Quantification of Neuromelanin as a Neuroimaging Biomarker for Parkinson’s Disease
Bibliographic record
Abstract
Loss of signals from substantia nigra (SN) and locus coeruleus (LC) on neuromelanin (NM)-sensitive sequences of MRI is reported as a potential biomarker in patients with Parkinson's disease (PD) and related diseases. This scoping review aims to consolidate current knowledge on MRI techniques to visualize and quantify these signals and their clinical applications in PD. Publicly available databases were searched for original studies using MRI to quantify NM in PD and other related disorders. Different studies were compared based on MRI sequence, quantification techniques and correlations with clinical scores. Furthermore, studies on genetic forms of PD and prodromal PD were also evaluated and compared. The most common MRI sequences used were T1-weighted sequences and gradient echo sequences. Different studies used different quantitative measures such as signal-to-noise ratio, contrast-to-noise ratio and contrast ratio. Morphometric evaluations such as volume and area of the SN and LC signals were also used. Most studies showed evidence of significant difference in the signals in different stages of PD compared to controls both at the SN and LC. There were significant correlations between the SN and LC signals and clinical scores. Hence, quantification of these signals may be reliable in diagnosis and disease monitoring in PD. The relative ease of signal quantification and widespread availability of MRI may make it a quantitative surrogate biomarker.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".