Multimodal imaging of glymphatic dysfunction and retinal vascular changes as biomarkers for Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is associated with retinal vascular changes, while the relationships among the glymphatic system, retina vasculature, and cognition in AD remain unclear. METHODS: Thirty-one AD patients and 24 healthy controls (HC) were enrolled. Glymphatic function was assessed using perivascular space (PVS) scoring in the hippocampus (Hip), basal ganglia, and white matter, along with free water content and diffusion tensor imaging along the perivascular space index. Retinal vascular parameters (RVPs) included fractal dimension (FD), vascular density (VD), and mean arterial/venous caliber, analyzed across subregions scaled by the optic papilla diameter (PD). Group differences in glymphatic system and RVPs between AD and HC groups were examined, and correlations with cognitive performance were assessed. Bagged Trees classifiers were used to distinguish AD from HC based on these features. RESULTS: AD patients exhibited significantly increased Hip_PVS compared to HC. FD and VD (total, 0.5-1.0 PD, 1.0-1.5 PD) were significantly lower in AD and negatively correlated with Hip_PVS. Mini-Mental State Examination and Montreal Cognitive Assessment scores were negatively correlated with Hip_PVS, and positively correlated with FD, VD (total), VD (0.5-1.0 PD), and VD (1.0-1.5 PD). Mediation analysis revealed that Hip_PVS mediated the relationship between RVPs and cognitive impairment. The retinal combination model, incorporating FD, VD (total), VD (0.5-1.0 PD), and VD (1.0-1.5 PD), achieved an AUC of 0.768 for distinguishing AD from HC, increasing to 0.877 when combined with Hip_PVS. CONCLUSIONS: Hip_PVS-mediated glymphatic dysfunction may link retinal vascular changes to cognitive decline in AD, enhancing diagnostic effectiveness with combined retinal and glymphatic biomarkers.
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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 | 0.001 |
| 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 | 0.001 | 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".