Normative Values of Neuromelanin‐Sensitive <scp>MRI</scp> Signal in Older Adults Obtained Using a Turbo Spin Echo Sequence
Bibliographic record
Abstract
BACKGROUND: The integrity and function of catecholamine neurotransmitter systems can be assessed using neuromelanin-sensitive MRI (NM-MRI). The relevance of this method to neurodegenerative and psychiatric disorders is becoming increasingly evident, and it has potential as a clinical biomarker. PURPOSE: To support future application of NM-MRI as a clinical biomarker by defining the normative range of NM-MRI signal and volume metrics in cognitively normal older adults. STUDY TYPE: Prospective. POPULATION: A total of 152 cognitively normal older adults aged 53-86 years old, including 41 participants who had follow-up NM-MRI data collected 9-16 months later. FIELD STRENGTH/SEQUENCE: A 3.0 T; NM-MRI turbo spin echo and T1-weighted magnetization-prepared rapid acquisition with gradient echo sequences. ASSESSMENT: NM-MRI images were processed to yield summary measures of volume and signal (contrast-to-noise ratio, CNR) for the substantia nigra (SN) and locus coeruleus (LC) using a recently developed software employing a fully automated algorithm. Change in these metrics over time was also assessed. STATISTICAL TESTS: Mean and standard deviation of NM-MRI metrics were calculated; change over time was tested for significance using 1-sample t-tests. P values < 0.05 were considered statistically significant. RESULTS: = -2.57, P = 0.014). DATA CONCLUSION: We report normative values for NM-MRI signal and volume in the SN and LC of cognitively normal older adults and explore their change over time. These values may help future efforts to use NM-MRI as a clinical biomarker by facilitating identification of patients with extreme NM-MRI values. TECHNICAL EFFICACY STAGE: 1.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".