Normative values of neuromelanin-sensitive MRI signal in older adults obtained using a standard protocol for acquisition and analysis
Bibliographic record
Abstract
Abstract Background The integrity and function of catecholamine neurotransmitter systems can be assessed using MRI sequences often referred to as 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. To support such future applications, we report here the normative range of NM-MRI signal and volume metrics in cognitively normal older adults. Methods 3 Tesla NM-MRI images and demographic and cognitive data were available from 152 cognitively normal older adults aged 53-86 years old at baseline; a subsample of 68 participants also had follow-up NM-MRI data collected around one-year later. 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. The extent of annual change in these metrics was quantified and tested for significance using 1-sample t-tests. Results Baseline SN signal (CNR) was 10.02% (left SN) and 10.28% (right) and baseline LC signal was 24.71% (left) and 20.42% (right). The only NM-MRI metric to show a significant annual change was a decrease in left SN volume. Conclusion We report normative values for NM-MRI signal and volume in the SN and LC of cognitively normal older adults and normative values for their change over time. These values may help future efforts to use NM-MRI as a clinical biomarker for adults in this age range by facilitating identification of patients with extreme NM-MRI values.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".