared to controls (-1.1 ± 7.8 vs. 5.4 ± 6.1 ppb; effect sizes: -0.35 to -0.91). Study outcomes were robust across a wide range of regularization parameters, with effect size differences influenced by background field removal regularization. A similar pattern was observed in the older cohort, where thalamic susceptibility was again lower in pwMS compared to controls (4.0 ± 9.5 vs. 9.6 ± 10.7 ppb; effect size: -0.55). CONCLUSIONS: Our findings contradict the "early rise" hypothesis of thalamic iron levels in pwMS. The consistency of our results across multiple analyses suggests that QSM processing artifacts are unlikely to explain previous reports of increased thalamic iron. Instead, these variations may stem from demographic or clinical differences, such as geographical factors and treatment regimens.
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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 | 0.032 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".