Orientation Dependent MR Signal Decay Differentiates between People with MS, Their Asymptomatic Siblings and Unrelated Healthy Controls
Bibliographic record
Abstract
R2* relaxometry of the brain is a quantitative magnetic resonance technique which is influenced by iron and myelin content across different brain regions. Multiple sclerosis (MS) is a common inflammatory, demyelinating disease affecting both white and grey matter regions of the CNS. Using R2*, increased iron deposition has been described in deep gray matter of MS patients. Iron accumulation might promote oxidative stress in the brain, which can lead to cell death and neurodegeneration. However, recent histological work indicates that iron may be reduced within the normal appearing white matter (WM) in MS. In the present study we analyzed the R2* signal across the white matter in 39 patients with MS, 31 asymptomatic age matched siblings of patients and 30 age-matched controls. The measurement of R2* in white matter is affected by the signal's dependency on white matter fibre orientation with respect to the main magnetic field which can be accounted using diffusion tensor imaging. We observed a clear separation of the three study groups in R2*. The values in the MS group were significantly lower compared to the siblings and controls, while the siblings group presented with significantly higher R2* values than both unrelated healthy controls and patients. Furthermore, we found significantly decreased normal-appearing white matter R2* values in patients with more severe disease course. Angle resolved analysis of R2* improves the sensitivity for detecting subtle differences in WM R2* compared to standard histogram based analyses. Our findings suggest that the decreased R2* values in MS are due to diffuse tissue damage and decreased myelin in the normal appearing and diffusely abnormal WM. The increased R2* in unaffected siblings may identify a predisposition to increased iron and the potential for oxidative stress as a risk factor for developing MS.
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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) | 0.002 | 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".