Increased mean R2* in the deep gray matter of multiple sclerosis patients: Have we been measuring atrophy?
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance relaxometry studies in multiple sclerosis (MS) have suggested that iron accumulates within deep gray matter (DGM) structures early in the disease course. However, the commonly utilized mean R2* and magnetic susceptibility measures reflect regional iron concentration but not a structure's total iron content. Thus, tissue atrophy could impact mean R2* and magnetic susceptibility estimates. PURPOSE: To demonstrate that both average iron concentration and total iron content need to be reported in order to distinguish between atrophy-related and definite magnetic susceptibility changes. STUDY TYPE: Observational. POPULATION: The study was performed on 30 healthy controls (HCs) and 39 people with definite MS. FIELD STRENGTH/SEQUENCE: 3T Philips Achieva using an 8-channel SENSE head coil. R2* data were acquired using a multiecho gradient echo sequence and diffusion tensor imaging data were acquired using an echo-planar sequence. ASSESSMENT: ) and compared to the mean R2* as a measure of iron concentration. STATISTICAL TEST: Significant group differences were investigated in a linear regression model. All DGM structures were assessed individually and the significance threshold was adjusted using the Bonferroni-Holm correction for multiple comparisons. RESULTS: in patients was found to be lower in the thalamus and the caudate (P = 0.0011) compared to HCs, and similar between the two cohorts in the other DGM regions. DATA CONCLUSION: as an additional metric to account for the effects of tissue atrophy when assessing tissue content changes, such as iron deposition or loss. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:201-208.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".