Test-retest reproducibility of <i>in vivo</i> magnetization transfer ratio and saturation index in mice at 9.4 Tesla
Bibliographic record
Abstract
Abstract Background Magnetization transfer saturation (MTsat) imaging was developed to reduce T1 dependence and improve specificity to myelin compared to the widely used MT ratio (MTR), while maintaining a feasible scan time. Knowledge of MTsat reproducibility is necessary to apply MTsat in preclinical neuroimaging. Purpose To assess the test-retest reproducibility of MTR and MTsat in the mouse brain at 9.4 T and calculate sample sizes required to detect various effect sizes. Study Type Prospective Animal Model C57Bl/6 Mouse Model (6 females and 6 males, aged 12 – 14 weeks) Field Strength/Sequence Magnetization Transfer Imaging at 9.4 T Assessment All mice were scanned at two timepoints (5 days apart). MTR and MTsat maps were analyzed using mean region-of-interest (ROI), and whole brain voxel-wise analysis. Statistical Tests Bland-Altman plots assessed biases between test and retest measurements. Test-retest reproducibility was evaluated via between and within-subject coefficients of variation (CV). Sample sizes required were calculated (at a 95 % significance level and power of 80 %), given various minimum detectable effect sizes, using both between and within-subject approaches. Results Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based and voxel-wise CVs revealed high reproducibility for both MTR (ROI: CVs < 8 %) and MTsat (ROI: CVs < 10 %). With a sample size of 6, changes on the order of 15% can be detected in MTR and MTsat, both between and within subjects, while smaller changes (6 – 8 %) require sample sizes of 10 – 15 for MTR, and 15 – 20 for MTsat. Data Conclusion MTsat exhibits comparable reproducibility to MTR, while providing sensitivity to myelin with less T1 dependence than MTR. Our findings suggest both MTR and MTsat can detect moderate changes, common in pathologies, with feasible preclinical sample sizes.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".