Iron deposition in the precuneus is correlated with mild cognitive impairment in patients with cerebral microbleeds: A quantitative susceptibility mapping study
Bibliographic record
Abstract
Purpose: The purpose of this study was to define whether mild cognitive impairment (MCI) is associated with iron deposition in rich-club nodes distant from cerebral microbleeds (CMBs) in patients with cerebral small vessel disease (CSVD). Methods: A total of 64 participants underwent magnetic resonance imaging (MRI) scanning and were separated into three groups, namely, CMB(+), CMB(-), and healthy controls (HCs). We compared their characteristics and susceptibility values of rich-club nodes [e.g., superior frontal gyrus (SFG), precuneus, superior occipital gyrus (SOG), thalamus, and putamen]. We then divided the CMB(+) and CMB(-) groups into subgroups of patients with or without MCI. Then, we analyzed the relationship between iron deposition and MCI by comparing the susceptibility values of rich-club nodes. We assessed cognitive functions using the Montreal Cognitive Assessment (MoCA) and quantified iron content using quantitative susceptibility mapping (QSM). Results: In the putamen, the CMB(+) and CMB(-) groups had significantly different susceptibility values. Compared with the HCs, the CMB(+) and CMB(-) groups had significantly different susceptibility values for the SFG and SOG. In addition, we found significant differences in the putamen susceptibility values of the CMB(+)MCI(+) group and the two CMB(-) groups. The CMB(+)MCI(+) and CMB(+)MCI(-) groups had significantly different precuneus susceptibility values. The binary logistic regression analysis revealed that only higher susceptibility values of precuneus were associated with a cognitive decline in patients with CMBs, and it indicated statistical significance. Conclusion: Iron deposition in the precuneus is an independent risk factor for MCI in patients with CMBs. CMBs might influence iron content in remote rich-club nodes and be relevant to MCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".