Glymphatic activity in behavioral variant frontotemporal dementia: Link with vascular function and astrocytic activation
Bibliographic record
Abstract
INTRODUCTION: Glymphatic activity, vascular function, and astrocytic activation play pivotal roles in the pathophysiology of frontotemporal dementia (FTD). However, their interrelationships and combined impact on clinical features remain unclear. METHODS: Glymphatic activity was measured by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in patients with behavioral variant FTD (bvFTD, n = 61) and normal controls (NC, n = 61). RESULTS: Glymphatic activity in bvFTD correlated strongly with vascular dysfunction and astrocytic activation, and was associated with brain changes and clinical severity. Astrocytic activation mediates the association between vascular and glymphatic function. Astrocytic activation mediates the association between glymphatic function and clinical scales, whereas glymphatic function mediates the association between vascular function and clinical scales. The interaction effect was found between vascular function and glymphatic activity on white matter hyperintensity. DISCUSSION: Our findings reveal a complex interrelationship among glymphatic, vascular, and inflammatory mechanisms in FTD, highlighting their collective impact on disease severity. Highlight Vascular function, astrocytic activation, and glymphatic activity correlate with each other in frontotemporal dementia. Glymphatic activity mediates the association between vascular function and cognition. Astrocytic activation mediates the association between glymphatic function and cognition. Vascular and glymphatic function synergy drives white matter hyperintensity.
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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 | 0.000 |
| 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".