Locus Coeruleus Degeneration in Essential Tremor With Mild Cognitive Impairment: A Neuromelanin <scp>MRI</scp> Study
Bibliographic record
Abstract
ABSTRACT Objective Our aim was to research the neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) features of the locus coeruleus (LC) in essential tremor (ET) patients of various cognitive states and to explore the relationships between these features and cognition. Methods We recruited three groups of participants, including 30 ET patients with mild cognitive impairment (ET‐MCI), 57 ET patients with normal cognition (ET‐NC), and 105 healthy controls (HCs). All participants underwent MRI scanning and clinical evaluation. Through NM‐MRI images, we compared the contrast‐to‐noise ratio of LC (CNR LC ) between groups and evaluated the relationships between CNR LC and cognitive scales. Results Compared to HCs, ET‐MCI patients had a substantially lower CNR LC value ( p = 0.017). The CNR LC of ET‐NC patients was intermediate between that of ET‐MCI patients and HCs. Furthermore, a partial correlation analysis in ET‐MCI patients, controlling for age, gender, and education level, showed that higher CNR LC values correlate with better performance on the Montreal cognitive assessment test and the trail making test A. Conclusion LC degeneration in ET patients may partially contribute to cognitive decline, suggesting that the LC norepinephrine system deserves further research on the mechanism of cognitive decline of ET patients as well as the development of targeted drugs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".