Reliability and Reproducibility of Neuromelanin‐Sensitive Imaging of the Substantia Nigra: A Comparison of Three Different Sequences
Bibliographic record
Abstract
BACKGROUND: Neuromelanin-sensitive MRI (NM-MRI) of the substantia nigra provides a noninvasive way to acquire an indirect measure of dopamine functioning. Despite the potential of NM-MRI as a candidate biomarker for dopaminergic pathology, studies about its reproducibility are sparse. PURPOSE: To assess the test-retest reproducibility of three commonly used NM-MRI sequences and evaluate three analysis methods. STUDY TYPE: Prospective study. POPULATION: A total of 11 healthy participants age between 20-27 years. FIELD STRENGTH/SEQUENCE: 3.0T; NM-MRI gradient recalled echo (GRE) with magnetization transfer (MT) pulse; NM-MRI turbo spin echo (TSE) with MT pulse; NM-MRI TSE without MT pulse. ASSESSMENT: Participants were scanned twice with a 3-week interval. Manual analysis, threshold analysis, and voxelwise analysis were performed for volume and contrast ratio (CR) measurements. STATISTICAL TESTS: Intraclass correlation coefficients (ICCs) were calculated for test-retest and inter- and intrarater variability. RESULTS: The GRE sequence achieved the highest contrast and lowest variability (4.9-5.7%) and showed substantial to almost perfect test-retest ICC (0.72-0.90) for CR measurements. For volume measurements, the manual analysis showed a higher variability (10.7-17.9%) and scored lower test-retest ICCs (-0.13-0.73) than the other analysis methods. The threshold analysis showed higher test-retest ICC (0.77) than the manual analysis for the volume measurements. DATA CONCLUSION: NM-MRI is a highly reproducible measure, especially when using the GRE sequence and CR measurements. Volume measurements appear to be more sensitive to inter/intrarater variability and variability in placement and orientation of the NM-MRI slab. The threshold analysis appears to be the best alternative for volume analysis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.
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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 | 0.001 |
| 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".