The association of myelination in the internal capsule with iron deposition in the basal ganglia in macaques: a magnetic resonance imaging study
Bibliographic record
Abstract
BACKGROUND: Iron plays a vital role in myelin synthesis and maintenance. A tight association between iron concentration and myelin content has been demonstrated in local brain regions; however, whether such a relationship exists between distant brain regions that are anatomically connected is largely unknown. METHODS: measurement of iron and myelin content in the brains of 8 young (mean age: 7.7 years) and 8 old (mean age: 24.7 years) macaques by integrating two MRI-based techniques: quantitative susceptibility mapping (QSM) and myelin water fraction (MWF) imaging. We examined the relationship between iron deposition in components of the basal ganglia (BG), and the myelin content of the BG-connecting fiber tract internal capsule (IC) and four more white matter (WM) structures, including the optic tract, and the genu, body, and splenium of the corpus callosum, which are anatomically separate from the BG. RESULTS: Spearman's correlation analysis revealed a moderate to high (r=0.528-0.808, P<0.05) positive correlation between the magnetic susceptibility of the BG and the MWF of anatomically connected IC structures during myelin production and maintenance, but little significant correlation was found between the susceptibility of the BG and the MWF of WM structures not anatomically connected to the BG. CONCLUSIONS: These results advance the understanding of the relationship between iron and myelin, and suggest that future studies should consider the impact that iron concentration in the BG has on the myelination of WM structures that are anatomically connected to the BG.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".