Quantitative high-field imaging of sub-cortical gray matter in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In addition to neuronal injury, inflammatory, and demyelinating processes, evidence suggests multiple sclerosis (MS) is also associated with increased iron deposition in the basal ganglia. Magnetic resonance imaging (MRI), particularly at very high field strengths, is sensitive to iron accumulation and may enable visualization and quantification of iron associated with MS. OBJECTIVES: To investigate the sub-cortical gray matter in patients with early-stage relapsing-remitting MS using multiple, and novel, quantitative MRI measures at very high field. METHODS: In total, 22 patients with relapsing-remitting MS and 22 control subjects were imaged at 4.7 Tesla. Transverse relaxation rates (R₂ and R₂*) and susceptibility phase were quantified in four basal ganglia nuclei, the thalamus, and the red nuclei. Parameters in patients with MS were compared with those in healthy subjects and correlated with clinical scores. RESULTS: Significant abnormalities were observed in most structures, most notably in the pulvinar sub-nucleus. Significant correlations with disability were observed in the pulvinar; marginally significant correlations were also observed in the thalamus and red nucleus. No significant correlations were observed with duration since index relapse. CONCLUSIONS: Widespread abnormalities are present in the deep gray matter nuclei of patients recently diagnosed with MS; these abnormalities can be detected via multi-modal high-field MRI. Imaging metrics, particularly R₂*, relate to disease severity in the pulvinar and other gray matter regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".