Simvastatin restores neurovascular coupling and cognition in APP mice
Bibliographic record
Abstract
Abstract Background Alzheimer's disease (AD) is the leading cause of dementia worldwide and vascular dysfunction represents one of the first abnormalities in AD spectrum. Brain imaging techniques that use changes in hemodynamic signals to measure alterations in neurovascular coupling (NVC) have proven useful for early detection of cognitive deterioration. Pharmacological interventions targeting vascular risk factors, including simvastatin (SV), show promise in preventing dementia. To better understand the changes in brain NVC through the AD progression, we measured whisker‐evoked changes in hemodynamics signals and cognition longitudinally in a transgenic mouse model of AD (APP‐J20 mice) and wild‐type (WT) mice treated or not with SV. Method Hemodynamic signals following whisker stimulation (8Hz, 10 sec) were recorded longitudinally (3‐8 months) in anesthetized (ketamine/xylazine) and awake WT and APP mice, implanted with full cranial window, using optical imaging of intrinsic signals (OIS) (525, 590, and 625 nm), together with cognitive testing. The effects of SV treatment were evaluated after 2.5 and 5 months. Result APP mice displayed significant NVC deficits compared to WT littermates in both awake and anesthetized conditions. The anesthesia worsened the whisker‐evoked hemodynamic responses in APP mice. These changes were detected at early stage of the disease (3 months‐old), before the appearance of cognitive impairment (5.5 months‐old). SV treatment totally restored NVC and cognitive function after 5 months of treatment in APP mice. Conclusion The longitudinal analysis of NVC supports their relevance as translational measures for early diagnosis. Our data with SV indicate that preventive cardiovascular strategy for individuals at risk of developing AD may bear promise in protecting brain function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".