Dorsal hyperintensity and iron deposition patterns in the substantia nigra of Parkinson’s disease, idiopathic REM sleep behavior disorder, and Parkinson-plus syndromes at 7T MRI: a prospective diagnostic study
Bibliographic record
Abstract
BACKGROUND: Dorsal nigral hyperintensity (DNH) abnormality associated with excessive iron deposition in the substantia nigra, is recognized as an imaging characteristic of Parkinson's disease (PD) and can be effectively visualized using 7T MRI. This study was aimed to develop and validate the optimal DNH assessment method as a biomarker for PD, idiopathic rapid eye movement sleep behavior disorder (iRBD), and Parkinson-plus syndromes, and to explore the nigral iron deposition patterns in these diseases. METHODS: Three-dimensional gradient-echo T2*-weighted images were acquired by 7T MRI from a total of 402 patients and 100 healthy controls (HCs) in two independent cohorts (development and validation cohorts). Seven methods, including four dichotomous methods and three DNH rating scales, were used to assess DNH and evaluate their diagnostic performance. R2* mapping and principal component analysis were performed to assess nigral iron deposition patterns. RESULTS: Bilateral DNH detection rates in the development cohort were 22.6% for early-stage PD, 3.7% for advanced PD, 93.5% for iRBD, 5.7% for MSA-parkinsonian type, 78.8% for MSA-cerebellar type, 11.8% for progressive supranuclear palsy (PSP), and 100% for HC, with similar rates in the validation cohort. A cut-off of 6 on the 6-point visibility scale demonstrated a 100% accuracy for diagnosing early-stage PD in both the development and the validation cohorts. This scale exhibited moderate differential diagnostic performance between early-stage PD and iRBD (area under the curve [AUC] = 0.940) or MSA-C (AUC = 0.892). Iron deposition was predominantly in the dorsal and posterior substantia nigra of PD and PSP, the intermediate and posterior substantia nigra of MSA-P, and the ventral substantia nigra of MSA-C. CONCLUSION: DNH may be preserved in approximately one-quarter of early-stage PD and most MSA-C cases. The 6-point visibility scale on 7T effectively distinguished PD from HC, iRBD, and MSA-C. The nigral iron deposition pattern in PD may help distinguish PD from MSA-P and MSA-C, although it overlaps with that of PSP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".