Correlations of myelin, axon, and inflammation metrics from multi echo T2 relaxation and multi-shell diffusion imaging in multiple sclerosis and healthy controls
Bibliographic record
Abstract
Background Magnetic resonance imaging (MRI) metrics from multi-echo T 2 relaxation measurements and diffusion imaging models are thought to reflect myelin, axon, and inflammation markers which may be useful for characterising tissue changes in multiple sclerosis (MS). Objective To evaluate how relaxation, neurite orientation dispersion and density imaging (NODDI), and diffusion basis spectrum imaging (DBSI) metrics that quantify myelin, axon and inflammation microstructural changes are correlated. Methods 122 MS participants and 16 healthy controls underwent 48-echo gradient and spin echo, diffusion, 3DT 1 , proton-density and T 2 -weighted scans at 3T. Pairwise Spearman correlations were used to compare MRI metrics in white matter and MS lesions. Results Most correlations were consistent with expected relationships. Unexpectedly, NODDI neurite density index only weakly correlated with DBSI fiber fraction (other axonal measure) in lesions. Geometric mean T 2 of the intra/extracellular water pool may primarily increase due to edema/tissue loss, instead of an increase of inflammatory cells. Lesions and white matter showed different results for some metric pairs, with some correlations becoming stronger in lesions while others were weaker compared to the correlations in white matter. This could be due to debris in lesions impacting modeling assumptions, or lesions having a larger range of parameter values allowing stronger correlations. Conclusions Diffusion and T 2 relaxation metrics supposedly quantifying similar microstructural characteristics are uniquely influenced by pathology. Lesion pathology, such as inflammation and tissue debris, complicate modeling, and may decrease the specificity of MRI metrics.
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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) | 0.001 | 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 | 0.001 | 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".