Increased Iron Deposition on Brain Quantitative Susceptibility Mapping Correlates with Decreased Cognitive Function in Alzheimer’s Disease
Bibliographic record
Abstract
The excessive accumulation of iron in deep gray structures is an important pathological characteristic in patients with Alzheimer's disease (AD). Quantitative susceptibility mapping (QSM) is more specific than other imaging-based iron measurement modalities and allows noninvasive assessment of tissue magnetic susceptibility, which has been shown to correlate well with brain iron levels. This study aimed to investigate the correlations between the magnetic susceptibility values of deep gray matter nuclei and the cognitive functions assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) in patients with mild and moderate AD. Thirty subjects with mild and moderate AD and 30 age- and sex-matched healthy controls were scanned with a 3.0 T magnetic resonance imaging (MRI) scanner. The magnetic susceptibilities of the regions of interest (ROIs), including caudate nucleus (Cd), putamen (Pt), globus pallidus (Gp), thalamus (Th), red nucleus (Rn), substantia nigra (Sn), and dentate nucleus (Dn), were quantified by QSM. We found that the susceptibility values of the bilateral Cd and Pt were significantly higher in AD patients than the controls ( P < 0.05). In contrast, bilateral Rn had significantly lower susceptibility values in AD than the controls. Regardless of gender and age, the increase of magnetic susceptibility in the left Cd was significantly correlated with the decrease of MMSE scores and MoCA scores ( P < 0.05). Our study indicated that magnetic susceptibility value of left Cd could be potentially used as a biomarker of disease severity in mild and moderate AD.
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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 | 0.000 |
| 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".